systematic literature review of definitions and characteristics

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Article Contents

Introduction, results of data synthesis, conclusions, acknowledgements.

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How are medication errors defined? A systematic literature review of definitions and characteristics

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M. Lisby, L.P. Nielsen, B. Brock, J. Mainz, How are medication errors defined? A systematic literature review of definitions and characteristics, International Journal for Quality in Health Care , Volume 22, Issue 6, December 2010, Pages 507–518, https://doi.org/10.1093/intqhc/mzq059

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Multiplicity in terminology has been suggested as a possible explanation for the variation in the prevalence of medication errors. So far, few empirical studies have challenged this assertion. The objective of this review was, therefore, to describe the extent and characteristics of medication error definitions in hospitals and to consider the consequences for measuring the prevalence of medication errors.

Studies were searched for in PubMed, PsychINFO, Embase and CINAHL employing primary search terms such as ‘medication errors’ and ‘adverse drug events’. Peer-reviewed articles containing these terms as primary end-points were included. Study country, year, aim, design, data-collection methods, sample-size, interventions and main results were extracted.

Forty-five of 203 relevant studies provided a generic definition of medication errors including 26 different forms of wordings. The studies conducted in nine countries represented a variety of clinical settings and the approach was mainly descriptive. Of utmost importance is the documented prevalence of medication errors, which ranged from 2 to 75% with no associations found between definitions and prevalence.

Inconsistency in defining medication errors has been confirmed. It appears that definitions and methods of detection rather than being reproducible and reliable methods are subject to the individual researcher's preferences. Thus, application of a clear-cut definition, standardized terminology and reliable methods has the potential to greatly improve the quality and consistency of medication error reporting. Efforts to achieve a common accepted definition that defines the scope and content are therefore needed.

In the Harvard Medical Practice studies of adverse events in hospitals, medication errors were found to be the main contributor constituting around one in five of the events, which were subsequently confirmed in comparable studies and studies of adverse drug events (ADEs) [ 1–4 ]. This has led to an increased focus on epidemiology and prevention of medication error in hospital settings around the world prompting numerous studies [ 5–13 ]. However, this contribution has not provided clarity or consistent findings with respect to medication errors. Quite the contrary, there appears to be a multiplicity of terms involved in defining the clinical range of medication errors and classifying consequences e.g. error, failure, near miss, rule violation, deviation, preventable ADE and potential ADE [ 14–18 ]. Moreover, it has been suggested that this inconsistency has contributed to the substantial variation in the reported occurrences of medication errors [ 19–21 ]. Thus, compared with other epidemiological fields in health care, no single definition is currently being used to determine medication errors although attempts to develop an international definition have been made e.g. National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) [ 22 ], which is clearly reflected in the referred literature. As an important consequence, this lack of clarity hinders reliable comparison of findings across studies, clinical settings and countries.

Another obstacle to achieving consensus of a common definition is the different approaches towards interpreting and detecting medication errors such as the system-oriented approach using mandatory or voluntary-based reporting systems in contrast to the epidemiological approach using specific research methods. Choice of reliable methods, including denominators, data completeness and systematic data collection, is essential in the epidemiological approach. However, these considerations are likely to be secondary in a system approach where causation is paramount. Unfortunately, identifying error causes without consistent, reliable measures is unlikely to lead to well-targeted prevention strategies. So far, the literature has mainly emphasized the problem of inconsistent use of definitions and data collection methods, whereas few studies have explored medical error subsets, and these have often been in specific clinical settings or particular to specific patient safety organizations [ 5 , 19–21 , 23–25 ]. Most importantly, no studies have to our knowledge systematically provided an overview of the extent of existing definitions and their possible impact on the occurrence of medication errors. Hence, the objective of this study was to investigate definitions of medication errors and, furthermore, to describe characteristics as well as to assess whether or not there were any associations between definitions and observed prevalence in hospitals.

Data sources

A systematic search of studies related to medication errors was performed in the databases on 18 December 2006 in PubMed (1951), Embase (1948), CINAHL (1981) and PsycINFO (1806) using the following key terms: ‘medication errors’, ‘adverse drug events’, ‘adverse drug events and errors’ and ‘medication errors and adverse drug events’ (Fig.  1 ). To capture all possible studies of medication errors in hospitals, the search was not restricted to MeSH terms in PubMed. However, a comparable search using the MeSH term ‘medication errors’ was performed in which all studies from the key term search could be retrieved.

Summary of search and review process.

Study selection

Only studies performed in hospital settings having medication errors and/or ADEs as the main objective were included in the present review, excluding studies performed solely in primary health care and literature reviews of medication errors and ADE (Fig.  1 ). Although there is no reason to believe that the occurrence of medication errors would be significantly different from hospitals, primary health care was excluded in this review due to assumed differences in medication handling and to the limited amount of completed medication error research in primary health care when the literature search was conducted. Finally, the search was limited to peer-reviewed studies with abstracts and studies presented in English.

First, titles and abstracts were examined in accordance with inclusion and exclusion criteria. Secondly, papers that met the inclusion criteria or papers in which inclusion could not be determined directly e.g. whether a setting was representing primary care were obtained. Thirdly, all duplicates between databases, papers that did not meet the inclusion criteria or papers that could not be obtained were excluded.

Data extraction

Definitions of medication errors and ADEs were registered along with included error types and whether the paper focused on ordering, dispensing, administering and monitoring. Moreover, general information regarding journal, author, year, title, aim, setting, participants, design, methods, intervention, results and evidence level were registered in an Access database.

Determination of evidence level was based on modified Oxford Criteria (Table  1 ). Studies in which evidence level could not be determined on behalf of available information, were discussed with a clinical pharmacologist and a professor in Public Health.

Levels of evidence, Oxford Centre for Evidence-based Medicine (2001) and pharmaco-epidemiological study design

In Table 1 , study-designs, in respectively, Oxford Centre for Evidence-based Medicine (therapy/prevention/aetiology/harm) and in the Pharmaco-epidemiological literature are provided along with the matching evidence levels (right column). In the present review, the evidence levels of the included studies were classified in accordance to these study-designs, as appropriate.

Due to the obvious lack of standard methodology and outcome measures, data could not be statistically summarized. However, prevalences of medication errors were reported for studies in which denominators were accessible. In pre–post studies and controlled studies, only prevalences of medication errors at baseline or from a control group were presented, whereas no prevalences could be calculated in studies using data from reporting systems [ 26 ]. Definitions were analysed with regard to similarities in content leading to the following five categories: (i) studies using the term error; (ii) studies using the NCC MERP definition; (iii) studies using failure; (iv) studies using deviation; and, finally (v) other terms. In each category, definitions from included studies were presented along with study characteristics. Finally, possible tendencies towards associations between definitions and prevalences were examined.

The literature search revealed 203 eligible papers (Fig.  1 ) of which 45 (23%) included a generic definition of medication errors. An additional 30 studies included a stage-specific definition; 22 prescribing, 3 in dispensing, 5 in administering and, finally, 4 studies contained a definition of intravenous errors. However, in 124 studies, no definitions were provided.

Overall characteristics

The 45 included studies were published in 26 different peer-reviewed journals in the period from 1984 to 2006 with half of them in the period 2005–06. The majority of studies were conducted in North America, representing 36 studies; 2 were done in Australia, 6 in Western Europe and, finally, 1 in Asia. The studies were conducted in a variety of clinical settings with almost 50% assessing more than one type of setting e.g. medical and surgical departments. Moreover, 20 studies included only adults, 9 studies only children, 9 studies both adults and children and, finally, 8 studies included other types of participants e.g. nurses and pharmacists. In 13 studies an intervention was addressed of which 9 were technologies in the medication process (e.g. computerized order entry (CPOE) either alone or combined with clinical decision support (CDS) systems, dose dispensing systems and infusion pumps with CDS). Descriptive designs were employed in 37 studies, whereas 2 studies were conducted as randomized clinical controlled studies, 1 as a case–control study and 1 as a prospective cohort study, and, finally, 4 studies were conducted using other designs e.g. case reports. Nine out of 10 studies were classified as evidence level IV or V, and, finally, chart review and reporting systems were the most frequently used methods to detect medication errors.

Prevalence of medication errors

In 21 of 45 studies, it was not possible to determine a prevalence of medication errors due to lack of valid denominators. These were in particular studies using reporting systems, interview and questionnaires as data collection method. Overall, a prevalence of 75% was found, with the majority being below <10% (Tables  2–4 ).

Studies using errors in definition of medication errors

a Oxford Centre for Evidence-based Medicine Levels of Evidence. Abbreviations: P: prescription; T: transcription; D: dispensing; A: administration; CPOE: computerized order entry; CDS: clinical decision support; OE: opportunities for errors; MEOS: medication error outcome scale. b Pre-intervention. c Preventable ADE + potential ADE.

Studies using the NCC MERP definition of medication errors

NCC MERP definition: ‘Any preventable event that may cause or lead to inappropriate medication use or patient harm while the medication is in control of the health-care professional, patient or consumer. Such events may be related to professional practice, health-care products, procedures and systems, including prescribing; order communication; product labelling, packaging and nomenclature; compounding; dispensing; distribution; administration; education; monitoring; and use.’ a Oxford Centre for Evidence-based Medicine Levels of Evidence. Abbreviations: P: prescription; T: transcription; D: dispensing; A: administration; CPOE: computerized order entry; CDS: clinical decision support; N/A: not applicable.

Studies using ‘failure, deviation or other’ terms in definition of medication errors

a Oxford Centre for Evidence-based Medicine Levels of Evidence. Abbreviations: AUS: Australia; UK: United Kingdom; P: prescription; T: transcription; D: dispensing; A: administration; CPOE: computerized order entry; CDS: clinical decision support; OE: opportunities for errors; N/A: not applicable; MEOS: medication error outcome scale. b Prevalence from a control group or pre-intervention.

An average of nine error types (min/max: 1/38) were identified in 38 of the 45 studies. In seven studies no error types were included due to study design. The study having one error type, namely, overdose (gentamicin) accounted for the highest prevalence in the review. Unfortunately, it was not possible to retrieve prevalence in the study using the highest number of error types, as data were collected from voluntary reporting. Dosing errors were the most frequent single error type, and in studies including all stages in the medication process, prescribing errors accounted for the highest percentage.

Definitions

Of the 45 definitions, 26 differed in wording and/or content. One definition used harm or potential for harm as a criterion for medication error, whereas one explicitly included intercepted medication errors [ 27–29 ]. Finally, five definitions were limited to deviations between ordered and administered drugs and doses [ 30–34 ]. In all other definitions no restrictions were specified.

A crude categorization of the revealed definitions was performed based on similarities in wording and/or content. Tables  2–4 provide an overview of definitions and characteristics of each study. Table  2 shows 15 definitions using the word ‘error/s’ followed by information about included stages in the medication process. In seven definitions, information regarding injury or intercepted errors is stated. Table  3 reveals characteristics of 17 studies using the definition from NCC MERP [ 22 ]. Finally, Table  4 presents five definitions using failure instead of error; five focusing on deviations between ordered and administered drugs/doses and three using other definitions.

Trends towards association between definition and prevalence

In the first category (Table  2 ), it was possible to ascertain prevalence in all studies ranging from 2 to 75% with the two European studies as the main contributors. In the second category (Table  3 ), which included studies using the definition from NCC MERP, it was possible to retrieve prevalence in 1 out of 17 studies, due to the use of reporting systems. This study revealed a prevalence of 8%. In the third category (Table  4 ) consisting of five studies using the term ‘failure’, it was not possible to provide information about prevalence due to study design and data collection methods. Finally, in the fourth category (Table  4 ), a prevalence of 3–16% was observed in studies focusing on deviations between ordered and administered drugs/doses.

To our knowledge, this is the first study to systematically explore the extent and impact of generic definitions of medication errors in hospital settings. The literature review confirmed an inconsistent use of definitions. However, other aspects have to be considered in order to explain the variation in prevalence of medication errors, as interpretation of the included definitions did not suggest any tendencies.

It is of particular relevance that fewer than half of the studies explicitly defined medication errors either as an overall definition (generic) or a stage/route-specific term. Furthermore, fewer than a quarter presented a generic definition despite that being the main objective of the studies. Thus, the inconsistency in terminology only represents the tip of the iceberg. Additionally, the present review has confirmed that the overall poor understanding of the epidemiology of medication errors can, at least, partly be explained by choice of design, data collection methods and study population, including denominators [ 19–21 , 23 ]. Based on these shortcomings, we have revealed a prevalence of 2–75% in studies that included a generic definition of medication error.

The second important problem is the choice of denominator or study population. It has previously been suggested that to use opportunities for errors rather than number of patients as denominator reduces the risk of case-mix bias [ 26 ]. Here we demonstrated a variety of denominators including drug order, doses, opportunities for errors, patients, nurses, reports and triggers. In addition, the frequent use of a reporting system excluded calculation of valid prevalence in almost half of all the studies thereby increasing the lack of clarity.

Thirdly, the impact of error types should be considered. It could be assumed that increasing the number of error types being measured, would automatically result in higher occurrences of medication errors due to an increased probability of detecting more errors. However, the study with the highest prevalence of errors (75%) in the present review included only one error type, namely, dosing errors, which conflicts with this assumption [ 35 ]. On the other hand, not all error types are mutually exclusive e.g. dosing errors, which inevitably includes all errors resulting in wrong or omitted dose (under-dose, overdose, omission of dose). Thus, the number of error types has to be weighed against type of error and the sensitivity of error detection methods. Unfortunately, the present review did not provide sufficient information on the impact of error types with regard to prevalence.

Finally, choice of data collection method should be considered important. Previously, chart review has been considered as the most appropriate method to detect prescribing errors and direct observation the most sensitive method to detect dispensing and administration errors, as opposed to voluntary reporting, which was found to be the least sensitive method [ 32 , 36 ]. In recent years the availability of computer-generated signals in error detection has increased, which allow an objective detection of all incidents that have been defined as an error in the computer. Thus, it can be assumed that such systems will increase the detection of systematic documented electronic data such as dosing of gentamicin [ 35 ]. In the present review the most frequently applied error detection method was chart review, which might have contributed to an underestimation of the occurrence of medication errors when applied to detection of errors other than prescribing.

Definition and prevalence

Interestingly, definitions, which at first glance appeared to be similar (Table  2 ), turned out to have the widest range in prevalence of medication errors. A closer scrutiny revealed that 10 of 15 studies in this category were affiliated with the same institutions in Boston, USA [ 7 , 15 , 28 , 37–43 ]. In addition, these studies demonstrated the lowest occurrence of medication errors ranging from 2 to 8% regardless of whether intercepted errors were included or not, suggesting consistency in error detection methods. However, prevalence in the two studies from Europe exceeded the American studies by as much as eight times, despite use of virtually identical definitions [ 10 , 35 ]. No obvious circumstances can explain these extreme differences, apart from use of data collection methods, as the study with the highest prevalence used computer-generated signals to detect dosing errors [ 35 ].

The majority of studies used the definition by NCC MERP. Unfortunately, it was only possible to retrieve one valid prevalence of medication errors [ 44 ]. This definition was initially developed for medication error reporting and, therefore, was an obvious choice for studies using reporting systems, which was the case for almost all the studies in this category [ 22 ]. An important drawback to reporting systems is an increased risk of underestimating the occurrence of medication errors due to the reporter's awareness of errors, attitudes towards reporting errors and fear of sanctions [ 45 ]. In addition, reporting systems are by nature denominator free as they do not provide information on the whole population; on the contrary, retrospective fitted denominators, such as time period or admissions, are frequently employed to demonstrate error rates [ 26 ]. Thus, reports of incidence or prevalence in studies using reporting systems should be avoided or interpreted with caution. Unfortunately, this expelled a unique opportunity to compare prevalence in studies using identical definitions.

Surprisingly, only 1 of the 45 definitions restricted medication errors to failures that either result in harm or have the potential to lead to harm [ 27 ]. Contrary to other definitions of medication errors in the present review, this approach relates process and outcome factors within the same definition, which previously has been suggested as minimizing the risk of accepting associations between errors and processes as synonyms for causation [ 46 ]. Moreover, this definition has been tested in an Australian study, in which it proved to be the most robust among other definitions, when evaluated in comparison with different medication error scenarios [ 25 ]. However, due to the design of this study it was not possible to elucidate that a restricted definition would lead to lower occurrences of medication errors compared with more profound definitions [ 15 ].

Finally, definitions that considered a medication error as a deviation between an ordered and administered drug and dose seemed to be more homogeneous with regard to prevalence despite representing different countries and employing different study designs [ 30 , 32–34 , 47 ]. However, these studies predominantly used the same types of denominator (opportunities for errors; doses and orders) as well as the most sensitive and appropriate data collection methods, e.g. direct observation in studies of dispensing and administration errors. A possible explanation for this consistency is the clear-cut limitation to deviations, which might appear simpler and be a less subjective approach in determination of medication errors. However, this approach excludes prescribing errors, as prescriptions serve as the gold standard in these definitions.

Limitations

The aim of this review was to investigate the multiplicity of definitions used in studies having medication errors and/or ADEs as the main objective. Hence, the characteristics and prevalence reported here might not reflect the overall occurrence of medication errors. However, it could be assumed that prevalence ranging from 2 to 75% represents the vast majority of studies in medication errors. Secondly, the literature search was limited to four major databases and restricted to papers in the English language. It is, therefore, possible that studies that would have met the inclusion criteria, were not indexed by these databases or were published in other languages than English. Nevertheless, due to experience from the current literature search, in which studies from a time span of >20 years were included, we assume that studies that might have been unintentionally disregarded in the search strategy would rather have added to the current inconsistency than contributed to clarification of the terminology. Third, the groupings we selected were somewhat arbitrary and this might have affected our chances of seeing an effect.

In the present systematic literature review of 45 studies we have confirmed inconsistency in defining medication errors as well as lack of definitions. Most of the definitions were profound, including minor deviations as well as fatal errors, whereas a single definition was restricted to harmful or potentially harmful errors.

Most importantly, it appears that definitions of medication errors and methods of detection, rather than being reproducible and reliable methods, are subject to individual researcher's preferences. Thus, it is obvious that application of a clear-cut definition, standardized terminology and reliable methods will greatly improve the quality and consistency of medication error findings. Efforts to achieve a commonly accepted definition that defines the scope and content is required in future studies.

We would like to thank, Prof. D.W. Bates, Harvard Medical School and Harvard School of Public Health, Boston, MA, for commenting on the present article.

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• Systematic Review | Definition, Example, & Guide

Systematic Review | Definition, Example & Guide

Published on June 15, 2022 by Shaun Turney . Revised on November 20, 2023.

A systematic review is a type of review that uses repeatable methods to find, select, and synthesize all available evidence. It answers a clearly formulated research question and explicitly states the methods used to arrive at the answer.

They answered the question “What is the effectiveness of probiotics in reducing eczema symptoms and improving quality of life in patients with eczema?”

In this context, a probiotic is a health product that contains live microorganisms and is taken by mouth. Eczema is a common skin condition that causes red, itchy skin.

Table of contents

What is a systematic review, systematic review vs. meta-analysis, systematic review vs. literature review, systematic review vs. scoping review, when to conduct a systematic review, pros and cons of systematic reviews, step-by-step example of a systematic review, other interesting articles, frequently asked questions about systematic reviews.

A review is an overview of the research that’s already been completed on a topic.

What makes a systematic review different from other types of reviews is that the research methods are designed to reduce bias . The methods are repeatable, and the approach is formal and systematic:

• Formulate a research question
• Develop a protocol
• Search for all relevant studies
• Apply the selection criteria
• Extract the data
• Synthesize the data
• Write and publish a report

Although multiple sets of guidelines exist, the Cochrane Handbook for Systematic Reviews is among the most widely used. It provides detailed guidelines on how to complete each step of the systematic review process.

Systematic reviews are most commonly used in medical and public health research, but they can also be found in other disciplines.

Systematic reviews typically answer their research question by synthesizing all available evidence and evaluating the quality of the evidence. Synthesizing means bringing together different information to tell a single, cohesive story. The synthesis can be narrative ( qualitative ), quantitative , or both.

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Systematic reviews often quantitatively synthesize the evidence using a meta-analysis . A meta-analysis is a statistical analysis, not a type of review.

A meta-analysis is a technique to synthesize results from multiple studies. It’s a statistical analysis that combines the results of two or more studies, usually to estimate an effect size .

A literature review is a type of review that uses a less systematic and formal approach than a systematic review. Typically, an expert in a topic will qualitatively summarize and evaluate previous work, without using a formal, explicit method.

Although literature reviews are often less time-consuming and can be insightful or helpful, they have a higher risk of bias and are less transparent than systematic reviews.

Similar to a systematic review, a scoping review is a type of review that tries to minimize bias by using transparent and repeatable methods.

However, a scoping review isn’t a type of systematic review. The most important difference is the goal: rather than answering a specific question, a scoping review explores a topic. The researcher tries to identify the main concepts, theories, and evidence, as well as gaps in the current research.

Sometimes scoping reviews are an exploratory preparation step for a systematic review, and sometimes they are a standalone project.

A systematic review is a good choice of review if you want to answer a question about the effectiveness of an intervention , such as a medical treatment.

To conduct a systematic review, you’ll need the following:

• A precise question , usually about the effectiveness of an intervention. The question needs to be about a topic that’s previously been studied by multiple researchers. If there’s no previous research, there’s nothing to review.
• If you’re doing a systematic review on your own (e.g., for a research paper or thesis ), you should take appropriate measures to ensure the validity and reliability of your research.
• Access to databases and journal archives. Often, your educational institution provides you with access.
• Time. A professional systematic review is a time-consuming process: it will take the lead author about six months of full-time work. If you’re a student, you should narrow the scope of your systematic review and stick to a tight schedule.
• Bibliographic, word-processing, spreadsheet, and statistical software . For example, you could use EndNote, Microsoft Word, Excel, and SPSS.

A systematic review has many pros .

• They minimize research bias by considering all available evidence and evaluating each study for bias.
• Their methods are transparent , so they can be scrutinized by others.
• They’re thorough : they summarize all available evidence.
• They can be replicated and updated by others.

Systematic reviews also have a few cons .

• They’re time-consuming .
• They’re narrow in scope : they only answer the precise research question.

The 7 steps for conducting a systematic review are explained with an example.

Step 1: Formulate a research question

Formulating the research question is probably the most important step of a systematic review. A clear research question will:

• Allow you to more effectively communicate your research to other researchers and practitioners
• Guide your decisions as you plan and conduct your systematic review

A good research question for a systematic review has four components, which you can remember with the acronym PICO :

• Population(s) or problem(s)
• Intervention(s)
• Comparison(s)

You can rearrange these four components to write your research question:

• What is the effectiveness of I versus C for O in P ?

Sometimes, you may want to include a fifth component, the type of study design . In this case, the acronym is PICOT .

• Type of study design(s)
• The population of patients with eczema
• The intervention of probiotics
• In comparison to no treatment, placebo , or non-probiotic treatment
• The outcome of changes in participant-, parent-, and doctor-rated symptoms of eczema and quality of life
• Randomized control trials, a type of study design

Their research question was:

• What is the effectiveness of probiotics versus no treatment, a placebo, or a non-probiotic treatment for reducing eczema symptoms and improving quality of life in patients with eczema?

Step 2: Develop a protocol

A protocol is a document that contains your research plan for the systematic review. This is an important step because having a plan allows you to work more efficiently and reduces bias.

Your protocol should include the following components:

• Background information : Provide the context of the research question, including why it’s important.
• Research objective (s) : Rephrase your research question as an objective.
• Selection criteria: State how you’ll decide which studies to include or exclude from your review.
• Search strategy: Discuss your plan for finding studies.
• Analysis: Explain what information you’ll collect from the studies and how you’ll synthesize the data.

If you’re a professional seeking to publish your review, it’s a good idea to bring together an advisory committee . This is a group of about six people who have experience in the topic you’re researching. They can help you make decisions about your protocol.

It’s highly recommended to register your protocol. Registering your protocol means submitting it to a database such as PROSPERO or ClinicalTrials.gov .

Step 3: Search for all relevant studies

Searching for relevant studies is the most time-consuming step of a systematic review.

To reduce bias, it’s important to search for relevant studies very thoroughly. Your strategy will depend on your field and your research question, but sources generally fall into these four categories:

• Databases: Search multiple databases of peer-reviewed literature, such as PubMed or Scopus . Think carefully about how to phrase your search terms and include multiple synonyms of each word. Use Boolean operators if relevant.
• Handsearching: In addition to searching the primary sources using databases, you’ll also need to search manually. One strategy is to scan relevant journals or conference proceedings. Another strategy is to scan the reference lists of relevant studies.
• Gray literature: Gray literature includes documents produced by governments, universities, and other institutions that aren’t published by traditional publishers. Graduate student theses are an important type of gray literature, which you can search using the Networked Digital Library of Theses and Dissertations (NDLTD) . In medicine, clinical trial registries are another important type of gray literature.
• Experts: Contact experts in the field to ask if they have unpublished studies that should be included in your review.

At this stage of your review, you won’t read the articles yet. Simply save any potentially relevant citations using bibliographic software, such as Scribbr’s APA or MLA Generator .

• Databases: EMBASE, PsycINFO, AMED, LILACS, and ISI Web of Science
• Handsearch: Conference proceedings and reference lists of articles
• Gray literature: The Cochrane Library, the metaRegister of Controlled Trials, and the Ongoing Skin Trials Register
• Experts: Authors of unpublished registered trials, pharmaceutical companies, and manufacturers of probiotics

Step 4: Apply the selection criteria

Applying the selection criteria is a three-person job. Two of you will independently read the studies and decide which to include in your review based on the selection criteria you established in your protocol . The third person’s job is to break any ties.

To increase inter-rater reliability , ensure that everyone thoroughly understands the selection criteria before you begin.

If you’re writing a systematic review as a student for an assignment, you might not have a team. In this case, you’ll have to apply the selection criteria on your own; you can mention this as a limitation in your paper’s discussion.

You should apply the selection criteria in two phases:

• Based on the titles and abstracts : Decide whether each article potentially meets the selection criteria based on the information provided in the abstracts.
• Based on the full texts: Download the articles that weren’t excluded during the first phase. If an article isn’t available online or through your library, you may need to contact the authors to ask for a copy. Read the articles and decide which articles meet the selection criteria.

It’s very important to keep a meticulous record of why you included or excluded each article. When the selection process is complete, you can summarize what you did using a PRISMA flow diagram .

Next, Boyle and colleagues found the full texts for each of the remaining studies. Boyle and Tang read through the articles to decide if any more studies needed to be excluded based on the selection criteria.

When Boyle and Tang disagreed about whether a study should be excluded, they discussed it with Varigos until the three researchers came to an agreement.

Step 5: Extract the data

Extracting the data means collecting information from the selected studies in a systematic way. There are two types of information you need to collect from each study:

• Information about the study’s methods and results . The exact information will depend on your research question, but it might include the year, study design , sample size, context, research findings , and conclusions. If any data are missing, you’ll need to contact the study’s authors.
• Your judgment of the quality of the evidence, including risk of bias .

You should collect this information using forms. You can find sample forms in The Registry of Methods and Tools for Evidence-Informed Decision Making and the Grading of Recommendations, Assessment, Development and Evaluations Working Group .

Extracting the data is also a three-person job. Two people should do this step independently, and the third person will resolve any disagreements.

They also collected data about possible sources of bias, such as how the study participants were randomized into the control and treatment groups.

Step 6: Synthesize the data

Synthesizing the data means bringing together the information you collected into a single, cohesive story. There are two main approaches to synthesizing the data:

• Narrative ( qualitative ): Summarize the information in words. You’ll need to discuss the studies and assess their overall quality.
• Quantitative : Use statistical methods to summarize and compare data from different studies. The most common quantitative approach is a meta-analysis , which allows you to combine results from multiple studies into a summary result.

Generally, you should use both approaches together whenever possible. If you don’t have enough data, or the data from different studies aren’t comparable, then you can take just a narrative approach. However, you should justify why a quantitative approach wasn’t possible.

Boyle and colleagues also divided the studies into subgroups, such as studies about babies, children, and adults, and analyzed the effect sizes within each group.

Step 7: Write and publish a report

The purpose of writing a systematic review article is to share the answer to your research question and explain how you arrived at this answer.

Your article should include the following sections:

• Abstract : A summary of the review
• Introduction : Including the rationale and objectives
• Methods : Including the selection criteria, search method, data extraction method, and synthesis method
• Results : Including results of the search and selection process, study characteristics, risk of bias in the studies, and synthesis results
• Discussion : Including interpretation of the results and limitations of the review
• Conclusion : The answer to your research question and implications for practice, policy, or research

To verify that your report includes everything it needs, you can use the PRISMA checklist .

Once your report is written, you can publish it in a systematic review database, such as the Cochrane Database of Systematic Reviews , and/or in a peer-reviewed journal.

In their report, Boyle and colleagues concluded that probiotics cannot be recommended for reducing eczema symptoms or improving quality of life in patients with eczema. Note Generative AI tools like ChatGPT can be useful at various stages of the writing and research process and can help you to write your systematic review. However, we strongly advise against trying to pass AI-generated text off as your own work.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

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A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

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A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

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1.2.2  What is a systematic review?

A systematic review attempts to collate all empirical evidence that fits pre-specified eligibility criteria in order to answer a specific research question.  It  uses explicit, systematic methods that are selected with a view to minimizing bias, thus providing more reliable findings from which conclusions can be drawn and decisions made (Antman 1992, Oxman 1993) . The key characteristics of a systematic review are:

a clearly stated set of objectives with pre-defined eligibility criteria for studies;

an explicit, reproducible methodology;

a systematic search that attempts to identify all studies that would meet the eligibility criteria;

an assessment of the validity of the findings of the included studies, for example through the assessment of risk of bias; and

a systematic presentation, and synthesis, of the characteristics and findings of the included studies.

Many systematic reviews contain meta-analyses. Meta-analysis is the use of statistical methods to summarize the results of independent studies (Glass 1976). By combining information from all relevant studies, meta-analyses can provide more precise estimates of the effects of health care than those derived from the individual studies included within a review (see Chapter 9, Section 9.1.3 ). They also facilitate investigations of the consistency of evidence across studies, and the exploration of differences across studies.

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How are medication errors defined? A systematic literature review of definitions and characteristics.

Lisby M, Nielsen LP, Brock B, et al. How are medication errors defined? A systematic literature review of definitions and characteristics. International Journal for Quality in Health Care. 2010;22(6). doi:10.1093/intqhc/mzq059.

This systematic review found wide variation in how medication errors are defined between studies. This variation has significant implications for determining the prevalence of medication errors. Prior commentaries have noted the need for standardized, universally applicable definitions of adverse drug events.

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Involvement of parents in critical incidents in a neonatal-paediatric intensive care unit. December 16, 2009

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Association of surgical resident wellness with medical errors and patient outcomes. May 6, 2020

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An overview of methodological approaches in systematic reviews

Prabhakar veginadu.

1 Department of Rural Clinical Sciences, La Trobe Rural Health School, La Trobe University, Bendigo Victoria, Australia

Hanny Calache

2 Lincoln International Institute for Rural Health, University of Lincoln, Brayford Pool, Lincoln UK

Akshaya Pandian

3 Department of Orthodontics, Saveetha Dental College, Chennai Tamil Nadu, India

Mohd Masood

Associated data.

APPENDIX B: List of excluded studies with detailed reasons for exclusion

APPENDIX C: Quality assessment of included reviews using AMSTAR 2

The aim of this overview is to identify and collate evidence from existing published systematic review (SR) articles evaluating various methodological approaches used at each stage of an SR.

The search was conducted in five electronic databases from inception to November 2020 and updated in February 2022: MEDLINE, Embase, Web of Science Core Collection, Cochrane Database of Systematic Reviews, and APA PsycINFO. Title and abstract screening were performed in two stages by one reviewer, supported by a second reviewer. Full‐text screening, data extraction, and quality appraisal were performed by two reviewers independently. The quality of the included SRs was assessed using the AMSTAR 2 checklist.

The search retrieved 41,556 unique citations, of which 9 SRs were deemed eligible for inclusion in final synthesis. Included SRs evaluated 24 unique methodological approaches used for defining the review scope and eligibility, literature search, screening, data extraction, and quality appraisal in the SR process. Limited evidence supports the following (a) searching multiple resources (electronic databases, handsearching, and reference lists) to identify relevant literature; (b) excluding non‐English, gray, and unpublished literature, and (c) use of text‐mining approaches during title and abstract screening.

The overview identified limited SR‐level evidence on various methodological approaches currently employed during five of the seven fundamental steps in the SR process, as well as some methodological modifications currently used in expedited SRs. Overall, findings of this overview highlight the dearth of published SRs focused on SR methodologies and this warrants future work in this area.

1. INTRODUCTION

Evidence synthesis is a prerequisite for knowledge translation. 1 A well conducted systematic review (SR), often in conjunction with meta‐analyses (MA) when appropriate, is considered the “gold standard” of methods for synthesizing evidence related to a topic of interest. 2 The central strength of an SR is the transparency of the methods used to systematically search, appraise, and synthesize the available evidence. 3 Several guidelines, developed by various organizations, are available for the conduct of an SR; 4 , 5 , 6 , 7 among these, Cochrane is considered a pioneer in developing rigorous and highly structured methodology for the conduct of SRs. 8 The guidelines developed by these organizations outline seven fundamental steps required in SR process: defining the scope of the review and eligibility criteria, literature searching and retrieval, selecting eligible studies, extracting relevant data, assessing risk of bias (RoB) in included studies, synthesizing results, and assessing certainty of evidence (CoE) and presenting findings. 4 , 5 , 6 , 7

The methodological rigor involved in an SR can require a significant amount of time and resource, which may not always be available. 9 As a result, there has been a proliferation of modifications made to the traditional SR process, such as refining, shortening, bypassing, or omitting one or more steps, 10 , 11 for example, limits on the number and type of databases searched, limits on publication date, language, and types of studies included, and limiting to one reviewer for screening and selection of studies, as opposed to two or more reviewers. 10 , 11 These methodological modifications are made to accommodate the needs of and resource constraints of the reviewers and stakeholders (e.g., organizations, policymakers, health care professionals, and other knowledge users). While such modifications are considered time and resource efficient, they may introduce bias in the review process reducing their usefulness. 5

Substantial research has been conducted examining various approaches used in the standardized SR methodology and their impact on the validity of SR results. There are a number of published reviews examining the approaches or modifications corresponding to single 12 , 13 or multiple steps 14 involved in an SR. However, there is yet to be a comprehensive summary of the SR‐level evidence for all the seven fundamental steps in an SR. Such a holistic evidence synthesis will provide an empirical basis to confirm the validity of current accepted practices in the conduct of SRs. Furthermore, sometimes there is a balance that needs to be achieved between the resource availability and the need to synthesize the evidence in the best way possible, given the constraints. This evidence base will also inform the choice of modifications to be made to the SR methods, as well as the potential impact of these modifications on the SR results. An overview is considered the choice of approach for summarizing existing evidence on a broad topic, directing the reader to evidence, or highlighting the gaps in evidence, where the evidence is derived exclusively from SRs. 15 Therefore, for this review, an overview approach was used to (a) identify and collate evidence from existing published SR articles evaluating various methodological approaches employed in each of the seven fundamental steps of an SR and (b) highlight both the gaps in the current research and the potential areas for future research on the methods employed in SRs.

An a priori protocol was developed for this overview but was not registered with the International Prospective Register of Systematic Reviews (PROSPERO), as the review was primarily methodological in nature and did not meet PROSPERO eligibility criteria for registration. The protocol is available from the corresponding author upon reasonable request. This overview was conducted based on the guidelines for the conduct of overviews as outlined in The Cochrane Handbook. 15 Reporting followed the Preferred Reporting Items for Systematic reviews and Meta‐analyses (PRISMA) statement. 3

2.1. Eligibility criteria

Only published SRs, with or without associated MA, were included in this overview. We adopted the defining characteristics of SRs from The Cochrane Handbook. 5 According to The Cochrane Handbook, a review was considered systematic if it satisfied the following criteria: (a) clearly states the objectives and eligibility criteria for study inclusion; (b) provides reproducible methodology; (c) includes a systematic search to identify all eligible studies; (d) reports assessment of validity of findings of included studies (e.g., RoB assessment of the included studies); (e) systematically presents all the characteristics or findings of the included studies. 5 Reviews that did not meet all of the above criteria were not considered a SR for this study and were excluded. MA‐only articles were included if it was mentioned that the MA was based on an SR.

SRs and/or MA of primary studies evaluating methodological approaches used in defining review scope and study eligibility, literature search, study selection, data extraction, RoB assessment, data synthesis, and CoE assessment and reporting were included. The methodological approaches examined in these SRs and/or MA can also be related to the substeps or elements of these steps; for example, applying limits on date or type of publication are the elements of literature search. Included SRs examined or compared various aspects of a method or methods, and the associated factors, including but not limited to: precision or effectiveness; accuracy or reliability; impact on the SR and/or MA results; reproducibility of an SR steps or bias occurred; time and/or resource efficiency. SRs assessing the methodological quality of SRs (e.g., adherence to reporting guidelines), evaluating techniques for building search strategies or the use of specific database filters (e.g., use of Boolean operators or search filters for randomized controlled trials), examining various tools used for RoB or CoE assessment (e.g., ROBINS vs. Cochrane RoB tool), or evaluating statistical techniques used in meta‐analyses were excluded. 14

2.2. Search

The search for published SRs was performed on the following scientific databases initially from inception to third week of November 2020 and updated in the last week of February 2022: MEDLINE (via Ovid), Embase (via Ovid), Web of Science Core Collection, Cochrane Database of Systematic Reviews, and American Psychological Association (APA) PsycINFO. Search was restricted to English language publications. Following the objectives of this study, study design filters within databases were used to restrict the search to SRs and MA, where available. The reference lists of included SRs were also searched for potentially relevant publications.

The search terms included keywords, truncations, and subject headings for the key concepts in the review question: SRs and/or MA, methods, and evaluation. Some of the terms were adopted from the search strategy used in a previous review by Robson et al., which reviewed primary studies on methodological approaches used in study selection, data extraction, and quality appraisal steps of SR process. 14 Individual search strategies were developed for respective databases by combining the search terms using appropriate proximity and Boolean operators, along with the related subject headings in order to identify SRs and/or MA. 16 , 17 A senior librarian was consulted in the design of the search terms and strategy. Appendix A presents the detailed search strategies for all five databases.

2.3. Study selection and data extraction

Title and abstract screening of references were performed in three steps. First, one reviewer (PV) screened all the titles and excluded obviously irrelevant citations, for example, articles on topics not related to SRs, non‐SR publications (such as randomized controlled trials, observational studies, scoping reviews, etc.). Next, from the remaining citations, a random sample of 200 titles and abstracts were screened against the predefined eligibility criteria by two reviewers (PV and MM), independently, in duplicate. Discrepancies were discussed and resolved by consensus. This step ensured that the responses of the two reviewers were calibrated for consistency in the application of the eligibility criteria in the screening process. Finally, all the remaining titles and abstracts were reviewed by a single “calibrated” reviewer (PV) to identify potential full‐text records. Full‐text screening was performed by at least two authors independently (PV screened all the records, and duplicate assessment was conducted by MM, HC, or MG), with discrepancies resolved via discussions or by consulting a third reviewer.

Data related to review characteristics, results, key findings, and conclusions were extracted by at least two reviewers independently (PV performed data extraction for all the reviews and duplicate extraction was performed by AP, HC, or MG).

2.4. Quality assessment of included reviews

The quality assessment of the included SRs was performed using the AMSTAR 2 (A MeaSurement Tool to Assess systematic Reviews). The tool consists of a 16‐item checklist addressing critical and noncritical domains. 18 For the purpose of this study, the domain related to MA was reclassified from critical to noncritical, as SRs with and without MA were included. The other six critical domains were used according to the tool guidelines. 18 Two reviewers (PV and AP) independently responded to each of the 16 items in the checklist with either “yes,” “partial yes,” or “no.” Based on the interpretations of the critical and noncritical domains, the overall quality of the review was rated as high, moderate, low, or critically low. 18 Disagreements were resolved through discussion or by consulting a third reviewer.

2.5. Data synthesis

To provide an understandable summary of existing evidence syntheses, characteristics of the methods evaluated in the included SRs were examined and key findings were categorized and presented based on the corresponding step in the SR process. The categories of key elements within each step were discussed and agreed by the authors. Results of the included reviews were tabulated and summarized descriptively, along with a discussion on any overlap in the primary studies. 15 No quantitative analyses of the data were performed.

From 41,556 unique citations identified through literature search, 50 full‐text records were reviewed, and nine systematic reviews 14 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 were deemed eligible for inclusion. The flow of studies through the screening process is presented in Figure  1 . A list of excluded studies with reasons can be found in Appendix B .

Study selection flowchart

3.1. Characteristics of included reviews

Table  1 summarizes the characteristics of included SRs. The majority of the included reviews (six of nine) were published after 2010. 14 , 22 , 23 , 24 , 25 , 26 Four of the nine included SRs were Cochrane reviews. 20 , 21 , 22 , 23 The number of databases searched in the reviews ranged from 2 to 14, 2 reviews searched gray literature sources, 24 , 25 and 7 reviews included a supplementary search strategy to identify relevant literature. 14 , 19 , 20 , 21 , 22 , 23 , 26 Three of the included SRs (all Cochrane reviews) included an integrated MA. 20 , 21 , 23

Characteristics of included studies

SR = systematic review; MA = meta‐analysis; RCT = randomized controlled trial; CCT = controlled clinical trial; N/R = not reported.

The included SRs evaluated 24 unique methodological approaches (26 in total) used across five steps in the SR process; 8 SRs evaluated 6 approaches, 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 while 1 review evaluated 18 approaches. 14 Exclusion of gray or unpublished literature 21 , 26 and blinding of reviewers for RoB assessment 14 , 23 were evaluated in two reviews each. Included SRs evaluated methods used in five different steps in the SR process, including methods used in defining the scope of review ( n  = 3), literature search ( n  = 3), study selection ( n  = 2), data extraction ( n  = 1), and RoB assessment ( n  = 2) (Table  2 ).

Summary of findings from review evaluating systematic review methods

There was some overlap in the primary studies evaluated in the included SRs on the same topics: Schmucker et al. 26 and Hopewell et al. 21 ( n  = 4), Hopewell et al. 20 and Crumley et al. 19 ( n  = 30), and Robson et al. 14 and Morissette et al. 23 ( n  = 4). There were no conflicting results between any of the identified SRs on the same topic.

3.2. Methodological quality of included reviews

Overall, the quality of the included reviews was assessed as moderate at best (Table  2 ). The most common critical weakness in the reviews was failure to provide justification for excluding individual studies (four reviews). Detailed quality assessment is provided in Appendix C .

3.3. Evidence on systematic review methods

3.3.1. methods for defining review scope and eligibility.

Two SRs investigated the effect of excluding data obtained from gray or unpublished sources on the pooled effect estimates of MA. 21 , 26 Hopewell et al. 21 reviewed five studies that compared the impact of gray literature on the results of a cohort of MA of RCTs in health care interventions. Gray literature was defined as information published in “print or electronic sources not controlled by commercial or academic publishers.” Findings showed an overall greater treatment effect for published trials than trials reported in gray literature. In a more recent review, Schmucker et al. 26 addressed similar objectives, by investigating gray and unpublished data in medicine. In addition to gray literature, defined similar to the previous review by Hopewell et al., the authors also evaluated unpublished data—defined as “supplemental unpublished data related to published trials, data obtained from the Food and Drug Administration  or other regulatory websites or postmarketing analyses hidden from the public.” The review found that in majority of the MA, excluding gray literature had little or no effect on the pooled effect estimates. The evidence was limited to conclude if the data from gray and unpublished literature had an impact on the conclusions of MA. 26

Morrison et al. 24 examined five studies measuring the effect of excluding non‐English language RCTs on the summary treatment effects of SR‐based MA in various fields of conventional medicine. Although none of the included studies reported major difference in the treatment effect estimates between English only and non‐English inclusive MA, the review found inconsistent evidence regarding the methodological and reporting quality of English and non‐English trials. 24 As such, there might be a risk of introducing “language bias” when excluding non‐English language RCTs. The authors also noted that the numbers of non‐English trials vary across medical specialties, as does the impact of these trials on MA results. Based on these findings, Morrison et al. 24 conclude that literature searches must include non‐English studies when resources and time are available to minimize the risk of introducing “language bias.”

3.3.2. Methods for searching studies

Crumley et al. 19 analyzed recall (also referred to as “sensitivity” by some researchers; defined as “percentage of relevant studies identified by the search”) and precision (defined as “percentage of studies identified by the search that were relevant”) when searching a single resource to identify randomized controlled trials and controlled clinical trials, as opposed to searching multiple resources. The studies included in their review frequently compared a MEDLINE only search with the search involving a combination of other resources. The review found low median recall estimates (median values between 24% and 92%) and very low median precisions (median values between 0% and 49%) for most of the electronic databases when searched singularly. 19 A between‐database comparison, based on the type of search strategy used, showed better recall and precision for complex and Cochrane Highly Sensitive search strategies (CHSSS). In conclusion, the authors emphasize that literature searches for trials in SRs must include multiple sources. 19

In an SR comparing handsearching and electronic database searching, Hopewell et al. 20 found that handsearching retrieved more relevant RCTs (retrieval rate of 92%−100%) than searching in a single electronic database (retrieval rates of 67% for PsycINFO/PsycLIT, 55% for MEDLINE, and 49% for Embase). The retrieval rates varied depending on the quality of handsearching, type of electronic search strategy used (e.g., simple, complex or CHSSS), and type of trial reports searched (e.g., full reports, conference abstracts, etc.). The authors concluded that handsearching was particularly important in identifying full trials published in nonindexed journals and in languages other than English, as well as those published as abstracts and letters. 20

The effectiveness of checking reference lists to retrieve additional relevant studies for an SR was investigated by Horsley et al. 22 The review reported that checking reference lists yielded 2.5%–40% more studies depending on the quality and comprehensiveness of the electronic search used. The authors conclude that there is some evidence, although from poor quality studies, to support use of checking reference lists to supplement database searching. 22

3.3.3. Methods for selecting studies

Three approaches relevant to reviewer characteristics, including number, experience, and blinding of reviewers involved in the screening process were highlighted in an SR by Robson et al. 14 Based on the retrieved evidence, the authors recommended that two independent, experienced, and unblinded reviewers be involved in study selection. 14 A modified approach has also been suggested by the review authors, where one reviewer screens and the other reviewer verifies the list of excluded studies, when the resources are limited. It should be noted however this suggestion is likely based on the authors’ opinion, as there was no evidence related to this from the studies included in the review.

Robson et al. 14 also reported two methods describing the use of technology for screening studies: use of Google Translate for translating languages (for example, German language articles to English) to facilitate screening was considered a viable method, while using two computer monitors for screening did not increase the screening efficiency in SR. Title‐first screening was found to be more efficient than simultaneous screening of titles and abstracts, although the gain in time with the former method was lesser than the latter. Therefore, considering that the search results are routinely exported as titles and abstracts, Robson et al. 14 recommend screening titles and abstracts simultaneously. However, the authors note that these conclusions were based on very limited number (in most instances one study per method) of low‐quality studies. 14

3.3.4. Methods for data extraction

Robson et al. 14 examined three approaches for data extraction relevant to reviewer characteristics, including number, experience, and blinding of reviewers (similar to the study selection step). Although based on limited evidence from a small number of studies, the authors recommended use of two experienced and unblinded reviewers for data extraction. The experience of the reviewers was suggested to be especially important when extracting continuous outcomes (or quantitative) data. However, when the resources are limited, data extraction by one reviewer and a verification of the outcomes data by a second reviewer was recommended.

As for the methods involving use of technology, Robson et al. 14 identified limited evidence on the use of two monitors to improve the data extraction efficiency and computer‐assisted programs for graphical data extraction. However, use of Google Translate for data extraction in non‐English articles was not considered to be viable. 14 In the same review, Robson et al. 14 identified evidence supporting contacting authors for obtaining additional relevant data.

3.3.5. Methods for RoB assessment

Two SRs examined the impact of blinding of reviewers for RoB assessments. 14 , 23 Morissette et al. 23 investigated the mean differences between the blinded and unblinded RoB assessment scores and found inconsistent differences among the included studies providing no definitive conclusions. Similar conclusions were drawn in a more recent review by Robson et al., 14 which included four studies on reviewer blinding for RoB assessment that completely overlapped with Morissette et al. 23

Use of experienced reviewers and provision of additional guidance for RoB assessment were examined by Robson et al. 14 The review concluded that providing intensive training and guidance on assessing studies reporting insufficient data to the reviewers improves RoB assessments. 14 Obtaining additional data related to quality assessment by contacting study authors was also found to help the RoB assessments, although based on limited evidence. When assessing the qualitative or mixed method reviews, Robson et al. 14 recommends the use of a structured RoB tool as opposed to an unstructured tool. No SRs were identified on data synthesis and CoE assessment and reporting steps.

4. DISCUSSION

4.1. summary of findings.

Nine SRs examining 24 unique methods used across five steps in the SR process were identified in this overview. The collective evidence supports some current traditional and modified SR practices, while challenging other approaches. However, the quality of the included reviews was assessed to be moderate at best and in the majority of the included SRs, evidence related to the evaluated methods was obtained from very limited numbers of primary studies. As such, the interpretations from these SRs should be made cautiously.

The evidence gathered from the included SRs corroborate a few current SR approaches. 5 For example, it is important to search multiple resources for identifying relevant trials (RCTs and/or CCTs). The resources must include a combination of electronic database searching, handsearching, and reference lists of retrieved articles. 5 However, no SRs have been identified that evaluated the impact of the number of electronic databases searched. A recent study by Halladay et al. 27 found that articles on therapeutic intervention, retrieved by searching databases other than PubMed (including Embase), contributed only a small amount of information to the MA and also had a minimal impact on the MA results. The authors concluded that when the resources are limited and when large number of studies are expected to be retrieved for the SR or MA, PubMed‐only search can yield reliable results. 27

Findings from the included SRs also reiterate some methodological modifications currently employed to “expedite” the SR process. 10 , 11 For example, excluding non‐English language trials and gray/unpublished trials from MA have been shown to have minimal or no impact on the results of MA. 24 , 26 However, the efficiency of these SR methods, in terms of time and the resources used, have not been evaluated in the included SRs. 24 , 26 Of the SRs included, only two have focused on the aspect of efficiency 14 , 25 ; O'Mara‐Eves et al. 25 report some evidence to support the use of text‐mining approaches for title and abstract screening in order to increase the rate of screening. Moreover, only one included SR 14 considered primary studies that evaluated reliability (inter‐ or intra‐reviewer consistency) and accuracy (validity when compared against a “gold standard” method) of the SR methods. This can be attributed to the limited number of primary studies that evaluated these outcomes when evaluating the SR methods. 14 Lack of outcome measures related to reliability, accuracy, and efficiency precludes making definitive recommendations on the use of these methods/modifications. Future research studies must focus on these outcomes.

Some evaluated methods may be relevant to multiple steps; for example, exclusions based on publication status (gray/unpublished literature) and language of publication (non‐English language studies) can be outlined in the a priori eligibility criteria or can be incorporated as search limits in the search strategy. SRs included in this overview focused on the effect of study exclusions on pooled treatment effect estimates or MA conclusions. Excluding studies from the search results, after conducting a comprehensive search, based on different eligibility criteria may yield different results when compared to the results obtained when limiting the search itself. 28 Further studies are required to examine this aspect.

Although we acknowledge the lack of standardized quality assessment tools for methodological study designs, we adhered to the Cochrane criteria for identifying SRs in this overview. This was done to ensure consistency in the quality of the included evidence. As a result, we excluded three reviews that did not provide any form of discussion on the quality of the included studies. The methods investigated in these reviews concern supplementary search, 29 data extraction, 12 and screening. 13 However, methods reported in two of these three reviews, by Mathes et al. 12 and Waffenschmidt et al., 13 have also been examined in the SR by Robson et al., 14 which was included in this overview; in most instances (with the exception of one study included in Mathes et al. 12 and Waffenschmidt et al. 13 each), the studies examined in these excluded reviews overlapped with those in the SR by Robson et al. 14

One of the key gaps in the knowledge observed in this overview was the dearth of SRs on the methods used in the data synthesis component of SR. Narrative and quantitative syntheses are the two most commonly used approaches for synthesizing data in evidence synthesis. 5 There are some published studies on the proposed indications and implications of these two approaches. 30 , 31 These studies found that both data synthesis methods produced comparable results and have their own advantages, suggesting that the choice of the method must be based on the purpose of the review. 31 With increasing number of “expedited” SR approaches (so called “rapid reviews”) avoiding MA, 10 , 11 further research studies are warranted in this area to determine the impact of the type of data synthesis on the results of the SR.

4.2. Implications for future research

The findings of this overview highlight several areas of paucity in primary research and evidence synthesis on SR methods. First, no SRs were identified on methods used in two important components of the SR process, including data synthesis and CoE and reporting. As for the included SRs, a limited number of evaluation studies have been identified for several methods. This indicates that further research is required to corroborate many of the methods recommended in current SR guidelines. 4 , 5 , 6 , 7 Second, some SRs evaluated the impact of methods on the results of quantitative synthesis and MA conclusions. Future research studies must also focus on the interpretations of SR results. 28 , 32 Finally, most of the included SRs were conducted on specific topics related to the field of health care, limiting the generalizability of the findings to other areas. It is important that future research studies evaluating evidence syntheses broaden the objectives and include studies on different topics within the field of health care.

4.3. Strengths and limitations

To our knowledge, this is the first overview summarizing current evidence from SRs and MA on different methodological approaches used in several fundamental steps in SR conduct. The overview methodology followed well established guidelines and strict criteria defined for the inclusion of SRs.

There are several limitations related to the nature of the included reviews. Evidence for most of the methods investigated in the included reviews was derived from a limited number of primary studies. Also, the majority of the included SRs may be considered outdated as they were published (or last updated) more than 5 years ago 33 ; only three of the nine SRs have been published in the last 5 years. 14 , 25 , 26 Therefore, important and recent evidence related to these topics may not have been included. Substantial numbers of included SRs were conducted in the field of health, which may limit the generalizability of the findings. Some method evaluations in the included SRs focused on quantitative analyses components and MA conclusions only. As such, the applicability of these findings to SR more broadly is still unclear. 28 Considering the methodological nature of our overview, limiting the inclusion of SRs according to the Cochrane criteria might have resulted in missing some relevant evidence from those reviews without a quality assessment component. 12 , 13 , 29 Although the included SRs performed some form of quality appraisal of the included studies, most of them did not use a standardized RoB tool, which may impact the confidence in their conclusions. Due to the type of outcome measures used for the method evaluations in the primary studies and the included SRs, some of the identified methods have not been validated against a reference standard.

Some limitations in the overview process must be noted. While our literature search was exhaustive covering five bibliographic databases and supplementary search of reference lists, no gray sources or other evidence resources were searched. Also, the search was primarily conducted in health databases, which might have resulted in missing SRs published in other fields. Moreover, only English language SRs were included for feasibility. As the literature search retrieved large number of citations (i.e., 41,556), the title and abstract screening was performed by a single reviewer, calibrated for consistency in the screening process by another reviewer, owing to time and resource limitations. These might have potentially resulted in some errors when retrieving and selecting relevant SRs. The SR methods were grouped based on key elements of each recommended SR step, as agreed by the authors. This categorization pertains to the identified set of methods and should be considered subjective.

5. CONCLUSIONS

This overview identified limited SR‐level evidence on various methodological approaches currently employed during five of the seven fundamental steps in the SR process. Limited evidence was also identified on some methodological modifications currently used to expedite the SR process. Overall, findings highlight the dearth of SRs on SR methodologies, warranting further work to confirm several current recommendations on conventional and expedited SR processes.

CONFLICT OF INTEREST

The authors declare no conflicts of interest.

Supporting information

APPENDIX A: Detailed search strategies

ACKNOWLEDGMENTS

The first author is supported by a La Trobe University Full Fee Research Scholarship and a Graduate Research Scholarship.

Open Access Funding provided by La Trobe University.

Veginadu P, Calache H, Gussy M, Pandian A, Masood M. An overview of methodological approaches in systematic reviews . J Evid Based Med . 2022; 15 :39–54. 10.1111/jebm.12468 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Impulsive suicide attempts: a systematic literature review of definitions, characteristics and risk factors

Affiliations.

• 1 Australian Institute for Suicide Research and Prevention, National Centre of Excellence in Suicide Prevention, World Health Organisation Collaborating Centre for Research and Training in Suicide Prevention, Griffith University, Australia. Electronic address: [email protected].
• 2 Australian Institute for Suicide Research and Prevention, National Centre of Excellence in Suicide Prevention, World Health Organisation Collaborating Centre for Research and Training in Suicide Prevention, Griffith University, Australia; Griffith Health Institute, Australia.
• 3 Australian Institute for Suicide Research and Prevention, National Centre of Excellence in Suicide Prevention, World Health Organisation Collaborating Centre for Research and Training in Suicide Prevention, Griffith University, Australia.
• PMID: 25299440
• DOI: 10.1016/j.jad.2014.08.044

Background: Extensive research on impulsive suicide attempts, but lack of agreement on the use of this term indicates the need for a systematic literature review of the area. The aim of this review was to examine definitions and likely correlates of impulsive attempts.

Methods: A search of Medline, Psychinfo, Scopus, Proquest and Web of Knowledge databases was conducted. Additional articles were identified using the cross-referencing function of Google Scholar.

Results: 179 relevant papers were identified. Four different groups of research criteria used to assess suicide attempt impulsivity emerged: (a) time-related criteria, (b) absence of proximal planning/preparations, (c) presence of suicide plan in lifetime/previous year, and (d) other. Subsequent analysis used these criteria to compare results from different studies on 20 most researched hypotheses. Conclusions regarding the characteristics of impulsive attempts are more consistent than those on the risk factors specific to such attempts. No risk factors were identified that uniformly related to suicide attempt impulsivity across all criteria groups, but relationships emerged between separate criteria and specific characteristics of suicide attempters.

Limitations: Only published articles were included. Large inconsistencies in methods of the studies included in this review prevented comparison of effect sizes.

Conclusions: The vast disparities in findings on risk factors for impulsive suicide attempts among different criteria groups suggest the need to address the methodological issues in defining suicide attempt impulsivity before further research into correlates of such attempts can effectively progress. Specific recommendations are offered for necessary research.

Keywords: Impulsive suicide; Impulsivity; Planned suicide; Planning; Suicide attempts.

Copyright © 2014 Elsevier B.V. All rights reserved.

Publication types

• Research Support, Non-U.S. Gov't
• Systematic Review
• Impulsive Behavior*
• Risk Factors
• Suicide, Attempted / psychology*
• Suicide, Attempted / statistics & numerical data*

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Toward a framework for selecting indicators of measuring sustainability and circular economy in the agri-food sector: a systematic literature review

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• Cecilia Silvestri   ORCID: orcid.org/0000-0003-2528-601X 1 ,
• Luca Silvestri   ORCID: orcid.org/0000-0002-6754-899X 2 ,
• Michela Piccarozzi   ORCID: orcid.org/0000-0001-9717-9462 1 &
• Alessandro Ruggieri 1  

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A Correction to this article was published on 24 March 2022

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The implementation of sustainability and circular economy (CE) models in agri-food production can promote resource efficiency, reduce environmental burdens, and ensure improved and socially responsible systems. In this context, indicators for the measurement of sustainability play a crucial role. Indicators can measure CE strategies aimed to preserve functions, products, components, materials, or embodied energy. Although there is broad literature describing sustainability and CE indicators, no study offers such a comprehensive framework of indicators for measuring sustainability and CE in the agri-food sector.

Starting from this central research gap, a systematic literature review has been developed to measure the sustainability in the agri-food sector and, based on these findings, to understand how indicators are used and for which specific purposes.

The analysis of the results allowed us to classify the sample of articles in three main clusters (“Assessment-LCA,” “Best practice,” and “Decision-making”) and has shown increasing attention to the three pillars of sustainability (triple bottom line). In this context, an integrated approach of indicators (environmental, social, and economic) offers the best solution to ensure an easier transition to sustainability.

Conclusions

The sample analysis facilitated the identification of new categories of impact that deserve attention, such as the cooperation among stakeholders in the supply chain and eco-innovation.

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Source: Authors’ elaboration. Notes: The graph shows the temporal distribution of the articles under analysis

Source: Authors’ elaborations. Notes: The graph shows the time distribution of articles from the three major journals

Source: Authors’ elaboration. Notes: The graph shows the composition of the sample according to the three clusters identified by the analysis

Source: Authors’ elaboration. Notes: The graph shows the distribution of articles over time by cluster

Source: Authors’ elaboration. Notes: The graph shows the network visualization

Source: Authors’ elaboration. Notes: The graph shows the overlay visualization

Source: Authors’ elaboration. Notes: The graph shows the classification of articles by scientific field

Source: Authors’ elaboration. Notes: Article classification based on their cluster to which they belong and scientific field

Source: Authors’ elaboration

Source: Authors’ elaboration. Notes: The graph shows the distribution of items over time based on TBL

Source: Authors’ elaboration. Notes: The graph shows the Pareto diagram highlighting the most used indicators in literature for measuring sustainability in the agri-food sector

Source: Authors’ elaboration. Notes: The graph shows the distribution over time of articles divided into conceptual and empirical

Source: Authors’ elaboration. Notes: The graph shows the classification of articles, divided into conceptual and empirical, in-depth analysis

Source: Authors’ elaboration. Notes: The graph shows the geographical distribution of the authors

Source: Authors’ elaboration. Notes: The graph shows the distribution of authors according to the continent from which they originate

Source: Authors’ elaboration. Notes: The graph shows the time distribution of publication of authors according to the continent from which they originate

Source: Authors’ elaboration. Notes: Sustainability measurement indicators and impact categories of LCA, S-LCA, and LCC tools should be integrated in order to provide stakeholders with best practices as guidelines and tools to support both decision-making and measurement, according to the circular economy approach

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A Correction to this paper has been published: https://doi.org/10.1007/s11367-022-02038-9

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Silvestri, C., Silvestri, L., Piccarozzi, M. et al. Toward a framework for selecting indicators of measuring sustainability and circular economy in the agri-food sector: a systematic literature review. Int J Life Cycle Assess (2022). https://doi.org/10.1007/s11367-022-02032-1

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• Volume 31, Issue 1
• Definitions of digital biomarkers: a systematic mapping of the biomedical literature
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• Ana Karen Macias Alonso 1 , 2 ,
• http://orcid.org/0000-0001-6589-3936 Julian Hirt 2 , 3 , 4 ,
• Tim Woelfle 2 , 5 ,
• Perrine Janiaud 2 , 3 and
• Lars G Hemkens 2 , 3 , 6 , 7
• 1 Department of Applied Natural Sciences , Technische Hochschule Lübeck , Lübeck , Germany
• 2 Pragmatic Evidence Lab, Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) , University Hospital Basel and University of Basel , Basel , Switzerland
• 3 Department of Clinical Research , University Hospital Basel and University of Basel , Basel , Switzerland
• 4 Department of Health , Eastern Switzerland University of Applied Sciences , St.Gallen , Switzerland
• 5 Department of Neurology and MS Center , University Hospital Basel and University of Basel , Basel , Switzerland
• 6 Meta-Research Innovation Center at Stanford (METRICS) , Stanford University , Stanford , California , USA
• 7 Meta-Research Innovation Center Berlin (METRIC-B) , Berlin Institute of Health , Berlin , Germany
• Correspondence to Dr Lars G Hemkens; lars.hemkens{at}usb.ch

Background Technological devices such as smartphones, wearables and virtual assistants enable health data collection, serving as digital alternatives to conventional biomarkers. We aimed to provide a systematic overview of emerging literature on ‘digital biomarkers,’ covering definitions, features and citations in biomedical research.

Methods We analysed all articles in PubMed that used ‘digital biomarker(s)’ in title or abstract, considering any study involving humans and any review, editorial, perspective or opinion-based articles up to 8 March 2023. We systematically extracted characteristics of publications and research studies, and any definitions and features of ‘digital biomarkers’ mentioned. We described the most influential literature on digital biomarkers and their definitions using thematic categorisations of definitions considering the Food and Drug Administration Biomarkers, EndpointS and other Tools framework (ie, data type, data collection method, purpose of biomarker), analysing structural similarity of definitions by performing text and citation analyses.

Results We identified 415 articles using ‘digital biomarker’ between 2014 and 2023 (median 2021). The majority (283 articles; 68%) were primary research. Notably, 287 articles (69%) did not provide a definition of digital biomarkers. Among the 128 articles with definitions, there were 127 different ones. Of these, 78 considered data collection, 56 data type, 50 purpose and 23 included all three components. Those 128 articles with a definition had a median of 6 citations, with the top 10 each presenting distinct definitions.

Conclusions The definitions of digital biomarkers vary significantly, indicating a lack of consensus in this emerging field. Our overview highlights key defining characteristics, which could guide the development of a more harmonised accepted definition.

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Introduction

Biomarkers are defined as a set of characteristics that are objectively measured and used as indicators of normal biological processes, pathogenic processes or biological responses that appear due to exposure or therapeutic interventions. 1 This comprises physiological, molecular, histologic and radiographic measurements. 2 The US Food and Drug Administration (FDA) subclassifies susceptible/risk, diagnostic, monitoring, prognostic, predictive, response and safety biomarkers. 1 They highlight that a full biomarker description must include the source or matrix, the measurable characteristic(s) and the methods used to measure the biomarker. 1 The digitalisation of our world impacting daily living and healthcare broadens the spectrum of the possible source and methods used to measure biomarkers and introduces a novel dimension of measurable characteristics. This allows digital devices used daily, such as smartphones, wearable devices, sensors and smart home devices, to provide a new category of biomarkers, often called ‘digital biomarkers’. In recent years, digital biomarkers became increasingly present in routine care and in research in many areas of medicine, such as cardiology, oncology or COVID-19. For example, smartphone recorded cough sounds have been used as a digital biomarker to detect asthma and respiratory infections in clinical trials, 3 4 or deep learning was applied to data from a three-axis accelerometer to predict sleep/wake patterns. 4 5 Moreover, such digital biomarkers have spread in the field of neurology, which has a large unmet need for non-invasive and objective biomarkers reflecting cognitive and motor functions that are traditionally assessed with specific tests performed by neurologists. 6 Beyond monitoring health and disease status, predicting the occurrence and development of diseases would be promising applications of such novel approaches. 7

Thus, digital biomarkers have the potential to offer valuable insights on the health of patients. They usually have high temporal resolution (up to (quasi-)continuous), are usually objective (and not subject to interobserver variability) and can have high external validity as they may be applied in the patient’s routine environment (as opposed to, eg, the clinic or a research environment). 8

Many everyday digital tools used mainly for entertainment/leisure purposes (eg, fitness trackers) are increasingly considered as a source of helpful information that may be transformed into digital biomarkers. Yet, with all this diversity in application and complex interaction with rapidly evolving technology, it becomes necessary to provide a clear and precise definition of the fundamental underlying concepts to facilitate research and decision-making with and on these novel approaches.

One of the first definitions of this novel type of biomarker was provided by Dorsey et al , who defined digital biomarkers as ‘the use of a biosensor to collect objective data on a biological (eg, blood glucose, serum sodium), anatomical (eg, mole size) or physiological (eg, heart rate, blood pressure) parameter obtained using sensors followed by algorithms to transform these data into interpretable outcome measures, helping to address many of the shortcomings in current measures.’ Furthermore, they stated that these new measures ‘include portable (eg, smartphones), wearable, and implantable devices, and are by their nature largely independent of raters.’ 9 A later definition given in 2020 by the European Medicines Agency (EMA) was based on ‘digital measures’ (‘measured through digital tools’) and did not include the requirement of algorithms as a defining feature: ‘a digital biomarker is an objective, quantifiable measure of physiology and/or behaviour used as an indicator of biological, pathological process or response to an exposure or an intervention that is derived from a digital measure. […]’) 10

Others gave broader definitions including further defining features, for example, defining digital biomarkers as ‘objective, quantifiable, quantitative, physiological and behavioural data that are collected and measured by means of digital devices such as portables, wearables, implantables or digestibles. The data collected are used to explain, influence and/or predict health-related outcomes’. 2 6 11

Overall, such a disagreement between definitions used by regulators and in articles published in high-impact biomedical journals raised concerns that no clear consensus exists among researchers and users of this novel approach and terminology, increasing the risk for miscommunication. There are numerous examples where differences in definitions have been recognised as critical cause of inefficiencies and delay in health research and avoidable controversy, uncertainty and potential harm in clinical care and public health. 12–15 The Biomarkers, EndpointS and other Tools (BEST) framework developed by the FDA and US National Institutes of Health with ‘the goals of improving communication, aligning expectations, and improving scientific understanding’ highlights that ‘unclear definitions and inconsistent use of key terms can hinder the evaluation and interpretation of scientific evidence and may pose significant obstacles to medical product development programmes’. 1 We aimed to provide a systematic overview of the emerging literature on digital biomarkers and characterisation of the definitions of digital biomarkers that are provided in biomedical journal articles by performing a systematic mapping and citation analysis of all articles that prominently used the term ‘digital biomarker’. We sought to determine differences in characteristics of common definitions to provide a foundation for subsequent activities to develop clearer and consistent definitions that ensure improved application of digital biomarkers in research and healthcare decision-making.

We analysed all articles published at any time in PubMed that prominently used the term ‘digital biomarker’, that is, either in title or abstract.

We systematically explored definitions of digital biomarkers that are provided and/or referred to in the biomedical literature, that is, journal articles that are indexed in PubMed, in a mapping review without a formal assessment of included studies. 16 We structured our review report to the ‘Preferred Reporting Items for Systematic Reviews and Meta-Analyses’ guidance, where applicable. 17 We did not use a prespecified protocol.

Eligibility criteria, information source and search strategy

We searched PubMed and included all articles mentioning ‘digital biomarker’ or ‘digital biomarkers’ in their title or abstract (by searching PubMed for ‘digital biomarker*(tiab)’; date of last search: 8 March 2023). We excluded animal research.

Study selection

One reviewer (AKMA) screened titles, abstracts and full texts for eligibility. Confirmation by a second reviewer (JH or LGH) was planned for situations where the reviewer was unsure, but this case never occurred given the clear and objective selection criteria.

Data extraction

We developed a spreadsheet to structure the data extraction process. One reviewer (AKMA) extracted data with confirmation by a second reviewer (JH or LGH) in case of any uncertainty.

We extracted from every article: author(s), publication year, title, journal, corresponding author, and country of correspondence, article type (ie, primary research, review or other type (eg, editorial, comment, opinion-based letter)). Of primary research articles, we additionally extracted definitions of digital biomarkers that are provided and/or referred to (based on a semantic search for indicators of definition such as ‘digital biomarkers are’, ‘… are defined as’, ‘… can be defined’, ‘the definition of … is’), medical context, and whether the article is about the development and/or validation of a digital biomarker. The number of global citations was obtained by using metadata from OpenAlex 18 ; accessed via the Local Citation Network 19 (as of 26 June 2023).

Data analysis and categorisation of definition components

We considered the BEST framework to derive components of definitions for digital biomarkers. 1 We analysed the identified digital biomarker definitions by assessing if they contained descriptions that fall within three key components, that is, the (1) type of data that is measured (eg, whether data were measured objectively, continuously or quantitatively), (2) data collection method (eg, whether sensors, computers, portables, wearables, implantables or digestibles were used to collect data) and (3) purpose of the digital biomarker (eg, whether a biomarker was used as measure of disease progression or to predict health-related outcomes). We defined definitions as duplicates when they used the same sequence of words. We illustrate the frequency of various terminologies used in all provided definitions with a word cloud. 20 We analysed the structural similarity of definitions that were provided without a reference by performing hierarchical clustering on the distance-matrix containing pairwise ‘Indel’-distances, that is, ‘the minimum number of insertions and deletions required to change one (definition) into the other’. 21 Since we aimed at exploring how digital biomarkers are defined in the biomedical literature, we did not critically assess the included articles and studies. For the analysis of citations, we calculated the quotient of number of global citations (retrieved by the Local Citation Network 19 ) and years since publication per article. To create a citation network of citing and cited relationships between the articles, we used the Local Citation Network with the OpenAlex scholarly index. 19 22

We used descriptive statistics by reporting numbers and percentages. For all analyses, we used R (V.4.2.2) or Python (V.3.11.4).

We identified 415 articles that had ‘digital biomarker’ in their title or abstract ( online supplemental S1 ). The first article was published in 2014 (median publication year 2021; figure 1 ; online supplemental S2 ). Most articles described primary studies (n=283; 68%) and were published in digital medicine specialty journals, including Digital Biomarkers (n=35; 8%), Journal of Medical Internet Researc h (n=21; 5%) or npj Digital Medicine (n=19; 4%; table 1 ). Of the 415 articles, 128 (31%) provided at least 1 definition of a digital biomarker.

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The annual number of published article types referring to digital biomarkers as of 8 March 2023 (n=415).

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Characteristics of all 415 articles in PubMed using ‘digital biomarker’ in title or abstract

Characteristics of articles providing a definition of digital biomarker

The 128 articles with a definition of digital biomarker were published between 2015 and 2023 (median: 2021). Of them, 59 articles were primary studies, 50 were reviews and 19 were other types of articles ( table 1 ).

Almost all primary studies described the development of one or more digital biomarkers (53 of 59 articles), and many described a validation process of biomarkers (35 of 59 articles). The most frequent medical field of the primary research articles that described the development of one or more digital biomarkers was neurology (25 of 53), while the spectrum of medical fields was overall very wide ( table 1 ). The most frequent diseases were dementia and related disorders (16 of 53 articles, ie, (mild) cognitive impairment or Alzheimer’s disease), Parkinson’s disease (5 of 53 articles) and diabetes (3 of 53 articles), with numerous other conditions addressed in one or two studies (eg, atrial fibrillation, cervical cancer, depression, heart failure and muscular dystrophy; online supplemental S2 ).

The corresponding authors were mostly from the USA (69 of 128 articles), Switzerland (22 of 128 articles), Germany (16 of 128 articles) and the UK (16 of 128 articles; table 1 ).

The articles were cited a median of 6 times (range 0–517, IQR 2–20, overall 2,705); on average two times per year (range 0–86, IQR 1–5; online supplemental S2 ). We show the citation network (ie, citing and cited relationships within the sample of these 128 articles) online ( https://LocalCitationNetwork.github.io/?fromJSON=Digital-Biomarker -Definitions.json).

Definitions of digital biomarkers

Overall, 128 articles reported between 1 and 7 definitions (median 1, IQR 1–2). In 91 articles, at least 1 reference was provided for these definitions made by the authors (median 1, range 1–13, IQR 1–2, overall 274 references); for 37 articles with 51 definitions, no reference was provided ( online supplemental S2 ).

The mostly used references to support the definitions were Coravos et al 4 (referenced by 51 of 91 articles); Dorsey et al 9 (11 articles); Califf 23 (9 articles); Piau et al 24 (9 articles); Babrak et al 6 (8 articles) and Coravos et al 25 (8 articles). All these articles were among the 415 articles analysed here. The original definitions in these top-cited articles can be found in table 2 . Other references were used by less than five articles.

The top cited definitions of Digital Biomarkers within the 415 articles

In total, the 128 articles reported 202 definitions; 75 of which were duplicates. Hence, we identified 127 unique definitions across the 128 articles.

The 10 most frequently used terms that most of the 127 unique definitions contained were ‘digital’ (125 of 127 definitions; 98%), ‘biomarkers’ (109 of 127 definitions; 85%), ‘data’ (62 of 127 definitions; 48%), ‘collected’ (55 of 127 definitions; 43%), ‘devices’ (50 of 127 definitions; 39%), ‘health’ (42 of 127 definitions; 33%), ‘physiological’ (37 of 127 definitions; 29%), ‘objective’ (37 of 127 definitions; 29%), ‘wearable’ (34 of 127 definitions; 26%) and ‘behavioural’ (33 from 127 definitions; 25%; figure 2 ).

Word cloud with the most frequently used terms in the analysed digital biomarker(s) definitions.

Of the 127 unique definitions, 56 definitions refer to the type of data that are collected, 78 definitions contain information on the data collection method, and 50 definitions provide information on the purpose of the digital biomarker. Only 23 of 127 definitions involve all 3 components and 26 contain none of these components ( table 3 ; online supplemental S3 ; online supplemental S2 ).

Definitions of digital biomarkers that include three key components: type of data, data collection method and purpose of a digital biomarker (n=23)

There were almost no structural similarities between the 51 identified definitions in 37 articles without a reference (for those with a reference, similarities such as paraphrasing are expected; online supplemental S4 ).

We systematically searched and characterised the biomedical literature that used the term digital biomarker and analysed the provided definitions of the concept. We identified 415 articles using ‘digital biomarker’ in title and/or abstract that were published between 2014 and 2023. Of them, 128 articles provided 127 different definitions. By comparing the defining features, we aimed to better understand what those who use this term in the context of biomedical research or healthcare mean by ‘digital biomarker’ and which components are deemed the essence of it. 26

The first definition of a digital biomarker is from 2015. 27 Within 8 years, more than 127 definitions have been used, with none of them clearly being the most widely used; indicating a high heterogeneity of the concept of digital biomarkers. The definitions often cover different aspects of definitional components that are traditionally used to describe more conventional biomarkers. Authors have created their own concepts and gave an identity to this type of biomarker. The variation in these definitions and the fact that only 23 of them provide a full description containing all components of FDA’s BEST framework, shows how broad the current understanding of this fundamental concept is.

Digital biomarkers emerged as a concept in medical and technological domains, although with a diverse terminology across different academic journals. In the medical field, digital biomarkers are often referred to as biomarkers of health or disease obtained through digital health technologies. In the technical field, these biomarkers are viewed as data-driven indicators collected from sensors, wearables and other portable digital technologies that provide an assessment of the health status. These diverse terminologies and definitions reflect the interdisciplinary nature of digital biomarkers with their application in a broad spectrum of biomedicine which underlines the importance of unified concepts to enhance the communications and cross-disciplinary collaborations on this evolving field.

Regulatory perspectives

The EMA has defined digital biomarkers in 2020 in their draft guidance ‘Questions and answers: Qualification of digital technology-based methodologies to support approval of medicinal products’, stating their ‘clinical meaning is established by a reliable relationship to an existing, validated endpoint’. 10 EMA draws a clear line to electronic clinical outcome assessments (eCOA), whose ‘clinical meaning is established de novo’. According to EMA’s terminology, both digital biomarkers and eCOA are derived from ‘digital measures’ and can be used as ‘digital endpoints’. 10

On the other hand, the term ‘digital biomarker’ cannot be found in the FDA draft guidance ‘Digital Health Technologies for Remote Data Acquisition in Clinical Investigations’, which instead features eCOA as examples of digital health technologies. 28 Figure 3 contains our semantic interpretation of the terminology used by EMA and FDA.

Semantic overview of terminology used by EMA and FDA. Digital health technologies obtain digital measures, which include digital biomarkers and electronic clinical outcome assessment (eCOA). Digital biomarkers and eCOAs both can provide digital endpoints. EMA, European Medicines Agency; FDA, Food and Drug Administration.

This distinction can rarely be observed in the medical literature—we found this term in 8 of the 415 articles analysed and a PubMed search for ‘electronic clinical outcome assessment*’ returned also only 8 articles mentioning it in title or abstract (as of 31 August 2023), compared with the 415 for our search term ‘digital biomarker*’. As Vasudevan et al stated in 2022: ‘There are currently multiple definitions of the term digital biomarker reported in the scientific literature, and some seem to conflate established definitions of a biomarker and a clinical outcomes assessment (COA)’. 11

This divergency in the terminology of digital biomarkers between the academic literature and the regulators’ language raises challenges and ambiguity. Consequently, a more cohesive and comprehensive framework within the digital biomarker field is needed to strengthen the clarity and continue growing the potential that this data could bring for health.

The development of a substantive and unified definition of digital biomarkers would be an important step in shaping a conceptual framework for the development, assessment and reporting of digital biomarkers. Our results may inform this process by using the existing understanding of digital biomarkers systematically analysed in this study as a basis. To achieve a common and more unified understanding of what digital biomarkers are—and are not—a Delphi study could be useful. 29 30 Such a study would aim to combine multiple views and expectations on the existing definitions of digital biomarkers and their components until a consensus is reached. Ideally, that would be achieved by an international panel with expert’s representative of all relevant stakeholders covering a range of medical fields (eg, cardiology, neurology), professional backgrounds (eg, clinical care/rehabilitation/nursing, software developers, device manufacturer, editors, guideline developers), and professional perspectives (eg, academia, regulatory, industry/technology, publishing) and involving patients.

Limitations

There are some limitations to our study.

First, we used a limited search only in a single database using the single term of ‘digital biomarker*’, which may have overlooked some other relevant studies. PubMed was chosen as literature database given its outstanding role, reflecting the most impactful journals in biomedicine. 31 We focused on this single term because we assume it to be the most central and widely used term describing the concept of ‘digital biomarker’. It is very unlikely that the definitions would be much more uniform in potentially overlooked studies or would we have included other potential concepts, and it is quite possible that many more different definitions would emerge, especially from digital biomarker developments contained in technical literature databases (such as IEEE Explore or ACM Digital Library). Therefore, we may have even underestimated the large number of different definitions.

Second, the screening and data extraction were performed by a single reviewer only. This may have resulted in some studies that were overlooked and some misclassifications, but it is unlikely that our main interpretation would change. Third, we developed a simple framework with three key elements of definitions based on a well-established framework (BEST), but the categorisation of elements is subjective to some degree. However, we employed a structured analysis that confirmed the observed heterogeneity across definitions.

Conclusions

Clear and unambiguous communication and research reporting is essential for the effective implementation of scientific innovations and developments. This requires clear definitions and consistent use and understanding of key terms and concepts. A lack of clarity and consistency can lead to research waste, delay or even misdirection of promising developments and potential. Digital biomarkers offer the opportunity to collect objective, meaningful, patient-relevant data cost-effectively with unprecedented granularity. An exact understanding of what they are and how they are described in biomedical literature is essential to let them shape the future of clinical research and enable them to provide most useful evidence for research and care. Our study can inform the development of a harmonised and more widely accepted definition, for example, with a Delphi study.

Ethics statements

Patient consent for publication.

Not applicable.

Acknowledgments

We thank Saido Haji Abukar (Medical Bachelor student at ETH Zurich) for her support with the study selection and data extraction.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2

Contributors All authors made substantial contributions to the conception and design of the work; all authors have drafted the work or substantively revised it; all authors have approved the submitted version; all authors have agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved and the resolution documented in the literature.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests RC2NB (Research Center for Clinical Neuroimmunology and Neuroscience Basel) is supported by Foundation Clinical Neuroimmunology and Neuroscience Basel. One of the main projects of RC2NB is the development and evaluation of a digital biomarkers which is supported by grants from Novartis, Roche and Innosuisse (Swiss Innovation Agency). All authors declare no competing interests.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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• Published: 13 April 2024

Acute spinal cord injury serum biomarkers in human and rat: a scoping systematic review

• Sina Shool   ORCID: orcid.org/0000-0002-0280-3187 1 , 2   na1 ,
• Saeed Rahmani 1   na1 ,
• Mohammad Amin Habibi 1 , 2   na1 ,
• Seyed Mohammad Piri 2 ,
• Mahmoud Lotfinia 3 ,
• Delara Jashnani 1 &
• Sina Asaadi   ORCID: orcid.org/0000-0003-2953-5992 4  

Spinal Cord Series and Cases volume  10 , Article number:  21 ( 2024 ) Cite this article

Metrics details

• Diagnostic markers
• Prognostic markers

Study design

Scoping systematic review.

To summarize the available experimental clinical and animal studies for the identification of all CSF and serum-derived biochemical markers in human and rat SCI models.

Tehran, Iran.

In this scoping article, we systematically reviewed the electronic databases of PubMed, Scopus, WOS, and CENTRAL to retrieve current literature assessing the levels of different biomarkers in human and rat SCI models.

A total of 19,589 articles were retrieved and 6897 duplicated titles were removed. The remaining 12,692 studies were screened by their title/abstract and 12,636 were removed. The remaining 56 were considered for full-text assessment, and 11 papers did not meet the criteria, and finally, 45 studies were included. 26 studies were human observational studies comprising 1630 patients, and 19 articles studied SCI models in rats, including 832 rats. Upon reviewing the literature, we encountered a remarkable heterogeneity in terms of selected biomarkers, timing, and method of measurement, studied models, extent, and mechanism of injury as well as outcome assessment measures.

Conclusions

The specific expression and distribution patterns of biomarkers in relation to spinal cord injury (SCI) phases, and their varied concentrations over time, suggest that cerebrospinal fluid (CSF) and blood biomarkers are effective measures for assessing the severity of SCI.

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Acknowledgements

The authors thank Shahid Beheshti University of Medical Science for their help and support.

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These authors contributed equally: Sina Shool, Saeed Rahmani, Mohammad Amin Habibi.

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Department of Neurosurgery, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Sina Shool, Saeed Rahmani, Mohammad Amin Habibi & Delara Jashnani

Sina Trauma and Surgery Research Center, Sina Hospital, Tehran University of Medical Sciences, Hassan-Abad Square, Imam Khomeini Ave, 11365-3876, Tehran, Iran

Sina Shool, Mohammad Amin Habibi & Seyed Mohammad Piri

Resident of Neurosurgery, Department of Neurosurgery, Klinikum Saarbrücken, University of Saarland, Saarbrücken, Germany

Mahmoud Lotfinia

Department of Surgery, Division of Acute Care Surgery, Loma Linda University, Loma Linda, CA, USA

Sina Asaadi

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SS was responsible for: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Roles/Writing - original draft; Writing - review & editing. SR was responsible for: Data curation; Roles/Writing - original draft; MAH was responsible for: Conceptualization; Roles/Writing - original draft; Writing - review & editing. SMP was responsible for: Data curation; Roles/Writing - original draft; ML was responsible for: Data curation; Roles/Writing - original draft; DJ was responsible for: Data curation; Roles/Writing - original draft; SA was responsible for: Conceptualization; Project administration; Resources; Supervision; Writing - review & editing.

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Shool, S., Rahmani, S., Habibi, M.A. et al. Acute spinal cord injury serum biomarkers in human and rat: a scoping systematic review. Spinal Cord Ser Cases 10 , 21 (2024). https://doi.org/10.1038/s41394-024-00636-3

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• Published: 05 December 2023

Acute care models for older people living with frailty: a systematic review and taxonomy

• Thomas Knight 1 ,
• Vicky Kamwa 1 ,
• Catherine Atkin 1 ,
• Catherine Green 2 ,
• Janahan Ragunathan 3 ,
• Daniel Lasserson 4 &
• Elizabeth Sapey 1  

BMC Geriatrics volume  23 , Article number:  809 ( 2023 ) Cite this article

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The need to improve the acute care pathway to meet the care needs of older people living with frailty is a strategic priority for many healthcare systems. The optimal care model for this patient group is unclear.

A systematic review was conducted to derive a taxonomy of acute care models for older people with acute medical illness and describe the outcomes used to assess their effectiveness. Care models providing time-limited episodes of care (up to 14 days) within 48 h of presentation to patients over the age of 65 with acute medical illness were included. Care models based in hospital and community settings were eligible.

Searches were undertaken in Medline, Embase, CINAHL and Cochrane databases. Interventions were described and classified in detail using a modified version of the TIDIeR checklist for complex interventions. Outcomes were described and classified using the Core Outcome Measures in Effectiveness Trials (COMET) taxonomy. Risk of bias was assessed using RoB2 and ROBINS-I.

The inclusion criteria were met by 103 articles. Four classes of acute care model were identified, acute-bed based care, hospital at home, emergency department in-reach and care home models. The field is dominated by small single centre randomised and non-randomised studies. Most studies were judged to be at risk of bias. A range of outcome measures were reported with little consistency between studies. Evidence of effectiveness was limited.

Acute care models for older people living with frailty are heterogenous. The clinical effectiveness of these models cannot be conclusively established from the available evidence.

Trial registration

PROSPERO registration (CRD42021279131).

Peer Review reports

Introduction

Population ageing and the increasing prevalence of long-term health conditions represent a significant challenge to many advanced health care systems [ 1 ]. Older people, particularly those living with frailty and multimorbidity, are at high risk of sudden health crisis necessitating urgent assessment to identify and treat causative conditions. The acute care pathway collectively defines the clinical processes employed to achieve this function. It typically comprises sequential assessment in community and hospital settings and culminates in emergency hospital admission when necessary.

Older people living with frailty are at high risk of adverse outcomes such as mortality [ 2 ] and have longer average lengths of hospital stay when accessing the acute care pathway [ 3 ]. The conversion rate from ED attendance to emergency admission is 3 times higher in people aged over 85 relative to people under 65 [ 4 ]. As older people represent a growing proportion of ED attendances the demand for hospital bed-based care is likely to rise [ 4 ]. This must be reconciled with downward trends in the number of acute hospital beds at the population level [ 5 ]. Improved integration between health and social care may help mitigate the impact of these changes to some degree but will not abrogate the need for hospital assessment and inpatient bed-based care in the context of sudden deterioration or severe illness [ 6 ]. Adaptations to the acute care pathway may improve the quality of care for older people while simultaneously reducing pressure on an increasingly congested acute care system.

These factors have collectively driven a rapid expansion of studies investigating models of care intended to mitigate the risk of hospital admission or avoid bed-based hospital care entirely [ 7 ]. Previous systematic reviews of acute care models for older people have focused on interventions located at specific points along the acute care pathway [ 8 , 9 , 10 ]. There has been a tendency to group interventions with different eligibility criteria and clinical processes. Differentiating models of care able to manage acute illness from those primarily engaged with rehabilitation and the functional consequence of resolving acute illness is not straightforward. This distinction is important as policy makers and commissioners look to maximise the efficiency of acute hospital bed use and find credible alternatives to acute inpatient care in the community.

It is possible that a more granular classification of the interventions may foster a greater understanding of which elements of the model drive effectiveness and highlight areas of best practice.

A systematic review was undertaken to describe and classify the range of acute care models designed to manage acute medical illness in older people with the objective of deriving a taxonomy of care models. The review also aimed to describe and classify the outcome measures used in studies investigating these models. A secondary objective was to determine whether the proposed taxonomy was useful in understanding any differences in observed outcomes between studies. We took the novel approach of including acute care models operating in hospital and community settings.

Study design

The systematic review was conducted using a two-step process. The first step was undertaken to describe and classify acute care models for older people and the outcome measures used to demonstrate their clinical effectiveness within the current literature. This information was used to create a taxonomy of care models accompanied by a narrative summary. No restrictions were placed on study design at this stage of the process.

The second step looked to describe the effectiveness of each model and restricted analysis to randomised controlled trials or observational studies with an experimental design (including non-randomised trials, cohort studies with comparator groups, before and after longitudinal studies). Previous systematic reviews and meta-analyses were not used to inform the taxonomy. Primary studies from relevant systematic reviews were included if they met the inclusion criteria. The systematic review was undertaken in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. The study protocol was registered with PROSPERO (CRD42021279131).

Eligibility criteria and study selection

Inclusion and exclusion criteria were designed to incorporate interventions operating within the hospital and the community. An age threshold of 65 years was used to define care models for older people (mean age of study participants > 65 years. Mean age as opposed to a strict age threshold was employed to ensure care models accepting younger patients with frailty identified using alternative measures, such as validated frailty scores or multi-morbidity were not excluded.

The intervention needed to target acute medical illness or acute exacerbation of chronic disease. There is no consensus definition of acute care. To ensure a focus on acute care, study participants needed to be recruited within 48 h of presentation and the care model had to provide time limited episodes of care (up to 14 days). The requirement for time limited episodes of care was used as a criterion to exclude care models delivering ongoing chronic disease management after resolution of acute illness which were felt likely to employ different care processes and focus on different clinical outcomes. Recruitment direct from the ED was used as a proxy for recruitment within 48 h in studies where this metric was not reported. Community interventions were only included if they were able to provide a credible alternative to hospital bed-based care. This was defined as the capability to provide face-to-face review alongside access to hospital level treatments (eg intravenous treatments) and hospital level diagnostics (eg blood tests, imaging) at home.

A full list of inclusion and exclusion criteria is provided in Table 1 .

Data sources and searches

The search strategy comprised both MeSH terms and keyword text and was performed on 30 th September 2021 with no date restrictions. The search strategy is provided in Supplementary Table 1 . The search was undertaken in 5 electronic databases (Ovid MEDLINE, Ovid Embase, Cumulative Index to Nursing and Allied Health Literature, Cochrane Database of systematic reviews, Cochrane Central Register of Controlled Trials). Hand reference list screening was carried out of all included articles. Systematic reviews were not included directly. All individual studies meeting the inclusion criteria contained within systematic reviews identified by the search were included.

Titles and abstracts were reviewed by two reviewers. (TK reviewed each and at-least one further review from CA, VK, CG, JR). Full-text records were obtained and reviewed against the eligibility criteria. Disagreements were resolved by a third reviewer (DL). Data extraction was undertaken by 1 reviewer (TK). A bespoke data extraction tool was adapted from the TIDIeR checklist to characterise each intervention [ 11 ]. Outcome measurements were classified using the Core Outcome Measures in Effectiveness Trials (COMET) taxonomy [ 12 ].

Data extraction and quality assessment

Risk of bias was assessed using criteria from the Cochrane Handbook. Randomised controlled trials were assessed using RoB-2 tool [ 13 ] and observational studies were assessed using the ROBINS-I tool [ 14 ]. Risk of bias was assessed by 1 reviewer (TK).

Data synthesis

Finding from included articles were grouped and summarised. Due to clinical heterogeneity between studies meta-analysis was not appropriate. A narrative synthesis of the results was undertaken. Visualisations were created using R statistical software (Version 1.3.1093, Vienna. Austria). The geographical location of included studies was mapped using the ggmap package. Source maps were obtained from © Stamen Design, under a Creative Commons Attribution (CC BY 3.0) license. Outcome areas and domains were plotted using the treemap package.

The initial search returned 13,102 relevant articles. Title and abstract screening identified 340 relevant articles for full text review. A total of 90 articles met the eligibility criteria. Hand searching of references identified 13 further articles. Therefore, 103 articles were included in the analysis (see Fig.  1 ). Identified articles were published between April 1991 and April 2021. This comprised 20 randomised controlled trials reported across 26 articles), 6 study protocols (results for 2 had been reported and were included), 38 observational studies with a comparator group reported across 51 articles, and 20 descriptive studies without a comparator group. The search identified 101 conference abstracts which did not contain sufficient information to adequately describe the model of care delivered. These abstracts were not used to inform the taxonomy.

A PRISMA flow diagram for the studies screened and included in the systematic review. Legend: Studies were screened against the inclusion and exclusion criteria described in Table 1 . Reasons for exclusion are provided

The articles could be broadly categorised into four groups based on the model of care they described. These included: bedded acute frailty units (AFU), Hospital at Home models (HaH), ED based in-reach models and acute care home models, see Fig.  2 . A detailed description of the interventions described in each individual study is provided in Supplementary Table 2 . The geographical location of included studies is provided in Fig.  3 .

The Proposed taxonomy of acute care models for older people. Legend: The taxonomy was defined using key features of the care models; Care models were initially differentiated based on location. Acute bedded frailty units operated from a fixed bed base or offering consultation to general medical wards. Hospital at home models were differentiated based on their use of telemedicine. Physician intensive models used face to face review at home as standard. Remote oversight models were primarily delivered by specialist nurses with care supported provided remotely by physicians on a selective basis. Emergency Department in reach models could be differentiated by their staffing model. Nurse led care coordination without direct input from a dedicated geriatrician or care delivered by geriatricians within the Emergency Department. Care home models were differentiated by their primary location of activity, either services offered within the care home or adaptations to the care pathway following transfer to the Emergency Department

A map identifying the countries where the included studies were based. Legend. The map shows the location of included studies identifying: Colours to denote the care model type as defined by the taxonomy. Brown dots represent Hospital at Home models, Violet dots represents bedded Acute Frailty Units. Purple dots Emergency Department in-reach models. Green dots care models. Source maps were obtained from © Stamen Design, under a Creative Commons Attribution (CC BY 3.0) license

Bedded acute frailty units models

The provision of tailored bed-based in-patient care for frail adults as a direct alternative to treatment on a general medical ward was described in 32 articles derived from 24 studies. This included 8 articles [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ] reporting results from 6 randomised controlled trials, 1 trial protocol without results [ 23 ], 11 observational studies with a comparator group reported across 15 articles [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ] and 8 descriptive studies without a comparator [ 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ]. A detailed description of the care models is provided in Supplementary Table 2 A.

The AFU care model has a strong focus on maintaining and restoring function, but in contrast to a rehabilitation ward intervenes prior to full resolution of acute illness. A range of names were used to identify care models with similar underlying approaches, including Acute Frailty units (AFU), Acute Care for Elders (ACE) units and CGA units. Generic descriptions of the model frequently reference four core components, patient centred care, specifically designed environments, review of medical care and early discharge planning as key characteristics of the model. There was considerable variation in how these shared high-level objectives were operationalised within individual care models.

Treatment was delivered within a geographically distinct bedded unit in 20 studies [ 15 , 16 , 17 , 18 , 19 , 21 , 22 , 23 , 24 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 34 , 35 , 38 , 39 , 42 , 44 , 45 , 46 ], of which 7 specifically reported adaptations to optimise the environment for older people [ 15 , 17 , 18 , 23 , 24 , 39 , 41 ]. The mean number of beds in each unit was 18 (SD 8). The number of beds was not reported in 3 studies [ 25 , 41 , 46 ]. A mobile model providing specialist consultations to patients within general medical bed was described in 3 studies [ 20 , 33 , 36 ] (and an integrated service with variable bed capacity operating within an acute medical unit in 1 study [ 45 ].

Eligibility criteria were heterogenous. Age criteria were reported in studies describing 20 care models [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 35 , 36 , 37 , 38 , 39 , 41 , 42 , 44 , 45 ]. Descriptions of the process of patient referral and how eligibility criteria were implemented in practice were uncommon. The presence of additional criteria such as functional impairment or specific geriatric conditions were frequently reported, but it was not possible to establish how these criteria were operationalised. The use of validated frailty assessment tools to define eligible patients were reported in 1 study (reported across 5 articles) [ 26 , 28 , 29 , 30 , 31 ]. Patients from residential care homes were excluded in 2 studies [ 18 , 21 ]. Bed availability was cited as a common determinant of receiving treatment on the AFU.

Hospital at home models

Hospital at home (HaH) models describe the provision of acute medical care within a person’s usual place of residence. The care model aims to replicate acute bed-based care and operate under the assumption that care would be delivered in an acute hospital setting if the model were absent. HaH models were described in 37 articles derived from 27 studies. This included 16 articles [ 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ] reporting results from 12 randomised controlled, 2 protocols (of which 1 had reported results and was included) [ 63 , 64 ], 9 observational studies with a comparator group reported across 15 articles [ 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 ] and 4 descriptive studies without a comparator group [ 79 , 80 , 81 , 82 ]. A detailed description of the care models is provided in Supplementary Table 2 B.

There was significant clinical heterogeneity between included HaH models. The model accommodated patients with unselected acute medical illness in 31 studies and specific disease groups in 7 studies (decompensated heart failure = 3 [ 57 , 58 , 62 ], COPD = 4 [ 47 , 51 , 52 , 70 , 79 ]).

Eligibility criteria to define suitability for HaH care were heterogenous. All included studies made the intention to act as an alternative to hospital bed-based care explicit. Clinical discretion exercised by the HaH team was the arbiter of the appropriateness and safety of HaH care in all the identified studies. No standardised approach to assessment was identified and it was not possible to reliably determine the acuity of included patients from the reported data. The majority of HaH studies specifically targeted adults over the age of 65. In models open to adults of all ages, the mean age of participants was over 65 in all cases. Care home residents were excluded in 9 studies [ 53 , 58 , 59 , 63 , 67 , 73 , 74 , 75 , 80 ].

Care was led by a geriatrician in 6 studies, [ 47 , 59 , 61 , 62 , 73 , 78 ] by a general internal medicine physician in 29 studies and a primary care physician in 2 studies [ 60 , 83 ]. The intensity of physician and nursing involvement varied substantially. Physician involvement ranged from multiple daily physical home visits to remote oversight without direct physical assessment. Specific out-of-hours arrangements were reported in 12 studies reported across 19 articles [ 47 , 53 , 54 , 55 , 61 , 62 , 65 , 67 , 68 , 69 , 71 , 72 , 74 , 75 , 76 , 77 , 81 , 82 , 83 ]. The use of telemedicine was described in 5 studies reported across 11 articles [ 47 , 65 , 66 , 68 , 69 , 71 , 72 , 74 , 75 , 76 , 77 ]. Reporting of the study intervention was often restricted to a description of standardised operating procedure. The frequency of assessment achieved in practice was reported in 6 studies [ 52 , 53 , 58 , 74 , 75 , 81 ] and the proportion of patients receiving specific treatments was reported in 3 studies [ 47 , 53 , 80 ].

ED in-reach models

ED in-reach models aim to optimise processes of care for older people in the ED. The care models typically provide care coordination and elements of CGA to reduce the likelihood of admission to acute-bed based care. ED in-reach models were described in 28 studies describing 27 care models. This included 2 randomised controlled trials, [ 84 , 85 ] 1 randomised controlled trial protocol without results [ 86 ], 12 observational studies with a comparator group [ 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 ] and 13 descriptive studies without a comparator group [ 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 ]. A detailed description of the care models is provided in Supplementary Table 2 C.

Two distinct approaches to the operational design of services were evident. One approach, described in 11 studies, involved the use of bedded areas located within ED clinical decision units (alternatively referred to as ED short stay units) to provide elements of CGA to older patients who required additional assessment and investigation before a decision regarding acute medical admission could be reached [ 87 , 89 , 90 , 91 , 94 , 96 , 104 , 105 , 107 , 109 , 111 ].

An alternative approach, described in 20 studies, involved the provision of elements of CGA directly within the ED. CGA was undertaken by a geriatrician in 10 care models [ 84 , 88 , 97 , 100 , 101 , 102 , 103 , 108 , 110 ] and by specially trained nurses in 7 care models [ 85 , 86 , 92 , 93 , 95 , 98 , 99 , 106 ]. Studies of this care model frequently cited a reduction in the number of avoidable medical admissions as the primary motivation for the service. The distinction between avoidable and unavoidable admissions was poorly defined.

Eligibility criteria were heterogenous. Age criteria were reported in 13 care models [ 84 , 88 , 91 , 93 , 94 , 95 , 98 , 99 , 102 , 103 , 104 , 106 , 108 , 112 ]. The use of validated frailty assessment tools to define eligible patients were reported in 5 care models [ 84 , 86 , 92 , 99 , 106 ]. Care home residents were excluded in 3 studies [ 86 , 91 , 94 ]. Eligibility criteria were not reported in 5 studies [ 87 , 89 , 91 , 109 , 110 ]. A variety of approaches were adopted to identifying potentially eligible patients in the ED. Screening of all patients attending the ED was reported in 3 studies [ 84 , 88 , 93 ]. The service was accessed by a referral from the ED team in 11 care models [ 89 , 90 , 92 , 95 , 98 , 99 , 100 , 101 , 104 , 109 , 110 ]. The process of referral and patient selection were not consistently reported.

Acute care home models

Models targeting care home residents were reported in 5 studies. All 5 studies had an observational design [ 113 , 114 , 115 , 116 , 117 ]. Two categories of intervention were described. The first involved the presence of dedicated staff trained in acute care present with the care home [ 113 , 117 ]. These staff had the ability to deliver acute interventions in the care home. Privileged access was given to the on-call ED physician in both models (augmented by telemedicine in one study) [ 117 ]. The process which triggered assessment by the on-site team were not defined. A detailed description of the care models is provided in Supplementary Table 2 D.

An alternative model involved a hospital-based team providing out-reach to care homes and early assessment of care home resident presenting to ED. Both care models in this category also had the capability to provide ongoing acute care in the care home when required. This was achieved by a geriatrician-led team with the option to provide daily visits in one model [ 116 ] and a specialist ED nursing team in the other [ 114 , 115 ].

Outcome measurements

Outcomes were classified using the COMET taxonomy. Outcomes were reported across 6 core areas and 15 domains. Mortality was reported (in isolation or as part of a composite outcome) in 35 studies, the reporting time horizon ranged from in-hospital mortality to 1 year. Life impact was reported 27 studies, this included measurement of physical function in 21 studies and cognitive function in 6 studies. The tools used to measure physical function and the time horizons of assessment varied.

Resource use was the most reported core outcome measure. Studies frequently described multiple outcome domains related to resource use. The average length of stay was reported in 34 studies and re-admission rate in 39 studies. Readmission rates were reported over a range of time horizons 30 days to 1 year. Care home admission were reported (in isolation of as part of a composite outcome) in 14 studies over a time horizon of 30 days to 6 months. Economic analysis was reported in 19 studies. Adverse events were reported in 22 studies. A detailed summary of the outcome domains, methods of measurement and associated time horizons is provided in Supplementary Table 3 .

The relative frequency with which the outcome domains were reported across all studies is provided in Fig.  4 A and stratified by care model in Fig.  4 B. Outcomes reported by bedded AFU and HaH were broadly similar, although AFU more commonly reported outcomes related to physical function. Economic analysis was less prevalent in studies investigating ED in-reach models. A focus on aspects of care delivery, such as disposition from the ED and analysis of clinical processes relevant to the quality and adequacy of intervention were more common in studies evaluating ED in-reach.

Tree diagrams: A tree diagrams representing the relative proportion of outcomes reported in all studies. B Tree diagrams representing the relative proportion of studies by study group. Legend. * Treemap representing hierarchical outcome data using nested rectangles. Large rectangle represent core outcome areas, smaller rectangular tiles within each core outcome area represent outcome domains. Each rectangle has an area proportional to the frequency reported within included studies. All studies n  = 103, Bedded acute frailty unit n  = 32, Hospital at Home n  = 38, ED in reach models n  = 28, Care home n  = 5

Effectiveness

Clinical heterogeneity amongst the care models identified and disparity in the outcomes measured used to evaluate the care models precluded meta-analysis. Risk of bias was assessed for each study. Aggregated results of the domain-based risk of bias assessment tools are provided in Fig.  5 and the results of individual study assessments are provided in Supplementary Table 4 .

Summary of bias assessments. A Summary of randomised controlled studies using RoB2 tool. B Summary of non-randomised studies using ROBINS-I tool

The nature of the intervention precluded blinding of participants or personnel to group allocation in all included randomised controlled trials. Partial blinding of outcome assessment was reported in one study investigating the effectiveness of bedded AFUs [ 17 ] and assessment was unblinded in the remainder. Blinding during outcome assessment was reported in 4 randomised controlled trials investigating HaH [ 47 , 52 , 59 , 60 ]. Outcome assessment was unblinded in both randomised controlled trials investigating ED in-reach models [ 84 , 85 ]. All the studies investigating bedded AFUs were undertaken in single sites which may have led to contamination of the control arm. This would be anticipated to favour the null hypothesis [ 15 , 16 , 18 , 19 , 20 , 21 , 22 ]. Contamination of the control arm was less likely in HaH models delivered by distinct clinical teams.

All included observational studies were at serious or critical risk of confounding. The decision to manage patients in the intervention arm is likely to have been selective, based on clinical judgment informed by pre-intervention clinical characteristics. Only 5 studies employed robust statistical techniques to control for confounding [ 65 , 67 , 69 , 78 , 92 ]. Residual confounding from unmeasured prognostic factors posed at risk of bias all included observational studies.

Effectiveness of acute care models

Bedded acute frailty unit models.

No statistical difference in primary outcome was observed in 2 randomised controlled trials (reported across 3 articles) of specialist bed-based care for unselected older medical patients, 1 study measured the composite outcome of death, severe dependence and psychological well-being [ 15 ] and the other physical function at 3 months following discharge [ 19 ]. A planned cost-analysis demonstrated no difference in the total cost of admission between groups [ 16 ]. A single centre randomised controlled trial comparing a specialist unit for acutely unwell patients with cognitive impairment with usual care demonstrated no statistical difference in the composite outcome of days at home [ 17 ]. All included observational studies were judged to be at critical or serious risk of bias.

The largest randomised controlled trial included 1055 participants [ 59 ]. The study was designed to recruit to the HaH intervention at a ratio of 2:1. A significant number of participants moved from the control to the intervention arm due to operational pressures within the hospital. The study found no difference in the primary outcome of living at home at 6 months (the inverse of death or long-term residential care) [ 59 ]. The remaining 11 trials (reported across 15 articles) had smaller sample sizes (mean 81 participants, SD 33). One randomised controlled trial (2 articles) reported a statistically significant reduction in the rate of adverse events [ 50 ] and favourable functional outcomes in the group allocated to HaH care [ 49 ].

HaH care for older people with decompensated heart failure was investigated in 2 randomised controlled trials, 1 reported no difference in mortality or readmission at 6 months [ 62 ] and 1 no difference in mortality or readmission at 12 months [ 57 ]. HaH care for older people with an acute exacerbation of COPD was investigated in 2 randomised controlled trials, 1 reported a statistically significant reduction in readmissions at 6 months and no difference in mortality at 6 months [ 47 ] and 1 reported lower costs at 90 days, driven by shorted length of stay in the HaH group, with no difference in mortality or readmission rate at 90 days [ 52 ]. Economic analysis determined HaH was associated with lower costs in 1 randomised controlled trial of participants with unselected medical-illness [ 53 ]. Nested analysis of patient and carer satisfaction was included in 5 trials [ 47 , 52 , 53 , 59 , 62 ] in 3 trials the findings were reported in separate articles [ 51 , 55 , 66 ]. All showed an increase in measures of patient satisfaction in the HaH intervention group.

One randomised trial compared two contrasting models of HaH. The study arms compared HaH care led by primary care physicians with care led by hospital specialists [ 60 ]. Those in the hospital specialist arm were initially assessed in the ED and discharged within 4 h of assessment with a home-based care plan. The hospital specialist team did not undertake home visits. Those in the primary care physician arm received care exclusively at home. In both arms the plan care was delivered by a dedicated HaH nursing team. The primary care physician model was a associated with a statistically significant reduction in hospital admission at 7 days. A series of articles published as part of a non-randomised controlled trial [ 75 ] reported a reduction in length of admission, [ 75 ] reduced levels of carer stress [ 71 ] and no difference in physical function [ 72 ] in the HaH group.

ED in reach models

No statistical difference in the primary outcome measure was observed in 2 randomised controlled trials investigating ED in-reach models. In one study the provision of geriatrician lead CGA to patients aged over 75 with a clinical frailty scale (CFS) of 4 or above did not affect cumulative length of stay over a 1 year follow up period [ 84 ]. A randomised controlled trial investigating provision of nurse-led care coordination in the ED found no significant effect on the rate of hospital admission [ 85 ]. Uncontrolled before and after studies were a common methodological approach to the assessment of ED in-reach models, employed in 5 studies. All included observational studies were judged to be at serious risk of bias.

This systematic review provides a summary and classification of acute care models for older people living with frailty and an assessment of effectiveness based on current published evidence. The care models identified could be broadly differentiated by the location within the acute care pathway at which they operate. This generic classification provides a degree of structure to a large and complicated field of research, sensitive to the fact that relevant interventions have emerged across hospital and community settings. The spectrum of outcomes reported and differing approaches to measurement suggest consensus on how best to determine the effectiveness of these care models has yet to emerge.

The clinical effectiveness of acute care models for older people was difficult to determine from the available studies. The number of participants within each trial was small. The risk of confounding by indication was pervasive amongst observational studies and statistical techniques to control for cofounding were generally absent or inadequate. These methodological limitations prevented meaningful comparisons of the impact on outcomes between care models. There is a paucity of contemporary data on the effectiveness of acute care models for older people. Some of the most influential studies were conducted over two decades ago. This raises the concern that the clinical processes employed may now be obsolete.

Complex interventions, such as acute care models for older people are often difficult to characterise. The detailed summary of individual interventions provided within this review highlights the contrasting approaches adopted by services under the same umbrella.

Few studies adopted a structured approach to defining the intervention under investigation and the descriptions provided varied in depth and quality. The nature of care provided in the usual care arm of comparative studies was equally difficult to define. The absence of consistent inclusion and exclusion criteria or knowledge of how criteria were operationalised makes it difficult to discern the population targeted by each intervention. Assignment often incorporated a subjective assessment by an individual clinician acting as gatekeeper. Thresholds for admission and discharge are not standardised and risk tolerance may vary at the individual, hospital and system level. This is particularly pertinent to studies investigating the role of HaH and ED in-reach models, predicated on the assumption that care would inevitably require in-patient bed-based care if the intervention was absent. This assumption is inherently difficult to substantiate. All the HaH models included in this systematic review had access to hospital level diagnostics and interventions but the proportion of patients receiving these interventions were inconsistently reported. This obfuscates an objective assessment of acuity and whether hospital admission was warranted.

Comparison with previous literature

Clinical heterogeneity in the studies included in previous systematic reviews and the absence of universally accepted definitions for the care models investigated cloud interpretation of the existing literature. The diverse range of approaches to patient selection, operational design and outcome measurement highlighted in this review suggests caution is warranted when pooling studies in this subject area.

Several systematic reviews investigating acute care models for older people have focused the delivery of comprehensive geriatric assessment (CGA) [ 8 ]. CGA involves multidimensional assessment with particular attention on the functional consequences of illness [ 118 ]. CGA has been shown to increase the likelihood of being alive or returning to home at 3 to 12 months follow up amongst older patients admitted to hospital with acute illness [ 8 ]. Meta-analysis of CGA delivered in bed-based frailty units found a lower risk of functional decline, a higher likelihood of living at home after discharge and no differences in mortality [ 119 ]. CGA delivered in bed-based frailty units may also reduce the incidence of adverse events such as falls, delirium and pressure sores at discharge [ 10 ]. The inclusion of interventions delivered on rehabilitation wards, and patients with surgical and orthopaedic presentations in previous systematic reviews limits generalisation to care models employed at earlier time points in the acute care pathway. The available literature suggests alternatives to usual bed-based care incorporating CGA may be of benefit but offers little to guide how these services should be designed and implemented. When inclusion is limited to interventions employed within 48 h of presentation the evidence of effectiveness is less compelling. This is important given the benefit of CGA is cited as the primary motivation for operational models located upstream in the acute care pathway [ 120 ].

HaH models have also been the subject of systematic review and meta-analysis. A Cochrane review of admission avoidance HaH identified ten randomised controlled trials including 1333 participants of which 850 were included in individual patient level meta-analysis [ 121 ]. The analysis demonstrated a significant reduction in mortality at 6 months (adjusted HR 0.62, 95% CI 0.45–0.87). A more recent systematic review and meta-analysis found patients managed in HaH following discharge from the ED had a lower risk of admission to institutional care (RR 0.16 95% CI 0.03–0.74) and no difference in mortality (RR 0.84 95% CI 0.6–1.2) [ 122 ]. These systematic reviews pooled results from studies investigating HaH in the context of a diverse range of conditions including stroke, cellulitis, fractures and respiratory illness which would be expected to employ very different clinical processes. Applying a more restrictive approach to study inclusion, by only including HaH models with access to hospital level diagnostics and treatments allows greater confidence in the assertion that the HaH models included in the current review offered a true alternative to hospital admission.

Implications for policy and future research

The provision of acute care models for older people are predicated on a logic model rather than empirical evidence of benefit. Further large and rigorously constructed randomised controlled trials may strengthen the evidence base but may not be the most effectual method of influencing local decisions on service provision or the direction of policy.

Research in acute care delivery is complicated by a need to maintain operational performance. Amongst the studies identified, bed availability and restricted operational hours frequently resulted in a large differential between the number of potentially eligible participants and the number of patients ultimately included. Practical considerations aside, the outcomes of interventional studies are likely to be highly dependent on local context and external factors which influence generalisability.

Knowledge in this subject area may be enhanced by developing a consistent approach to outcome reporting and measurement, ideally incorporating the priorities and preferences of patients. Mortality may not be the most appropriate metric of effectiveness given a significant proportion of older people living with frailty requiring acute care for medical illness are entering the last 12 months of life [ 123 ]. Current models of acute care infrequently establish and record individual preferences in relation to location of care in the event of acute medical illness or preferred location of death amongst older people [ 124 ]. A narrow focus on clinical and operational outcomes may simplify study design, facilitate comparisons and provide reassurance around safety but risks ignoring other aspects of care, such as quality of life, which may be more meaningful from the patient perspective.

Given the complexity of the intervention, an understanding of the processes and behaviours which drive successful models may be best approached from a qualitative research paradigm.

Strength and limitations

The primary objective of this systematic review was to describe and categorise acute care models for older people and highlight variation in the outcome measures used to assess them. An extensive search strategy inclusive of the grey literature and indifferent to methodological design was purposefully employed in order to capture a comprehensive representation of the range of models in operation. Every acute hospital encounters older people living with frailty and the potential for variation in approach is vast. Only a small fraction of care models delivered in practice are reported in the literature. The practice of publishing multiple articles from the same original study was relatively common, particularly in literature pertaining to acute bed-based care and HaH models. The account provided is therefore susceptible to both publication and outcome reporting bias.

Availability of data and materials

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Knight, T., Kamwa, V., Atkin, C. et al. Acute care models for older people living with frailty: a systematic review and taxonomy. BMC Geriatr 23 , 809 (2023). https://doi.org/10.1186/s12877-023-04373-4

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Mathematical models of drug-resistant tuberculosis lack bacterial heterogeneity: A systematic review

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom, Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom, Antimicrobial Resistance Centre, London School of Hygiene and Tropical Medicine, London, United Kingdom, Tuberculosis Centre, London School of Hygiene and Tropical Medicine, London, United Kingdom

Roles Data curation, Validation, Writing – review & editing

Roles Data curation, Writing – review & editing

Roles Conceptualization, Writing – review & editing

Affiliation UCL Centre for Clinical Microbiology, Division of Infection & Immunity, Royal Free Campus, University College London, London, United Kingdom

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

• Naomi M. Fuller, 
• Christopher F. McQuaid, 
• Martin J. Harker, 
• Chathika K. Weerasuriya, 
• Timothy D. McHugh, 
• Gwenan M. Knight

• Published: April 10, 2024
• https://doi.org/10.1371/journal.ppat.1011574
• Peer Review
• Reader Comments

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Drug-resistant tuberculosis (DR-TB) threatens progress in the control of TB. Mathematical models are increasingly being used to guide public health decisions on managing both antimicrobial resistance (AMR) and TB. It is important to consider bacterial heterogeneity in models as it can have consequences for predictions of resistance prevalence, which may affect decision-making. We conducted a systematic review of published mathematical models to determine the modelling landscape and to explore methods for including bacterial heterogeneity. Our first objective was to identify and analyse the general characteristics of mathematical models of DR-mycobacteria, including M . tuberculosis . The second objective was to analyse methods of including bacterial heterogeneity in these models. We had different definitions of heterogeneity depending on the model level. For between-host models of mycobacterium, heterogeneity was defined as any model where bacteria of the same resistance level were further differentiated. For bacterial population models, heterogeneity was defined as having multiple distinct resistant populations. The search was conducted following PRISMA guidelines in five databases, with studies included if they were mechanistic or simulation models of DR-mycobacteria. We identified 195 studies modelling DR-mycobacteria, with most being dynamic transmission models of non-treatment intervention impact in M . tuberculosis (n = 58). Studies were set in a limited number of specific countries, and 44% of models (n = 85) included only a single level of “multidrug-resistance (MDR)”. Only 23 models (8 between-host) included any bacterial heterogeneity. Most of these also captured multiple antibiotic-resistant classes (n = 17), but six models included heterogeneity in bacterial populations resistant to a single antibiotic. Heterogeneity was usually represented by different fitness values for bacteria resistant to the same antibiotic (61%, n = 14). A large and growing body of mathematical models of DR-mycobacterium is being used to explore intervention impact to support policy as well as theoretical explorations of resistance dynamics. However, the majority lack bacterial heterogeneity, suggesting that important evolutionary effects may be missed.

Author summary

The emergence of drug-resistant tuberculosis (DR-TB), where the causative bacterium Mycobacterium tuberculosis is resistant to key antibiotics such as rifampicin and isoniazid, poses a significant threat to TB control efforts. To gain a broader understanding of the challenges surrounding DR-TB, mathematical models are increasingly being employed to estimate the impact of interventions, effectiveness of treatment, and to predict the evolution of drug-resistance. However, pragmaticism surrounding model construction often means that important aspects, such as bacterial heterogeneity, are overlooked. We undertook a systematic review of the existing DR-mycobacterium modelling literature, with the specific aim of capturing methods for including bacterial heterogeneity. Our analysis revealed that most models of drug-resistance in mycobacteria primarily focus on intervention strategies and cost-effectiveness analyses, with minimal attention to bacterial heterogeneity. Where heterogeneity is included it mostly consisted of different fitness costs for resistance.

Citation: Fuller NM, McQuaid CF, Harker MJ, Weerasuriya CK, McHugh TD, Knight GM (2024) Mathematical models of drug-resistant tuberculosis lack bacterial heterogeneity: A systematic review. PLoS Pathog 20(4): e1011574. https://doi.org/10.1371/journal.ppat.1011574

Editor: Mark Robert Davies, University of Melbourne, AUSTRALIA

Received: July 25, 2023; Accepted: March 25, 2024; Published: April 10, 2024

Copyright: © 2024 Fuller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This research and NMF was funded by the Biotechnology and Biological Sciences Research Council through the London Interdisciplinary Doctoral Training Programme (BBSRC LIDO, https://www.lido-dtp.ac.uk ) at the London School of Hygiene and Tropical Medicine (LSHTM) in partnership with University College London (UCL), Grant code - BB/M009513/1. CFM was funded for other work by Bill and Melinda Gates Foundation (TB MAC OPP1135288, INV-059518, https://www.gatesfoundation.org ) and Unitaid (20193–3-ASCENT, https://unitaid.org/calls/#en ). CKW was supported by a grant from the Bill and Melinda Gates Foundation (INV-001754, https://www.gatesfoundation.org ). GMK was supported by Medical Research Council UK, https://www.ukri.org/opportunity/career-development-award/ (MR/ W026643/1). The views expressed are those of the authors and not necessarily those of the BBSRC, LIDO, LSHTM or UCL. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Drug-resistant (DR-) strains of Mycobacterium tuberculosis ( M . tuberculosis ) are an urgent threat to the control of tuberculosis disease (TB) globally. For TB, the backbone antibiotics of standard therapy are rifampicin and isoniazid. In 2021, multidrug-resistant (combined rifampicin and isoniazid resistance) or rifampicin-resistant tuberculosis (MDR/RR-TB) caused an estimated 450,000 cases globally [ 1 ].

Routinely collected antimicrobial resistance (AMR) data use microbiological definitions of resistance, which are guided by threshold cut-offs for phenotypic resistance, resulting in discrete categorisations. For TB, these categorisations are further grouped with strains being classified as drug-susceptible (DS-), multidrug- or rifampicin-resistant- (MDR/RR-), pre-extensively-drug (pre-XDR) resistant (MDR plus resistance to a fluoroquinolone) or XDR- resistant (MDR plus resistance to a fluoroquinolone and a Group A drug) [ 1 ]. The MDR/RR grouping is based on the knowledge that isoniazid resistance is commonly acquired prior to rifampicin resistance and the wider prevalence of rifampicin-resistance testing through genotypic testing, making clinical management of RR- and MDR-TB similar [ 2 , 3 ]. These definitions are sufficient for patient care decision-making that does not need to account for the spectrum of phenotypic resistance levels (for example, those below the threshold for successful treatment) or any other bacterial characteristics (such as types of resistance-conferring mutations). However, bacterial populations are often highly diverse with a spectrum of characteristics. Hence, resistance categories will also have a high degree of bacterial heterogeneity, such as variation in transmission fitness between strains with the same phenotypic resistance, which affects the rate at which M . tuberculosis spreads between individuals.

Several important insights into the evolution of DR-TB, its emergence and spread, and the control of resistant bacteria more broadly have been generated by mathematical models. Some examples are the predominance of primary rather than acquired resistance, the effectiveness of TB surveillance for controlling DR-TB, and the potential impact of controlling HIV on reducing TB transmission [ 4 – 7 ]. Most mathematical models of AMR have typically adopted binary ( e . g . resistant versus susceptible) categorisations. When bacterial heterogeneity is included in mathematical models, the predicted public health outcomes can be different from those when bacterial heterogeneity is ignored [ 8 ]. We may lose subtlety in model outputs when modelling antibiotic treatment as a selective pressure if the traits allowing for bacterial heterogeneity are not included. Models may miss key dynamics, such as competition between strains and antibiotic effectiveness against strains with varying resistance levels, and be at risk of incorrectly predicting the effectiveness of a treatment intervention. As Trauer et al. (2018) point out, strain diversity, virulence and fitness costs have implications for the trajectory of drug resistance in TB [ 9 ]. Decisions as to what to include in a model will depend on the questions being asked, the selective pressures modelled, and the time-frame studied. Assessing this balance in model design between detailed and generalised parameters to allow a pragmatic approach for public health interventions can often prove challenging. Hence, assessing the extent to which bacterial heterogeneity has been included in existing models that predict intervention impact for DR-TB control is highly important.

Previous systematic reviews have explored the landscape of mathematical models of AMR [ 7 , 10 ] and TB [ 11 – 14 ], with up to 43 DR-TB transmission and 52 within-host studies being found prior to 2016. To our knowledge, only one expert review from 2009 focused on mathematical models of DR-TB [ 4 ], emphasising the useful insights from modelling but also highlighting important knowledge gaps in the economics, biological impact of mutations and ability to control DR-TB. To date, there is little evidence on how bacterial heterogeneity is incorporated into DR-TB models and little evidence of the effect this would have on model outcomes.

Mycobacteria predominantly develop antibiotic resistance via mutation [ 15 ], resulting in different patterns of resistance dynamics to other bacterial genera. Mycobacterial species other than M . tuberculosis can often be used as experimental or theoretical models for M . tuberculosis and are also responsible for a clinical burden [ 16 – 18 ]. They are often used to understand the resistance dynamics of M . tuberculosis [ 19 , 20 ].

We aimed to support future modelling of interventions against DR-TB by systematically surveying the characteristics of mathematical models of mycobacteria, of which we expect the M . tuberculosis species to dominate due to its substantial clinical burden. Our secondary objective was categorising the amount and type of bacterial heterogeneity included in mathematical models of DR-mycobacteria. We envisaged two broad settings of papers to be included in this review, within-host and between-host transmission models. This was noted by Cohen et al. (2009), a previous review of the DR-TB modelling literature [ 4 ], where “between-host” models refer to models on the human population scale. Since 2009, there has been an increase in models of bacterial populations set in the laboratory. As the populations captured will be similar to within-host models, we combined laboratory models and within-host models and collectively called them “bacterial population” models.

The aims, dynamics and model structure of between-host models differ considerably from bacterial population models, namely by transmission of the pathogen and populations included, making them difficult to compare. Therefore, we defined heterogeneity differently for bacterial populations and between-host models to compare methods within these categories and gain a clearer picture of bacterial heterogeneity modelling. At the between-host level, we were interested in capturing those models that went beyond capturing resistance phenotypes but included any added dimension of bacterial variation, including what may affect survival, such as fitness effects. Models of bacterial populations that captured any resistance variation were included; distinct populations of resistant bacteria needed to be modelled, which differed in their parameter values (e.g. growth rate or mutation rate).

Our review consisted of two stages of selection and data analysis. In Stage 1 of the review, our aim was to identify and analyse the general characteristics of mathematical models pertaining to drug-resistant (DR-) mycobacteria, such as model type and aim. In Stage 2 of the review, our focus was to identify mathematical models of DR-mycobacteria that specifically incorporated the concept of bacterial heterogeneity, as elucidated by the definition in the inclusion and exclusion criteria section.

Search strategy

The systematic review was designed and conducted following the PRISMA reporting protocol to search and review mathematical modelling papers of DR-mycobacteria [ 21 ]. The search terms consisted of those relevant to [ 1 ] “mycobacteria”, [ 2 ] “mathematical modelling”, and [ 3 ] “antibiotic resistance” ( S1 Text ). The search was conducted in five databases (Medline, Embase, Global Health, Web of Science and Scopus) initially on January 22nd, 2021, and then repeated on April 1st, 2022. Duplicates were removed before screening.

Inclusion and exclusion criteria

The screening process of the papers adhered to predefined inclusion and exclusion criteria ( Table 1 ). Initially, the titles and abstracts of the papers were screened to identify mathematical models specifically pertaining to DR-mycobacteria, followed by a full-text screening for inclusion in Stage 1. Finally, another round of full-text screening was carried out on the remaining papers to identify those appropriate for Stage 2 of the study.

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https://doi.org/10.1371/journal.ppat.1011574.t001

Mathematical models were defined as mechanistic models or simulation models reproducing a mathematically described scenario of DR-mycobacteria or of individuals carrying DR-mycobacteria. We excluded statistical analyses, such as regression models or risk analysis; molecular modelling (those focused on molecular structure of chemical compounds) or those only focused on drug development; models of drug-resistance that only used mycobacteria as an example or discussion point unless results for DR-mycobacteria were specifically included.

We split models into two groupings: “between-host” and “bacterial population” models, with the differences in their model scale, structure, and aims, resulting in a different bacterial heterogeneity definition. A “between-host” model was classed as a heterogenous model when strains infecting a human population resistant to the same drug varied in another characteristic such as fitness, rates of compensatory mutation evolution or associated treatment recovery rates. These characteristics were extracted during the full-text extraction stage. “Bacterial population” models included both within-host and models of bacterial populations capturing dynamics measured in laboratory or experimental conditions. A bacterial population model was classed as a heterogeneous model when there were distinct resistant strains captured which had different parameter values such as fitness, mutation rates and metabolic states. These parameter differences were extracted during the full-text extraction stage.

Selection and extraction: Stage 1

Title and abstract screening were performed for every paper by at least two authors (NMF, GMK, CFM, MJH and CKW) to determine if the paper likely included a mathematical model of DR-mycobacteria. High-level data extraction from these screened papers that continued to match the criteria for Stage 1 upon full-text screening provided a landscape analysis of DR-mycobacteria models. DR-mycobacteria models can address multiple aims with various methods, but they will have a common theme, such as parameter estimation or evaluation of the impact of interventions. We extracted information from the models to categorise and classify them into five categories, focusing on the main theme of the model. 1) model setting (such as geographic location), 2) model aims (7 categories of; non-treatment i nterventions that did not explore antibiotic usage (with and without cost-effectiveness), treatment interventions (with and without cost-effectiveness), parameter estimation, burden estimation or theoretical), 3) model type (7 categories of; bacterial dynamics, decision analytic, PK/PD, state transition (with and without a statistical component) or transmission (with or without an operational or state transition component), 4) mycobacterial species and 5) resistance classifications (such as MDR or XDR) ( S2 Text ). We extracted resistance classifications based on what the authors defined in their papers, as current resistance definitions are continuously updated. A resistance class is defined as a model stratification whereby strains (or the populations including them) are grouped across multiple antibiotic resistances (i.e. MDR could here be a single “resistance class” but represents resistance to multiple antibiotic agents). We only extracted which antibiotics were modelled in papers if their resistance was also considered. This extraction was performed by NMF and GMK, with discussions to resolve any conflicts.

Selection and extraction: Stage 2

For Stage 2, full-text screening of the Stage 1 papers was performed by three authors (NMF, GMK, CFM) to determine the models with bacterial heterogeneity, with subsequent discussions and consensus to resolve any discrepancies. NMF performed full-text extraction and data analysis of the extracted data from these papers ( S2 Table ). Stage 2 extracted data on the methods used to model heterogeneity, types of heterogeneity included, data sources and the effect of resistance inclusion (such as resistance effects on disease progression) ( S3 Text ).

After the removal of duplicates, 3,180 papers were identified ( Fig 1 ). Following a title and abstract screening, 372 papers remained for full-text screening. 195 papers were found to fulfil our Stage 1 criteria having a model of DR-mycobacteria strains ( S1 Table ). Of these papers, only 23 were found to meet the requirements of bacterial heterogeneity in mathematical models of DR-mycobacteria ( S2 Table ).

https://doi.org/10.1371/journal.ppat.1011574.g001

Stage 1 Results: DR-mycobacteria model landscape

Most models of mycobacteria were of M . tuberculosis (190 papers/97%) with HIV (59 papers) and diabetes mellitus (5 papers) often included. There was a rapid increase in the number of papers published on DR-mycobacterium from 2005 onwards ( S1 Fig ).

Settings captured

119 papers aimed to model a specific geographical location, typically at the national level ( Fig 2A and S3 Table ). This reflects the settings with the highest MDR-TB incidence but also highlights some countries that are not being focused on ( Fig 2B ). Of the 117 papers, 82 covered a single national analysis and 35 covered different countries. Other geographical locations included 7 models with a global focus, whilst 6 models covered regions with 4 models of Southeast Asia [ 22 – 24 ], and 1 of Eastern Europe [ 25 ] and 1 of the Asia-Pacific [ 26 ].

(a) Countries captured in models of DR-mycobacteria. Note: some models include outputs for multiple countries, therefore this image represents all countries modelled, not the total number of models. (b) From the WHO Global Tuberculosis Report 2022 [ 1 ], the 10 countries with the highest estimated MDR/RR-TB incidence are given with number of models in brackets. The colours in the table match the corresponding colours of the country in part (a). Map layer made with Natural Earth, free vector and raster map data @ naturalearthdata.com .

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Model aims and types

Of the seven distinct categories of study aim found ( Fig 3 ), non-treatment interventions without cost-effectiveness considered (n = 45, 23%) was the most common. Transmission models (n = 129, 67%) were the most common model type used for all model aims, except for “treatment interventions with cost-effectiveness”, which mostly used state transition models ( Fig 3 ). As would be expected, PK/PD models were used almost exclusively for “treatment interventions”, with one model being used for parameter estimation. Six models used a combination of methods: transmission and state transition [ 27 , 28 ], transmission and operational [ 29 ]and state transition and statistical [ 30 – 32 ]. “Bacterial dynamics” type models were used for “treatment interventions”, “theoretical” and “parameter estimation” aims only. “Decision analytic” type models were used for all aims other than “theoretical” and “parameter estimation”.

The model type (colours) definitions can be summarised as follows: [ 1 ] Bacterial dynamics: Capture bacterial populations without considering between-host transmission. [ 2 ]; Decision analytic: Track cohorts of human individuals through treatment or diagnostic pathways without ongoing transmission. [ 3 ] Pharmacokinetic/pharmacodynamic (PK/PD): Focus on drug concentrations and their effects in vivo, incorporating parameters related to bacterial populations. [ 4 ] State Transition: Involve individuals or populations transitioning between different disease states, with the force of infection as a static input parameter. [ 5 ] Statistical: inference-based models of collected or population data. [ 6 ] Transmission: Dynamically account for the spread of bacteria between individuals or populations. [ 7 ] Operational models: simulation of patient pathways and treatment or diagnostic procedures. The model aim (x axis) definitions can be summarised as follows: (1) Non-treatment Interventions: Model the impact of interventions not related to changes in antibiotic usage or treatment without considering economic aspects. (2) Non-treatment Interventions + cost-effectiveness: Model the impact of interventions not related to changes in antibiotic usage or treatment while considering their economic impact. (3) Treatment interventions: Model interventions related to changes in antibiotic usage. (4) Treatment interventions + cost-effectiveness: Model interventions related to changes in antibiotic usage while considering their economic impact. (5) Parameter estimation: Estimate parameters by comparing to data, trends, or varying model structures or components. (6) Burden estimation models: Quantify the number of individuals potentially infected with DR-mycobacteria. (7) Theoretical models: Theoretically explore interactions between susceptible and resistant strains. Note: "CE" stands for cost-effectiveness. For full details of aim and model type see S2 Text .

https://doi.org/10.1371/journal.ppat.1011574.g003

Resistance categories

Most models of DR-mycobacteria capture resistance to fewer than three antibiotics. Six models considered all possible combinations of resistance to several antibiotics (‘*’, Fig 4 ). Of 16 models to capture four or more resistances at once, 11 of these models included antibiotic resistance as stepwise accumulation of resistance [ 22 , 30 , 33 – 41 ] and 5 models only included mono-resistance of resistance to multiple antibiotics [ 20 , 42 – 45 ].

Each coloured cell represents a specific combination of resistances included in a model, with the size of the cell representing how many models included this combination of resistances. “Single” and “Multiple” sections refer to the number of antibiotic resistances included in a model, with “Multiple” referring to models that captured resistance to more than one antibiotic. "*" indicates the model included all possible combinations of antibiotic resistance listed. A = INH, RIF, MDR/RR, MOX, PZA, BDQ, PA, RIF + MOX, RIF + PZA, B = INH, RIF, MDR/RR, AMI, MOX, BDQ, RIF + MOX, RIF + AMI, RIF + BDQ, C = INH, RIF, MDR/RR, XDR, MDR + FQ, MDR + SLInject, D = INH, RIF, MDR/RR, XDR, Pre-XDR. Antibiotic abbreviations as follows: AMI = amikacin, BDQ = bedaquiline, CLR = clarithromycin, ETM = ethambutol, FQ = undefined fluoroquinolone, MOX = moxifloxacin, PA = pretomanid, PZA = pyrazinamide, STR = streptomycin, INH = isoniazid, RIF = rifampicin, MDR/RR = multidrug resistant/rifampicin resistant, XDR = extensively drug-resistant, SLInject = second line injectable antibiotic (from WHO guidelines 2014). S1 Fig shows all resistance categories per 195 models.

https://doi.org/10.1371/journal.ppat.1011574.g004

Overall, for stage 1, most models included a resistance class of MDR/RR-TB (129 papers/67%, Fig 4 ) with 85 models that chose to model only a single resistance class of MDR/RR-TB alongside DS-TB ( Fig 4 ). 40/195 models included isoniazid resistance ( Fig 4 ) with 27/40 also including MDR/RR-TB. 21/195 models included rifampicin resistance separate from MDR with 15/21 including isoniazid and rifampicin resistance as mono-resistances that developed into MDR with 6/15 models including the development of XDR-TB. Of 18 models that modelled XDR, 16 included MDR/RR, while two did not [ 46 , 47 ]. Out of the first-line antibiotics used to treat TB, isoniazid (n = 40) and rifampicin (n = 27) resistance were modelled the most, followed by pyrazinamide (n = 8) and then ethambutol (n = 5) resistance. Pyrazinamide resistance was often found to be modelled alongside rifampicin and/or isoniazid resistance with only 3 models including resistance to all 4 first-line antibiotics, 2 with mono-resistances and 1 with a combination of all 4 resistances [ 33 , 37 , 42 ] ( Fig 4 ).

41 theoretical models included resistance to a non-named antibiotic ( S1 Table ). One of these explored differences in drug action (bacteriostatic or bactericidal [ 48 ], and two explored antibiotic persistence [ 49 , 50 ] ( S1 Fig ). There were 38 theoretical modelling studies ( S1 Fig ) capturing “drug resistance”, with four of these models exploring firstly hypothetical and then antibiotic-specific resistance ( S1 Table ).

Stage 2 Results: Heterogeneous models

We found 23 models with bacterial heterogeneity—15 bacterial population and 8 between-host models ( S2 Table ) [ 8 , 20 , 33 , 34 , 37 , 43 – 45 , 48 , 49 , 51 – 63 ]. The distribution of model aims that these papers fall into were different from Stage 1 with 13 “parameter estimation”, 8 “treatment interventions”, 1 “theoretical”, and 1 “non-treatment intervention”. 12 of the 23 models modelled the immune system.

Bacterial population models

The fifteen bacterial population models mostly captured multiple resistance classes (n = 13) ( Fig 5 and S2 Table ). One other considered a single resistance class of isoniazid only in an M . tuberculosis population and explored deterministically the impact of antibiotic exposure on resistance dominance with or without heterogeneity in fitness and mutation distributions [ 52 ]. Including heterogeneity in fitness and mutation distributions was also the most common method for exploring variation in models with multiple resistance classes. This was true both for stochastic and deterministic model structures [ 33 , 43 , 51 , 57 , 59 , 62 ], though one deterministic model only explored differences in mutation rates [ 43 ]. Four models additionally explored the impact of variation in growth rates induced by different metabolic states [ 20 , 34 , 45 , 60 ], with one model including fitness variation too [ 45 ].

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Different clearance rates were used in 2 models, a PK/PD model and a bacterial dynamics model to differentiate between two resistant bacterial strains with the aim of determining the most effective treatment combination [ 48 , 58 ].

One model did not include AMR as a direct resistance to an antibiotic, but instead as persistence [ 49 ]. This was modelled as non-replicating bacterial populations and antibiotics had little to no effect on these bacterial populations. The model implemented heterogeneity by including fast and slow-growing bacteria.

Between-host models

All eight between-host models were compartmental models. Six of these models explored the impact of including a distribution of fitness costs affecting transmission resulting from resistance-conferring mutations to prevalence of either a single [ 8 , 53 , 55 , 56 ] or multiple resistance classes [ 54 , 63 ]. Four of these six models were deterministic [ 53 , 55 , 56 , 63 ], with Knight et al. (2015) exploring a stochastic version in the supplementary materials [ 8 ]. Blower et al. (2004) explored a stochastic model that included heterogeneity by modelling strains of M . tuberculosis with different fitness rates but also cure, treatment, detection, and resistance mutation rates. The model aimed to estimate MDR-TB prevalence [ 54 ].

Two stochastic models were classified as heterogeneous as they included resistance compartments stratified with different resistant genotypes [ 44 , 61 ]. These papers had different aims: Kendall et al. [ 44 ] explored the impact of high and low levels of moxifloxacin resistance on treatment regimens and drug susceptibility testing. Pecerska et al. [ 61 ] estimated the fitness cost of MDR-TB with and without pyrazinamide resistance from a genetic data set.

Use of data derived from the literature

All Stage 2 papers used at least one parameter sourced from existing literature, so no models were entirely theoretical. Some models used a primary data set that was collected from experiments or a population study [ 20 , 49 , 52 , 58 , 59 , 61 ]. Data types used were experimental (83%), epidemiological (26%), clinical (4%), genetic (4%) and WHO data (30%). All bacterial population models used experimental data, with one paper also including clinical data [ 37 ]. Between-host models used a combination of experimental, epidemiological, and WHO data, with one using only genetic data.

Acquired or primary resistance and discrete resistance

All models with heterogeneity represented resistance as discrete categories, such as MDR/RR-TB, with no models including resistance as a spectrum. 6/8 between-host heterogenous models modelled resistance as both primary and acquired and two models had no primary resistance, with acquired resistance only [ 44 , 63 ].

Resistance effects in models

Resistance affected the ability of M . tuberculosis to transmit in 6/8 between-host heterogenous models, with resistant strains usually having a lower value for the transmission coefficient or fitness parameter than the susceptible strain.

Resistance affected disease progression in all models except Knight et al. (2015) [ 8 ]. For bacterial population models, this was defined as different growth rates. For between-host models, this was included as a separate disease progression parameter for resistant strains [ 54 , 55 , 63 ], different relapse rates for patients with resistant bacteria [ 44 ], different associated mortality rates for each resistant strain [ 61 ], variance in cross-immunity by resistant strain [ 53 ], or different natural history pathways for resistant strains [ 56 ].

13/23 models assumed resistance affected operational parameters. In nine, resistance reduced treatment efficacy [ 8 , 44 , 45 , 53 – 56 , 61 , 63 ], with one also including different diagnostic (GeneXpert rapid nucleic acid amplification test for M . tuberculosis ) sensitivity parameters for each resistant strain [ 44 ]. Four bacterial population models had a different antibiotic kill rate [ 48 , 49 , 58 , 60 ], with one including different clinical conversion factors [ 49 ].

Our review of the mathematical modelling landscape of drug resistance in mycobacteria has revealed a growing body of work mostly using transmission dynamic models to explore intervention impact. We found that a minority (33%) explore resistances other than MDR/RR-TB. Few models account for the known heterogeneity that exists in bacterial populations. Where heterogeneity was captured in both bacterial population and between-host models, it was mostly through a variation in the model-specific fitness parameter (with the definition of fitness varying broadly from being related to transmission, ability to cause disease or speed of bacterial growth).

Our Stage 1 landscape analysis found that several high MDR-TB burden countries (e.g. Pakistan, Nigeria, Ukraine, and Myanmar) are underrepresented in the English DR-TB literature. Increasing modelling of DR-TB in specific countries may aid understanding of epidemiology in the specific country and increase the global understanding of DR-TB, as well as improve estimates of intervention efficacy and hence design of context-specific interventions. This is highly relevant when considering that, as has been found for models of M . tuberculosis in general [ 11 , 64 ], most models aimed to estimate the impact of public health interventions. Transmission models were used more than any other type of model across all categories, except for the category of "treatment interventions + cost-effectiveness”, where state transition models were most used. This indicates that most modellers are interested in modelling M . tuberculosis at a between-human host population scale.

MDR-TB was the most common category of resistance modelled (67% of DR-mycobacterium models)—an expected result linked to the historical importance of this as a clinical treatment threshold and reflected in most data collection [ 1 , 3 ]. Mono-isoniazid resistance was more commonly modelled than explicit mono-rifampicin resistance, with 27 models capturing the pathway from isoniazid resistance developing into MDR-TB. XDR-TB was not considered without MDR-TB other than by two papers by Basu et al. (2008, 2009), who were interested in the burden and interventions specific to XDR-TB [ 46 , 47 ]. XDR-TB was often treated as a final state of resistance in modelling systems, with no further resistance being acquired. This reflects the historic clinical decision-making pathway (susceptible or MDR or XDR) and that XDR-TB is resistant to a large number of anti-TB antibiotics. However, there is a great variation in DR-TB and the pathways that may lead to each level of it. Understanding this variation in DR-TB will drive improvements in treatment success by identifying which antibiotics will be most effective and, therefore improve patient outcomes.

Rifampicin and isoniazid resistance were the most modelled mono-resistances, followed by pyrazinamide and ethambutol, reflecting first-line treatments and prophylaxis for TB and data availability. Testing for pyrazinamide and ethambutol resistance is typically reserved for reference settings, and there is widespread use of GeneXpert (Cepheid 6/10-colour instrument), which tests for rifampicin resistance. Only 21% of models (n = 41) captured resistances beyond these four drugs. This will need to be expanded as we move into a period with many more treatment options–constructing, parameterising, and exploring mathematical models of other antibiotic resistances is vitally needed to optimise future treatment and TB control interventions, as well as to explore evolutionary pathways. For example, we found only two papers which explicitly modelled resistance to bedaquiline [ 44 , 45 ], whilst two new treatment regimens containing bedaquiline were approved by the WHO in 2022 [ 65 ].

Models that capture non-specific DR-TB can be useful in the absence of data or to explore broad trends. We found 45 models in this category and found that these theoretical or non-specific systems were used to understand under what constraints DR-TB would dominate over DS-TB or explored the efficacy of a theoretical intervention.

When designing a model to answer a specific question such as the impact of a public health intervention, a balance needs to be struck between designing a detailed or generalised model to allow for a pragmatic approach. This pragmatism is likely the reason for our stage 2 results that revealed few models including bacterial heterogeneity. This is despite several models showing how heterogeneity in transmission fitness can affect DR-TB prevalence estimates [ 8 , 54 – 56 ]. Or how including multiple levels of resistance to one antibiotic can affect treatment outcomes [ 44 , 61 ]. Authors cannot capture all the subtlety of antibiotics as a selection pressure without including the related resistance dynamics and from this the population diversity it fosters. Mathematically, it can be difficult to include complexities in all aspects, for example, population mixing, and often there is little context-specific data on bacterial heterogeneity to inform models. However, if authors want to understand the risk of antibiotic resistance developing under a new treatment regimen it should follow that those resistances are then included in predictions. Some nuance may be beneficial in results that are only achievable with models that include bacterial heterogeneity, such as in Basu et al. (2008) where their conclusions suggested that a weaker immune response to a DR-TB infection with high fitness levels leads to higher DR-TB prevalence in HIV-positive and -negative populations [ 53 ].

Interestingly, we found that all models included resistance in a small number of discrete compartments, with no near-continuous distributions of resistance. Biologically speaking, resistance exists across a spectrum with strains having a range of minimum inhibitory concentrations, but for therapeutic and diagnostic uses they are classified with discrete values. Modelling resistance at multiple possible sub-levels would enable new research questions to be posed about pathways to evolution and competition due to multiple resistant levels. To our knowledge, such a question has not yet been asked regarding M . tuberculosis .

We found that transmission fitness levels, by contrast to resistance levels, were commonly allowed to vary across a distribution within resistant populations, likely reflecting the available historical data pointing to fitness differences between TB strains [ 66 ]. This contrasts with the lack of data linking resistant strain variation with treatment outcomes such as failure or recovery. Including such fitness effects is a relatively easy single-parameter effect within standard transmission dynamic or bacterial dynamics models and is commonly included in models of drug resistance outside of M . tuberculosis [ 7 ].

In this review, we identified 190 published papers which included drug-resistant strains of M . tuberculosis , a further 5 with a drug-resistant non-tuberculosis mycobacteria species, and 1 including both M . tuberculosis and M . marinum . Our update on the literature shows an increasing trend to model DR-TB.

The limitations of our review included that we conducted the search for English language articles when a substantial burden of DR-TB is found in non-English speaking settings such as Eastern Europe [ 1 ]. We did not capture which antibiotics were explored in the models as our focus was on the resistance captured nor time horizons for each model. Our stage 1 analysis only extracted high-level information as our main interest was the bacterial heterogeneity in stage 2. Future work could use this baseline set of literature to explore how resistance is modelled in the natural history of tuberculosis.

We encourage future modellers to consider if the bacterial component of their research question would benefit from the inclusion of bacterial heterogeneity. By not including it, models miss key features of bacterial populations, such as competition or treatment efficacy differences between strains and may, for example, under or overestimate the degree by which an intervention might increase resistance or prevalence of DR-TB.

We were unable to provide a comprehensive review of how resistance was included in Stage 1 models due to the lack of model information provided in many papers such as parameter tables, model diagrams or equations. Future mathematical models should aim for clear model reporting as suggested by the WHO [ 67 ] and Bennett et al. (2012) for transparency and to enable reproducible research [ 68 ].

In this review, we identified 195 drug-resistant mycobacteria mathematical models, with 190 DR-TB models and 23 models including bacterial heterogeneity. This has provided us with an understanding of how resistant mycobacterial species have been modelled, in terms of geographical settings, model aims and types, resistances modelled and further insights into the inclusion of bacterial heterogeneity. However, we found that bacterial heterogeneity was often ignored despite evidence of its importance at the population level. Balancing pragmaticism with biological reality when building mathematical models is vital within the fundamental evolutionary dynamics of AMR.

Supporting information

S1 text. search strings..

https://doi.org/10.1371/journal.ppat.1011574.s001

S2 Text. Details of extraction table for stage 1.

https://doi.org/10.1371/journal.ppat.1011574.s002

S3 Text. Details of extraction table for stage 2.

https://doi.org/10.1371/journal.ppat.1011574.s003

S1 Fig. Heatmap of all resistance categories in stage 1 models.

Heatmap of resistances included per DR-TB model (n = 195) indicates a lack of diversity in resistances modelled, with MDR/RR-TB featuring in over half of all 195 models. Each coloured line indicates a model (y axis) included in stage 1 (purple) or stage 2 (orange). The graph groups models into specific (captures resistance to a named antibiotic), non-specific (defined resistance that are not specific to an antibiotic) and hypothetical (captures antibiotic resistance not linked to a named drug). Antibiotic acronyms as follows: AMI = amikacin, BDQ = bedaquiline, CLR = clarithromycin, ETM = ethambutol, FQ = undefined fluroquinolone, LZD = linezolid, MOX = moxifloxacin, PA = pretomanid, PZA = pyrazinamide, STR = streptomycin, INH = isoniazid, RIF = rifampicin, MDR/RR = multidrug resistant/ rifampicin resistant, XDR = extensively drug-resistant, SLInject = second line injectable antibiotic (from WHO guidelines 2014), another 1st line = rifampicin, ethambutol, or pyrazinamide. Index links to paper number in S1 Table .

https://doi.org/10.1371/journal.ppat.1011574.s004

S2 Fig. Plot of number of publications over time.

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S1 Table. Extraction table results from stage 1.

https://doi.org/10.1371/journal.ppat.1011574.s006

S2 Table. Extraction table results from stage 2.

https://doi.org/10.1371/journal.ppat.1011574.s007

S3 Table. Geographic settings in models.

https://doi.org/10.1371/journal.ppat.1011574.s008

Acknowledgments

We would like to thank the support of library staff at the London School of Hygiene and Tropical Medicine. Thank you for the guidance and advice for this work from Quentin Leclerc and Alastair Clements.

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SYSTEMATIC REVIEW article

Effect of thoracic paravertebral nerve block on delirium in patients after video-assisted thoracoscopic surgery: a systematic review and meta-analysis of randomized controlled trials.

• Department of Anesthesiology, The Second Clinical Medical College, North Sichuan Medical College, Nanchong Central Hospital, Nanchong, China

Background: Nerve blocks are widely used in various surgeries to alleviate postoperative pain and promote recovery. However, the impact of nerve block on delirium remains contentious. This study aims to systematically evaluate the influence of Thoracic Paravertebral Nerve Block (TPVB) on the incidence of delirium in patients post Video-Assisted Thoracoscopic Surgery (VATS).

Methods: We conducted a systematic search of PubMed, Embase, Web of Science, Cochrane Library, and Scopus databases in June 2023. The search strategy combined free-text and Medical Subject Headings (MeSH) terms, including perioperative cognitive dysfunction, delirium, postoperative cognitive dysfunction, paravertebral nerve block, thoracic surgery, lung surgery, pulmonary surgery, and esophageal/esophagus surgery. We utilized a random effects model for the analysis and synthesis of effect sizes.

Results: We included a total of 9 RCTs involving 1,123 participants in our study. In VATS, TPVB significantly reduced the incidence of delirium on postoperative day three (log(OR): −0.62, 95% CI [−1.05, −0.18], p  = 0.01, I 2  = 0.00%) and postoperative day seven (log(OR): −0.94, 95% CI [−1.39, −0.49], p  < 0.001, I 2  = 0.00%). Additionally, our study indicates the effectiveness of TPVB in postoperative pain relief ( g : −0.82, 95% CI [−1.15, −0.49], p  < 0.001, I 2  = 72.60%).

Conclusion: The comprehensive results suggest that in patients undergoing VATS, TPVB significantly reduces the incidence of delirium and notably diminishes pain scores.

Systematic review registration: CRD42023435528. https://www.crd.york.ac.uk/PROSPERO .

1 Introduction

For Lung cancer serves as a prominent contributor to cancer-related fatalities across the globe, accounting for nearly a quarter of all cancer-related deaths ( 1 ). Despite recent advancements in targeted therapies, immunotherapy, and radiation treatment for lung cancer, early-stage surgical resection remains the predominant curative strategy for the vast majority of patients ( 2 ). Video-Assisted Thoracoscopic Surgery (VATS) remains the favored technique for surgical resection in early-stage lung cancer ( 3 ). Nonetheless, the rate of postoperative complications in elderly patients who undergo thoracic surgery varies from 12 to 47%, with delirium frequently observed ( 4 ). Postoperative delirium is defined as delirium occurring within one week after surgery or before the patient’s discharge from the hospital (whichever occurs earlier), meeting the diagnostic criteria for delirium as outlined in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders ( 5 ). Delirium is categorized into two types based on timing: emergence delirium and postoperative delirium ( 6 , 7 ). Additionally, delirium is divided into three subtypes based on activity levels: hypoactive delirium, hyperactive delirium, and mixed-type delirium ( 8 , 9 ).

The presence of delirium has been definitively associated with higher patient mortality rates, reduced comfort during hospital stays, prolonged discharge times, increased hospital expenses, and additional strain on both patients’ families and the healthcare system ( 10 , 11 ). Recent studies indicate that the implementation of suitable preventive measures can effectively decrease the occurrence of delirium ( 12 ), thereby improving patient comfort and the overall quality of healthcare.

Current studies suggest that delirium arises from a combination of various factors, where neuroinflammation plays a pivotal role in both its onset and progression ( 13 , 14 ). Pain is universally recognized as a leading risk factor for post-surgical delirium ( 15 ). Nerve block is believed to have the potential to decrease postoperative pain ( 16 , 17 ) and diminish the stress and inflammatory responses triggered by surgery ( 18 – 21 ), thus potentially reducing the occurrence of postoperative delirium. However, there is still an ongoing debate regarding whether nerve block can effectively lower the incidence of delirium ( 22 , 23 ). This study aims to investigate the potential impact of Thoracic Paravertebral Nerve Block (TPVB) on postoperative delirium in patients undergoing VATS.

2.1 Study design

This study rigorously adhered to the principles outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) ( 24 ) and Assessing the methodological quality of Systematic Reviews (AMSTAR) ( 25 ) guidelines. All data incorporated into our study were exclusively derived from published literature, obviating the necessity for ethical review. Furthermore, the study is registered in the International Prospective Register of Systematic Reviews (PROSPERO CRD42023435528).

2.2 Data collection

In June 2023, a comprehensive search was carried out across various databases, including PubMed, Embase, Web of Science, Cochrane Library, and Scopus. The retrieval strategy combines a mixture of free-text and MeSH terms, including perioperative neurocognitive disorders, delirium, postoperative cognitive dysfunction, paravertebral nerve block, thoracic surgery, pulmonary surgery, lung surgery, esophageal/esophagus surgery, and others ( Supplementary Appendix 1 ).

2.3 Data selection

Two independent researchers conducted a comprehensive and independent assessment and review of the literature, covering titles, abstracts, and full-text articles, to determine the final inclusion of studies in this research. Any disagreements were resolved through discussion, and in cases where a consensus could not be reached, a third researcher intervened to make the final decision.

Inclusion Criteria.

The following criteria were employed for the inclusion of studies:

1. The literature must comprise a randomized controlled trial.

2. The study subjects must be patients undergoing thoracic or pulmonary surgery.

3. Paravertebral nerve block must be administered during the perioperative period.

4. The literature must evaluate the incidence of delirium.

5. Literature from the control group must also be incorporated into the study.

Exclusion Criteria.

The following criteria were applied for the exclusion of studies:

1. Literature classified as case reports.

2. Observational studies.

3. Retrospective cohort studies.

4. Review articles.

5. Trial protocols.

6. Insufficient or unclear data within the literature.

7. Inability to access the full text or contact the authors.

2.4 Data extraction and integration

We have developed a data extraction table and conducted a preliminary test. Subsequently, two researchers independently conducted data extraction. Any discrepancies in data extraction were thoroughly discussed. In cases where the two independent researchers were unable to reach a consensus, a third researcher was consulted to make the final decision. The data extraction table comprises the following key elements: author names, publication year, study design, participant age, participant count, type of surgery, anesthesia agent types and dosages, Delirium incidence, and postoperative pain scores. These data were primarily sourced from numerical data presented in tables and figures. We employed the online tool WebPlotDigitizer (Version 4.6; WebPlotDigitizer, A. Rohatgi, Pacifica, CA, United States) to extract data presented in graphical form. We employed the equation proposed by Wan et al. to estimate the mean and standard deviation of data described with medians (interquartile range) ( 26 ).

2.5 Bias risk assessment and evidence quality grading

We employed the Cochrane Collaboration’s Risk of Bias 2 tool ( 27 ) to assess potential bias in the included studies. This comprehensive tool evaluates various aspects, including random sequence generation, allocation concealment, blinding of patients, healthcare providers, data collectors, and outcome assessors, completeness of outcome data, selective outcome reporting, and other potential sources of bias. Each article was then categorized into one of three risk levels: “low,” “some concerns,” or “high.” To evaluate the quality of evidence for each outcome, we applied the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology ( 28 ). This systematic approach allowed us to categorize the quality of evidence as either very low, low, moderate, or high.

2.6 Data analysis methods

We carried out data analysis leveraging Stata 17.0 and Review Manager 5.4 software platforms. To gauge the extent of variability among the selected studies, we employed τ 2 (Tau squared) and I 2 (I-squared) statistical metrics. These statistics were used to gauge and measure the degree of heterogeneity within the gathered data, thereby enabling a more rigorous interpretation of the findings ( 29 ). To optimally mitigate potential confounders and to more accurately mirror real-world scenarios, we opted for a random-effects model ( 30 ). Notably, when heterogeneity is notably low, outcomes from the random-effects model align with those of the fixed-effects model ( 30 ). Consequently, this study uniformly applied a random-effects model for calculating and amalgamating log-odds ratios (log(OR)) and their corresponding 95% confidence intervals (CI) in binary data, as well as in determining Hedges’s g ( g ) and their relevant 95% CI in continuous outcomes. This methodology bolsters the accuracy and dependability of our analyses.

In this study, we employed log(OR) and g as statistical measures to assess the effect size differences between the experimental group and the control group. Essentially, log(OR) measures the same relationship as the odds ratio(OR), but it provides a more stable and normally distributed estimate, making it more suitable for studies with small sample sizes. On the other hand, g is a standardized measure of mean difference that accounts for the impact of sample size, allowing it to be compared with results from other studies. Compared to the simple standardized mean difference, g adjusts for estimation bias in studies with small samples, thereby offering a more accurate measure of effect size.

To assess and scrutinize publication bias for each assessed outcome, we utilized funnel plots and conducted Egger’s test as part of our analysis ( 29 ).

3.1 Inclusion of studies

The investigators commenced their study with an initial database search, encompassing PubMed ( n  = 170), EMBASE ( n  = 217), Cochrane Library ( n  = 147), Web of Science ( n  = 149), and Scopus ( n  = 75), culminating in a total of 758 articles obtained. Subsequently, we removed 258 redundant articles. A pair of researchers screened out 436 papers based on their titles and abstracts. Subsequently, 64 articles underwent full-text assessment by the same duo, resulting in a final selection of 9 articles. For a comprehensive selection process, please consult Figure 1 .

Figure 1 . Flow diagram of study selection.

3.2 Study characteristics

In this study, a total of eight studies conducted thoracoscopic lung lobe resection under general anesthesia combined with TPVB ( 31 – 38 ), while another study performed thoracoscopic surgery under general anesthesia combined with TPVB ( 39 ). In seven of the studies, the postoperative analgesia regimen employed Patient-Controlled Intravenous Analgesia (PCIA). In the remaining two studies, the pain management approach involved either continuous thoracic epidural analgesia or PCIA. Detailed information on each study can be found in Table 1 .

Table 1 . The characteristics of included studies.

3.3 Risk of bias and evidence quality grading

A single study was categorized as posing a low risk of bias ( 35 ), whereas seven studies elicited some level of concern regarding bias risk ( 31 – 33 , 36 – 39 ), and another study was flagged for high bias risk ( 34 ), as depicted in Figure 2 . The quality assessment of the evidence is outlined in Figure 3 .

Figure 2 . Risk of bias summary: the author’s assessment of the bias risk factors in various studies.

Figure 3 . GRADE evidence summary table.

3.4 The impact of delirium after surgery within three days

Six RCTs studies ( 32 – 36 , 39 ) were included in our postoperative three-day delirium analysis. We incorporated the delirium incidence rate (1–3 days) from each study that was closest to the third day. The TPVB group exhibited a lower delirium incidence rate three days post-surgery compared to the control cohort (log(OR): −0.62, 95% CI [−1.05, −0.18], p  = 0.01, I 2  = 0.00%) ( Figure 4 ). No substantial heterogeneity was identified via Galbraith Plot evaluation ( Figure 5 ). We conducted a publication bias funnel plot ( Figure 6 ) and Egger’s test ( p  = 0.767; Supplementary Appendix 2 ), both of which did not indicate significant evidence of publication bias.

Figure 4 . The impact of TPVB on postoperative delirium three days after surgery.

Figure 5 . Galbraith plot chart at three days post-surgery.

Figure 6 . Publication bias funnel plot at three days post-surgery.

3.5 The impact of delirium after surgery within seven days

Five RCTs studies ( 31 , 32 , 34 , 37 , 38 ) were included in our postoperative seven-day delirium analysis. We included the delirium incidence rate closest to the seventh postoperative day. The delirium incidence rate at seven days postoperatively was lower in the TPVB group compared to the control group (log(OR): –0.94, 95% CI [−1.39, −0.49], p  < 0.001, I 2  = 0.00%) ( Figure 7 ). Heterogeneity assessment using the Galbraith Plot indicated low heterogeneity ( Figure 8 ). We conducted a publication bias funnel plot ( Figure 9 ) and Egger’s test ( p  = 0.247; Supplementary Appendix 3 ), both of which did not reveal significant evidence of publication bias.

Figure 7 . The impact of TPVB on postoperative delirium seven days after surgery.

Figure 8 . Galbraith plot chart at seven days post-surgery.

Figure 9 . Publication bias funnel plot at seven days post-surgery.

3.6 The impact of postoperative pain

An analysis of post-surgical first-day pain scores was conducted, omitting a study with a divergent pain assessment approach ( 39 ). Six RCTs were integrated into our evaluation ( 31 – 36 ). When performing a meta-analysis on the remaining seven studies that employed VAS pain scores, we observed that on the first day postoperatively, the TPVB group had lower pain scores compared to the control group ( g : −1.52, 95% CI [−2.87, −0.17], p  = 0.03, I 2  = 98.50%) ( Supplementary Appendix 4 ). The documented I 2 value signifies a substantial level of heterogeneity. Heterogeneity analysis was conducted using the Galbraith plot ( Figure 10 ), and the influence of individual studies on the results can be observed in Figure 11 .

Figure 10 . A Galbraith plot related to postoperative pain within a one-day timeframe is presented in the figure.

Figure 11 . The impact of individual studies on the results.

We performed a sensitivity analysis of the pain scores on the first day after surgery. In this process, we excluded the study published by Wei et al. ( 32 ) to enhance the reliability and real-world relevance of our study findings. On the first day postoperatively, the VAS pain scores in the TPVB group were significantly lower than those in the control group, and this result was statistically significant ( g : −0.87, 95% CI [−1.25, −0.48], p  < 0.001, I 2  = 77.51%) ( Figure 12 ). This suggests that the application of TPVB effectively reduces postoperative pain in patients, leading to improved comfort, enhanced surgical experience, and a smoother recovery process. Nonetheless, it is important to note that there is a relatively high level of heterogeneity among the included studies. Consequently, we should interpret these results with caution. We conducted a publication bias funnel plot and Egger’s test ( p  = 0.662), both of which did not reveal significant evidence of publication bias.

Figure 12 . After one day postoperatively, the forest plot of VAS pain scores.

4 Discussion

Our systematic review and meta-analysis clearly demonstrate that TPVB can effectively reduce the incidence of delirium in patients after VATS. According to the GRADE methodology, the quality of evidence for this conclusion is rated as high. Additionally, patients receiving TPVB also exhibit reduced postoperative pain scores. The importance of this study lies in indicating the effectiveness of TPVB in reducing the rates of delirium and pain intensity in patients undergoing VATS.

Current perioperative neurocognitive disorders (PND) include delirium (occurring within one week post-surgery or until discharge, whichever happens first), delayed neurocognitive recovery (a decline in cognitive abilities occurring within 30 days post-surgery), and postoperative neurocognitive disorder (lasting up to 12 months) ( 5 ). In our investigation, we discerned that TPVB markedly attenuates the incidence of postoperative delirium on the third and seventh days postoperatively, with low heterogeneity. This result suggests that the implementation of TPVB has a short-term impact on the occurrence of PND. However, research on the long-term effects of TPVB on neurocognitive function after the 7th day following VATS is relatively scarce. Therefore, determining the long-term impact of TPVB on neurocognitive function presents a challenge. Further investigation into the effects of TPVB on postoperative neurocognitive function is necessary. These studies should involve larger sample sizes in clinical trials, the use of standardized diagnostic criteria, long-term tracking of neurocognitive function presents after surgery, and comparisons with patients who have not undergone TPVB. Conducting such studies will be instrumental in gaining a more comprehensive understanding of the effects of TPVB on delirium and neurocognitive function. Ultimately, these investigations will furnish more precise recommendations for clinical practice. Through additional research, we can determine whether TPVB can be considered an effective strategy for reducing the occurrence of delirium and improving patients’ neurocognitive function. Furthermore, we can investigate other potential influencing factors to gain a deeper understanding of the mechanisms behind PND and explore preventive measures.

A recent meta-analysis examining the influence of perioperative peripheral nerve blocks on delirium in elderly individuals undergoing hip joint surgery, encompassing 19 randomized controlled trials with a total of 1,977 patients, indicated a decrease in the occurrence of delirium on the third postoperative day (OR: 0.59, 95% CI [0.40–0.87], p  = 0.007, I 2  = 35%). ( 40 ). However, it’s worth noting that another meta-analysis yielded contradictory results, indicating no statistically significant difference in the incidence of postoperative delirium between regional anesthesia and general anesthesia ( 41 ). But, this study is subject to considerable heterogeneity influenced by multiple factors. Additionally, there are substantial inconsistencies in both the statistical outcomes and the definitions used. Consequently, drawing a definitive conclusion from this study proves to be challenging.

In our study, we found that TPVB can significantly reduce postoperative pain scores. Postoperative pain is considered one of the significant risk factors for delirium, and this may be associated with the observed decrease in delirium rates on the third postoperative day. At the same time, current research suggests that postoperative opioid use is also one of the risk factors for the occurrence of delirium ( 42 ). Considering that TPVB efficiently diminishes postoperative pain levels, it may contribute to a lower delirium rates by minimizing the need for opioid analgesics ( 43 , 44 ). Nonetheless, it’s worth mentioning that in this study, at the seven-day postoperative time point, patients in the TPVB group also demonstrated a significant decrease in delirium rates. This could be attributed to the ability of TPVB to mitigate perioperative and postoperative stress responses and inflammatory reactions induced by surgery, consequently reducing the incidence of delirium.

In our meta-analysis of pain scores, it is essential to take note that Wei et al. ( 39 ) utilized the FLACC scale for pain evaluation, while other studies employed the Visual Analog Scale (VAS) for pain assessment. As a result, we decided to exclude the study conducted by Wei et al. ( 39 ) from our analysis. Nevertheless, it is noteworthy that the study conducted by Wei et al. ( 39 ) also suggested a tendency towards lower pain scores (OR: 0.55, 95% CI [−0.04, 1.14], p  = 0.065). Simultaneously, the heterogeneity analysis of pain scores on postoperative day one, as depicted in the Galbraith plot ( Figure 10 ), and the influence of individual studies on the outcomes ( Figure 11 ), clearly demonstrates the substantial heterogeneity evident in the study by Wei et al. published in 2022 ( 32 ). This heterogeneity has had a notable impact on the experimental results, potentially introducing inherent bias into the study findings. Consequently, during sensitivity analysis, the aforementioned study was excluded.

Our investigation does face certain limitations. Primarily, due to variations in the timing of delirium diagnosis across the included studies, we extracted delirium incidence rates closest to the third and seventh post-surgical days, incorporating them into the analysis. This approach may inevitably introduce some potential bias. Secondly, multiple diagnostic tools are currently at our disposal for diagnosing delirium, including the Confusion Assessment Method (CAM), the Richmond Agitation-Sedation Scale (RASS), the Memorial Delirium Assessment Scale (MDAS), the Nursing Delirium Screening Scale (Nu-DESC), the Mini-Mental State Examination (MMSE), and the Montreal Cognitive Assessment (MOCA), among others ( 45 , 46 ). Nonetheless, there’s currently no uniform standard for diagnosing delirium. The research we incorporated utilized different methods for delirium diagnosis, including Nu-DESC, 3D-CAM, MoCA, MMSE, and one study did not explicitly specify its diagnostic approach. These variations in diagnostic methods may potentially influence the study results. Moreover, concerning the implementation of TPVB, most research favored ropivacaine as the primary agent, with a few opted for a combination of lidocaine. Notably, inconsistencies in dosage, volume, concentration, and injection site among these studies. These disparities could potentially affect the effectiveness of TPVB and create variances in the study outcomes.

In summary, this study indicates that TPVB has a positive short-term impact on reducing the incidence of delirium among patients undergoing VATS, with high-quality GRADE evidence supporting this conclusion. However, contradictions remain, and further research is necessary to fully understand TPVB’s effects on neurocognitive function and to refine postoperative management strategies. Such studies are essential for enhancing recovery support for surgical patients and ensuring a more comprehensive approach to their care.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding author.

Author contributions

XZ: Writing – review & editing, Writing – original draft. WM: Writing – review & editing, Writing – original draft. LZ: Writing – review & editing, Writing – original draft. HZ: Writing – review & editing, Writing – original draft. LC: Writing – review & editing, Writing – original draft. YX: Writing – review & editing, Writing – original draft. LL: Writing – review & editing, Writing – original draft.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

The authors would like to thank all the authors who participated in this study for their contributions to this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur.2024.1347991/full#supplementary-material

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Keywords: delirium, thoracic paravertebral nerve block, thoracic surgery thoracoscopy, thoracoscope, postoperative analgesia, meta-analysis

Citation: Zhou X, Mao W, Zhao L, Zhu H, Chen L, Xie Y and Li L (2024) Effect of thoracic paravertebral nerve block on delirium in patients after video-assisted thoracoscopic surgery: a systematic review and meta-analysis of randomized controlled trials. Front. Neurol . 15:1347991. doi: 10.3389/fneur.2024.1347991

Received: 15 January 2024; Accepted: 28 March 2024; Published: 10 April 2024.

Reviewed by:

Copyright © 2024 Zhou, Mao, Zhao, Zhu, Chen, Xie and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Linji Li, [email protected]

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.