Efficiency and comparability of using new evidence platforms for updating recommendations: experience with a diabetes guideline in Colombia

doi:10.21203/rs.3.rs-3338837/v1

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Efficiency and comparability of using new evidence platforms for updating recommendations: experience with a diabetes guideline in Colombia

https://doi.org/10.21203/rs.3.rs-3338837/v1

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Background: Updating recommendations of guidelines requires both comprehensive and efficient literature searches. While new information platforms are available for developing groups, their relative contribution to this purpose remains uncertain.

Methods: As part of a review/update of 8 selected evidence-based recommendations (EBR) on type 2 diabetes, we evaluated the following five literature search approaches (targeting systematic reviews, using predetermined criteria): PubMed for MEDLINE; Epistemonikos database basic search; Epistemonikos database, using a structured search strategy; Living overview of evidence (L.OVE) platform, and TRIP database. Three reviewers independently classified retrieved references as definitely or probably eligible/not eligible. Those falling in the same “definitely” categories for all reviewers were labelled as “true” positives/negatives. The rest went to re-assessment and if found eligible/not eligible by consensus became “false” negatives/positives, respectively. We described the yield for each approach, computed their “diagnostic accuracy” measures and agreement statistics.

Results: Altogether, the five approaches identified 318-505 references for the 8 recommendations, from which reviewers considered 4.2-9.4% eligible after the two rounds. While Pubmed outperformed the other approaches (diagnostic odds ratio 12.5 versus 2.6-5.3), no single search approach returned eligible references for all recommendations. Individually, searches identified up to 40% of all eligible references (n=71), and no combination of any three approaches could identify over 80% of them. Kappa statistics for retrieval between searches were very poor (9 out of 10 paired comparisons did not surpass chance-expected agreement).

Conclusion: Among the information platforms assessed, Pubmed seemed the more efficient to update this set of recommendations. However, the very poor agreement in the reference yield demands developing groups to add information from several (probably over 3) sources for this purpose. Further research is needed to replicate our findings and enhance our understanding of how to update recommendations efficiently.

Evidence-based recommendations

guidelines update

literature search

clinical practice guidelines.

Clinical practice guidelines (CPG) are increasingly being used as a knowledge translation tool to guide clinical decisions and optimize patient care (1, 2). CPGs typically include several statements with evidence- based recommendations (EBR) coming from systematic reviews (SR) of the relevant literature. This body of evidence is the basis to build a consensus process that incorporates stakeholders (patients, healthcare practitioners, policy makers) with their judgments on its applicability to specific situations (accessibility, economic impact, and patient preferences) (3–5). Along with the ever-changing supporting evidence, CPGs need regular updates to maintain the validity and relevance of their recommendations (6). As new evidence has the potential to change EBRs, periodic updates are desirable, and especially welcome when certainty behind the existing EBR is low.

Keeping CPGs to date is, however, costly, and time-consuming. Shekelle et al. (6) showed that most CPGs remain valid for a period of 3.6 years. A SR by Vernooji et al. (2) included 35 methodological handbooks and showed that the most common periods proposed for updating CPGs are two to three years. Although several guideline development manuals acknowledge the importance of updating CPGs, they fail in providing specific strategies to do so. The authors also point the lack of information for literature searching, evidence selection and synthesis and other aspects related to the updating process.

The Guidelines International Network (G-I-N) provides key aspects for updating CPGs, such as the inclusion of an expiration date in the description of the process that the guideline groups will use to update recommendations (2, 3). Identifying new relevant evidence begins by building search strategies to perform a SR of evidence (4, 7). Updating approaches are currently seeking to incorporate new technologies and software tools to make the process more efficient, dynamic, and interactive (7, 8). However, how these new approaches perform remains uncertain.

In this context, we aimed to evaluate the relative contribution (in terms of yield, efficiency, and agreement) of five different search approaches to identify new relevant evidence to update EBRs from Colombian CPGs. This assessment refers to a group of recommendations from the type 2 Diabetes Mellitus (T2DM) Colombian CPG, 2016 (9), and the American Diabetes Association (ADA) guideline, 2020 (10). This project is part of non-communicable diseases (NCDs) clinical practice guidelines implementation project in Colombia (11).

We selected eight recommendations included in the T2DM Colombian CPG (2016)(9) and ADA Guidelines (2020) (10), that included questions on prognosis or treatment (see details of the EBRs in supplementary appendix A). Our updating process used five different search approaches, as follows: (A) MEDLINE through PubMed (12), (B) The Epistemonikos database(13) using simple search, (C) The Epistemonikos database, using advanced search, (D) The Living Overview of Evidence (L.OVE) platform(14) and (E) The TRIP database (15).

The authors designed the search approaches based on conventional methods found in CPGs and the incorporation of new technological approaches (the supplementary appendices B and C describes the characteristics of all search approaches). The common goal for our search approaches was to identify primarily SR potentially answering the question related with the selected recommendations. That is, no individual studies were eligible for the update if at least one of the approaches identified one or more SR relevant to the question.

The search strategies were generated and approved by the investigators using the same search terms (as much as possible) for all databases. In brief, we accessed MEDLINE via PubMed following a restrictive search strategy entering the minimum number of Medical Subject Headings (MeSH) terms and keywords required, based on the search strategies published in the original CPG, plus a (narrow) filter for SR identification. For the Epistemonikos (basic) search, we performed a broad search strategy using only population and intervention terms. For the Epistemonikos advanced search, we performed an advanced search using the terms employed in the PubMed search and the filter given by the database for the identification of SR. The L.OVE platform uses artificial intelligence to retrieve references included in the correspondent PICO-specific T2DM-L.OVE platform. This platform allows adding terms to the PICO questions that are created automatically, theoretically making search strategies more specific. We added terms that were used in the other approaches as well. Finally, for the TRIP database we performed an advanced search using the pre-established terms, and we used the SR filter given by the database.

Study selection process:

Search results were exported to the software RAYYAN QCRI (16) and reviewed independently by three researchers applying predefined selection criteria. Assessors were to follow the elements of each PICO question to identify those potentially relevant references for each search approach.

Assessors of retrieved references judged every document as definitely, or probably eligible/not eligible. Based on their decisions, those reaching consensus (3 out of 3 assessors agreeing) in either of the “definitely” categories were left as final classification. Those references judged in the “probably” categories for at least one referee went for a second round of revision, seeking to re-classify them until getting to a new consensus. For the purpose of analysis (see below) based on diagnostic jargon, the references considered as definitely eligible/not eligible by all assessors in the first round were labelled as “true” positives/negatives. Those undergoing re-classification in the second round and becoming definitely eligible/not eligible were labelled as “false” negatives/positives, respectively.

Data analysis:

We conducted four types of data analyses. Firstly, we described the counts and proportions of references falling into each category through our classification process (i.e., retrieved references, “true positives”, “going to a second round of discussion”, etc.). Secondly, we formed 2x2 tables for each search approach based on the labels of diagnostic jargon and computed usual measures of diagnostic accuracy (sensitivity, specificity, likelihood ratios and diagnostic odds ratios) along with their 95% confidence intervals (CI). Thirdly, we described the absolute and relative contribution of the search approaches to find relevant references, both individually and as accumulative yield when adding approaches. Finally, we computed agreement statistics including chance-corrected Kappa coefficient along with 95% (CI). The analyses of diagnostic performance were run in Meta-DiSc, a freeware to perform Meta-analysis of diagnostic studies (Clinical Biostatistics Unit of the Ramon y Cajal Research Institute (IRYCIS). Hospital Ramón y Cajal, Madrid, España)(17) and all other analyses in Stata version 15 (18).

We updated searches for a total of eight recommendations using five different approaches. Four of the recommendations were taken from the Colombian CPGs (9) and four from ADA guidelines (10). As shown in the supplementary appendix A, five of the recommendations were about treatment, two about diagnosis of complications and one about follow-up. Most recommendations were of moderate to high certainty of evidence, but the recommendation about follow-up had the lowest certainty of evidence (E level in the ADA guideline).

All five approaches required a similar number of steps (see supplementary Appendix B), from entering the search terms or designing the search strategy to download and export the selected references. Although two of the approaches exhibit some characteristics such as lack of time filter or manual selection of references, which can make the search process less efficient. For approach C, since it was not necessary to design and run searches, but simply find the L.OVE based on the PICO question, it required less effort. Although approach E had a search feature based on the PICO question, it did not allow the exporting of files and therefore all the strategies had to be performed using the advanced features.

Retrieval process across search approaches for the eight recommendations of interest (Fig. 1).

Despite using the same terms and keywords in the searches, the approaches greatly varied in the number of articles retrieved. While approaches A, B and E yielded a similar number of references (407, 473 and 444 respectively), approach C (Epistemonikos, advanced) returned the lowest number (n = 318) of references and approach D (L.OVE) the highest (n = 505). As expected, most (78.7–90.1%) references were judged as definitely not eligible in the first round, but a range of 8.9–18.2% were left for further reclassification. After the selection performed by the three reviewers in the two rounds the total number of relevant references identified ranged from 20 to 31 (5.6 to 9.4% of all retrieved). Out of 136 references considered eligible at the end of the process (some of them repeated in two or more recommendations), two-thirds (91) were added in the second round of discussion among reviewers.

Diagnostic performance of the search approaches (Fig. 2).

Approach A (Pubmed) showed the highest sensitivity (50% with the others ranging from 24–33%) and the second-best specificity (93%, with the others ranging from 87–94%). Pubmed outperformed the other four search approaches in terms of its positive Likelihood ratio (6.73 compared with 2.23–4.19 of the other approaches), negative likelihood ratio (not shown in the Fig. 0.54, compared with 0.77–0.85 of the others) and, as a result, its diagnostic odds ratio (12.5 and 2.6–5.3, respectively).

Distribution of the references found eligible by the reviewers.

Table 1 shows the references retrieved by our five search approaches by recommendation and in total. The approaches retrieved 20–31 references for the 8 recommendations, but recommendation 1 accounted for 39% of all eligible references. Although Pubmed showed better “diagnostic performance” among the approaches, the approach with higher retrieval was (D) L.OVE, with 31 of 82 documents of interest (71 single references, as some documents were eligible for more than one recommendation). No search approach returned at least one reference for each of the 8 recommendations of interest. Individually, the approaches contributed with a relatively small portion (from 24–37%) of the eligible references. Likewise, the proportion of references provided solely each approach was relatively large (5–12 of the eligible references, that is 25–39% of their individual contribution).

Figure 3 shows the accumulative contribution of the eligible references (adding the 8 recommendations) provided by the 5 approaches, when the search sequence started by one or the other. It is noticeable that in none of the search sequences the eligible references reached an (arbitrarily, minimal) 80% threshold (n = 66), even when combining any 3 approaches. The relative contribution of (A) Pubmed seemed the higher among all approaches. As a result, to reach a goal of retrieving at least 80% of the eligible references, four search approaches should be added, only when Pubmed was included in the search.

Figure 4 shows the accumulative contribution of the searches for all recommendations, when starting with Pubmed. Despite the variation of the yield for the recommendations, there is consistency in the need to add search approaches to reach a substantial number of the eligible references. Although Pubmed identified roughly half of the references for most recommendations, only in one of the eight situations (EBR7) was sufficient as search approach. Moreover, in two recommendations (EBR 5 and 8) Pubmed did not return references of interest.

Agreement between search approaches ( Table 2 )

This table shows the number of eligible references (n = 71) in agreement as found/not found (positive/negative), the proportion of observed/expected (by chance) agreement and the Kappa statistics for the ten possible pairs of search approaches. In 9 of the cases, the agreement statistics did not surpass that found by chance.

This study addressing the performance of newly available search approaches to update recommendations shows that identification of a good breadth of relevant studies (SR in this case) continues to be a challenge. Our data indicates that feeding the review of evidence with a comprehensive set of literature still needs contributions from several information platforms. We found that Pubmed searches, somehow the conventional comparator, tended to be the more efficient in terms of diagnostic accuracy (a higher proportion of relevant references included, with relatively fewer studies needing re-classification) compared with the other search approaches. But despite this advantage, Pubmed as search approach (as well as the others) contributed with a relatively small proportion of references and sometimes (two out of eight recommendations) did not contribute with references for the recommendations being updated. This is the result of a very poor agreement in the references retrieved by the search approaches under evaluation, despite a strategy to include the same (or the most similar possible) terms.

Our approach aimed primarily at identifying SR in the field of T2D, a disease conveying a high burden and intensively researched. While all information platforms under assessment identified potentially relevant references for the updating process, there were substantially more literature in support of treatment recommendations relative to other aspects. This indicates that more SR are being produced around the effect of interventions, compared with other types of SR (19). No matter the information platform, its individual contribution was very modest (none over 40% of all eligible references) and no single search approach provided at least one reference for each recommendation. While Pubmed, the more established and comprehensive search engine had a poor agreement with the other approaches, this is also true between the new emerging information platforms. The only approaches with agreement over chance-expected were Epistemonikos, basic search and L.OVE platform (Cohen’s Kappa 0.37, 95%CI 0.14–0.60), probably because the domain of the L.OVE platform corresponds to Epistemonikos (13, 14). The poor agreement for retrieving eligible references across search approaches was also evident by showing that no combination of 3 searches reached even 80% of the relevant documents. In fact, to reach such threshold, 4 search approaches were necessary, only when Pubmed was one of them.

Conceptually, all five approaches assessed required the same steps: 1) design of the search strategy, 2) use of filters such as time or study type (SR), 3) selection of references, and 4) download and exporting files. However, those integrating the use of new technologies such as the L.OVE platform might require less effort in designing the search strategy, using de PICO format. Also, in our exercise PubMed the approach that requires more elaboration for the search terms, was more efficient in retrieving relevant references (DOR 12.5, 95% IC 5.5–28.1). The yield of references varied substantially both in general (range of 319 to 505, with difference of more than 100 references) and for those found eligible (20–31). We saw that finding eligible less than 10% (range 4.2 to 9.4) of the retrieved references was not surprising, as all strategies are oriented to comprehensiveness to ensure all relevant references can be identified.

In this study we choose the consensus among three reviewers as “gold standard”. The search strategies were designed by the whole research group, seeking to identify all potentially relevant references but starting from a consensus in the terms and studies targeted by the searches. This should have set a common ground for reviewers and familiarity with the studies of interest for the update. The perspective of being inclusive gave the chance to all references pointed as “probably eligible” to go to a second round of review. This may have increased the number of references re-assessed (8.9–18.2% in all search approaches) reducing efficiency but ensuring specificity (87–94%) and inclusiveness (two-thirds of the eligible references came from the second round of review). This perspective guiding the selection process also explained the relatively low sensitivity of the searches (24–50%). We believe that consensus, coming from a collective assessment with no pre-determined hierarchies among reviewers may be a good approach in general, and in our case highlighted the importance of their consensus in the selection process.

Our findings in context

Other authors have evaluated different approaches to identify key references to update recommendations or to guide patient care. Garcia et al. 2015 (22), evaluated three different strategies to identify key updated references, including an exhaustive approach (exhaustive literature search strategies for each clinical question), a restrictive approach using the minimum amount of MeSH terms and text words, and a PLUS approach using the PLUS database. They found that the proposed method using a restrictive search strategy using some features of the PubMed for MEDLINE such as the Clinical Queries filters can be a feasible and efficient method to identify new studies that could trigger the potential update of a recommendation. In line with our results, the approach that included PubMed for MEDLINE performed well in terms of identifying relevant references and its ability to determine negative articles.

Shariff et al. 2013 (23) compared the performance of searches in PubMed and Google Scholar, aiming to find relevant references to guide patient care, not to update of a specific recommendation. They found that quick clinical searches in Google Scholar returned twice as many relevant articles as PubMed and provided greater access to free – full text articles. We did not consider Google Scholar in any of our approaches; however, this platform resembles the use of an approach such as Epistemonikos, basic search, that did not require the design of any search strategy and still provided an important number of references (24).

In regard to combining platforms, other authors have reported that a composite approach such as a combination of MEDLINE and Epistemonikos (complemented by reference checking of included studies) is the best combination to identify SR on health-related topics (20). Rathbone et al. 2016 (21), assessed seven different databases to identify relevant SR of interventions for hypertension and found that despite the scope of many databases, a wider search including several databases should be considered, a suggestion also supported by our results.

Implications

In a rapidly evolving world, where new evidence may rapidly change recommendations for clinical practice, it is increasingly challenging keeping guidelines and implementation process up to date. There is a growing need for efficient approaches, so recommendations change timely as reviewing a CPG as whole is a resource consuming task. A first step may be identifying which recommendations require update, perhaps prioritizing those based in a low quality of evidence. Our findings suggests that several information platforms should be used, Pubmed being one of them. This study also highlights the relevance of having a collective assessment and seeking the consensus/discussion after the first selection of the articles to improve the completeness in the selection process. Getting the assessors involved from the beginning, formulating the PICO questions, and co-designing the search approaches should also keep a consistent selection. Some new information platforms seem appealing and less time-consuming by reducing the need to refine the search strategy. Other features of the databases should be considered, such as the ability to download and export the results easily and to access the advanced features without a fee. For the time being, these tools should be used in combination to ensure full representation in the task of updating recommendations. The vigorous wave of artificial intelligence and all its emerging possibilities should, at some point in the future, make a positive impact to reduce the time for new evidence to become an updated recommendation. More research is needed to monitor the expected changes in the information platforms, so these findings, somewhat discouraging can be refuted and simpler search approaches can easily identify most relevant documents for future updates.

Limitations

Our study is, to the best of our knowledge, the first study that assessed five different approaches including those supported by artificial intelligence algorithms. The searches were designed by this particular team and on a specific topic, and to SR (rather than primary studies) and it is implied that different search strategies will lead to different results. Also, our searches were limited to a relatively small number of PICO questions on one topic, leading to relatively small number of references (with little statistical precision). The validity of the eligibility based on the criteria of the reviewers may be no reproducible. was beyond the scope of this article. This study did not present information about the agreement among reviewers (there was an accident with the process of saving data in Rayyan). While this would have been an important information (e.g., whether the three reviewers performed similarly, or the need to keep the 3 reviewers for the selection), the focus was the agreement between searches. Regarding the databases evaluated, the L.OVE platform was still completing the reference classification process. Therefore, several references included under each PICO set may have not been specifically related to that question.

In this study to update recommendations, of five search approaches evaluated, no single database or combination contribute substantially to retrieve most of the eligible references. Agreement across search approaches to identify relevant references was poor. PubMed outperformed the other 4 search approaches in terms of efficiency, and probably should be part of any update exercise, along with at least two additional databases to maintain efficiency. Further research is needed to replicate our findings and enhance our understanding of how to update recommendations efficiently.

CPG- Clinical Practice Guideline

EBR- Evidence Based Recommendation

SR- Systematic Review

GIN- Guidelines International Network

T2DM- Type 2 Diabetes Mellitus

ADA- American Diabetes Association

NCDs- Non-communicable diseases

L.OVE- Living Overview of Evidence

Ethics approval and consent to participate: not applicable

Consent for publication: not applicable

Availability of data and materials: the complete datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests: the authors declare that they have no competing interest

Funding: this study is part of the research project “Estudio de impacto de estrategias de información para modificar conocimientos, actitudes y prácticas en enfermedades crónicas en Bogotá” BPIN code: 2016000100037, known as “Vector Salud Bogotá” supported by local government funding (Sistema General de Regalías).

Authors' contributions: study concept and design (LAT, JCV), data collection and analysis (LAT, EB, LAP, AM, AMB, JCV), drafting of the manuscript (JCV, LAT, LAP). All authors read and approved the final manuscript.

Acknowledgements: we thank Maria Ximena Rojas for their input and enthusiasm to conduct this study, and Lina Goez for her support during the data collection process.

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Tables 1 and 2 are available in the Supplementary Files section.

No competing interests reported.

Tablesupdatingrecommendations08092023.pptx
Supplementaryfileupdatingrecommendations11092023.docx
Supplementary File. Supplementary Appendix A. Evidence based recommendations (EBRs) in type 2 diabetes mellitus. Supplementary Appendix B. Characteristics of five approaches to update EBRs. Supplementary Appendix C. Details of the search strategies for each evidence-based recommendation (EBR) and approaches.

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Journal Publication

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Efficiency and comparability of using new evidence platforms for updating recommendations: experience with a diabetes guideline in Colombia

Status:

Journal Publication

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Abstract

Figures

Background

Methods

Study selection process:

Data analysis:

Results

Discussion

Our findings in context

Implications

Limitations

Conclusions

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Journal Publication

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