Journal of Clinical Epidemiology最新文献

{"title":"The definition of predictor and outcome variables in mortality prediction models: a scoping review and quality of reporting study","authors":"Eline G.M. Cox ,&nbsp;Daniek A.M. Meijs ,&nbsp;Laure Wynants ,&nbsp;Jan-Willem E.M. Sels ,&nbsp;Jacqueline Koeze ,&nbsp;Frederik Keus ,&nbsp;Bianca Bos - van Dongen ,&nbsp;Iwan C.C. van der Horst ,&nbsp;Bas C.T. van Bussel","doi":"10.1016/j.jclinepi.2024.111605","DOIUrl":"10.1016/j.jclinepi.2024.111605","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Mortality prediction models are promising tools for guiding clinical decision-making and resource allocation in intensive care units (ICUs). Clearly specified predictor and outcome variables are necessary to enable external validation and safe clinical application of prediction models. The objective of this study was to identify the predictor and outcome variables used in different mortality prediction models in the ICU and investigate their reporting.</div></div><div><h3>Methods</h3><div>For this scoping review, MEDLINE, EMBASE, Web of Science, and the Cochrane Central Register of Controlled Trials were searched. Studies developed within a general ICU population reporting on prediction models with mortality as a primary or secondary outcome were eligible. The selection criteria were adopted from a review by Keuning et al. Predictor and outcome variables, variable characteristics (defined as units, definitions, moments of measurement, and methods of measurement), and publication details (defined as first author, year of publication and title) were extracted from the included studies. Predictor and outcome variable categories were demographics, chronic disease, care logistics, acute diagnosis, clinical examination and physiological derangement, laboratory assessment, additional diagnostics, support and therapy, risk scores, and (mortality) outcomes.</div></div><div><h3>Results</h3><div>A total of 56 mortality prediction models, containing 204 unique predictor and outcome variables, were included. The predictor variables most frequently included in the models were age (40 times), admission type (27 times), and mechanical ventilation (21 times). We observed that single variables were measured with different units, according to different definitions, at a different moment, and with a different method of measurement in different studies. The reporting of the unit was mostly complete (98% overall, 95% in the laboratory assessment category), whereas the definition of the variable (74% overall, 63% in the chronic disease category) and method of measurement (70% overall, 34% in the demographics category) were most often lacking.</div></div><div><h3>Conclusion</h3><div>Accurate and transparent reporting of predictor and outcome variables is paramount to enhance reproducibility, model performance in different contexts, and validity. Since unclarity about the required input data may introduce bias and thereby affect model performance, this study advocates that prognostic ICU models can be improved by transparent and clear reporting of predictor and outcome variables and their characteristics.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111605"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Key challenges in epidemiology: embracing open science","authors":"Edward Xu ,&nbsp;Anna Catharina V. Armond ,&nbsp;David Moher ,&nbsp;Kelly Cobey","doi":"10.1016/j.jclinepi.2024.111618","DOIUrl":"10.1016/j.jclinepi.2024.111618","url":null,"abstract":"<div><div>Open science is a movement that fosters research transparency, reproducibility, and equity. Open science has been put forward by numerous stakeholders in the research ecosystem as a key science policy goal, with the United Nations Educational, Scientific, and Cultural Organization creating recommendations on open science and aligning these with UN Sustainability Goals. Open science practices are not standard to epidemiology despite their potential value to the field and especially during disease outbreaks. This article highlights core open science practices, including study registration, open data, code, material, use of reporting guideline, open access publishing, and preprints. It aims to provide readers with the fundamentals about open science, relevant international policy for open science, and the value of implementing open science for epidemiology and society as a whole. It is a practical piece that will provide readers with a starting point to expand their understanding of open science and to identify tools to learn more. The article also highlights the challenges of open science in its implementation and the importance of monitoring open science practices.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111618"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing feature importance biases in machine learning models for early diagnosis of type 1 Gaucher disease","authors":"Yoshiyasu Takefuji","doi":"10.1016/j.jclinepi.2024.111619","DOIUrl":"10.1016/j.jclinepi.2024.111619","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111619"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editors’ Choice: February 2025","authors":"David Tovey,&nbsp;Andrea C. Tricco","doi":"10.1016/j.jclinepi.2025.111681","DOIUrl":"10.1016/j.jclinepi.2025.111681","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111681"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A methodological review identified several options for utilizing registries for randomized controlled trials","authors":"Luisa Urban ,&nbsp;Nina Haller ,&nbsp;Dawid Pieper ,&nbsp;Tim Mathes","doi":"10.1016/j.jclinepi.2024.111614","DOIUrl":"10.1016/j.jclinepi.2024.111614","url":null,"abstract":"<div><h3>Objectives</h3><div>Registry-based randomized controlled trials (RRCTs) can provide internally valid results in a real-world context at relatively low effort and cost. However, the main characteristics, the extent to which the registry is utilized (eg, proportion of data from registry) and registry-related limitations are not well characterized. This methodological review of RRCTs aims to analyze the trial design features, investigate potential usage options, and identify possible limitations of using registry data for randomized controlled trials (RCTs).</div></div><div><h3>Study Design and Setting</h3><div>A systematic search in PubMed for ongoing and published RRCTs was conducted up to February 2, 2023. Studies that reported at least one outcome derived from a registry were included. Study selection was independently performed by two reviewers. All data were extracted into a standardized table, and descriptive statistics were generated.</div></div><div><h3>Results</h3><div>We included 162 RRCTs (41 protocols and 121 studies). Most RRCTs were multicenter trials (<em>n</em> = 127; 78.4%) comprising a large number of participants (median = 1787; range = 41 to 683,927) and a long follow-up period (median = 60 months; range = 1 to 367 months) with a minimal loss to follow-up. The inclusion criteria of participants were mostly broadly defined. Types of interventions ranged from surgical procedures to behavioral interventions, and almost half of the interventions (46.9%) had a preventive purpose. The main registry outcome was mostly a clinical endpoint (40.1%) or a composite endpoint of major clinical events (30.9%) that was objectively measurable. We found different degrees of registry utilization, ranging from the exclusive use of long-term monitoring of previously published data to the more comprehensive registry utilization for patient recruitment, endpoint collection, and long-term follow-up. Limitations related to the use of registry data comprised potential coding errors or incomplete data (eg, due to under-recording of mild cases). In addition, technical challenges must be considered (eg, failed linkages or time-delayed data entry).</div></div><div><h3>Conclusion</h3><div>A broad spectrum of potential usage options and usage extent of registry data exist. Our analysis suggests that in many cases, the potential of using registry data and thus their benefits were not fully utilized. In addition, the study illustrates that there is not a single, unified methodology for designing RRCTs but that registries can support RCTs in various ways. Therefore, future RRCTs should specify for what purposes and to what extent registries were utilized. Moreover, a clear definition and taxonomy of RRCTs appears necessary for facilitating future dialogue and research on RRCTs.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111614"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sociodemographic bias in clinical machine learning models: a scoping review of algorithmic bias instances and mechanisms","authors":"Michael Colacci ,&nbsp;Yu Qing Huang ,&nbsp;Gemma Postill ,&nbsp;Pavel Zhelnov ,&nbsp;Orna Fennelly ,&nbsp;Amol Verma ,&nbsp;Sharon Straus ,&nbsp;Andrea C. Tricco","doi":"10.1016/j.jclinepi.2024.111606","DOIUrl":"10.1016/j.jclinepi.2024.111606","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Clinical machine learning (ML) technologies can sometimes be biased and their use could exacerbate health disparities. The extent to which bias is present, the groups who most frequently experience bias, and the mechanism through which bias is introduced in clinical ML applications is not well described. The objective of this study was to examine instances of bias in clinical ML models. We identified the sociodemographic subgroups PROGRESS that experienced bias and the reported mechanisms of bias introduction.</div></div><div><h3>Methods</h3><div>We searched MEDLINE, EMBASE, PsycINFO, and Web of Science for all studies that evaluated bias on sociodemographic factors within ML algorithms created for the purpose of facilitating clinical care. The scoping review was conducted according to the Joanna Briggs Institute guide and reported using the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) extension for scoping reviews.</div></div><div><h3>Results</h3><div>We identified 6448 articles, of which 760 reported on a clinical ML model and 91 (12.0%) completed a bias evaluation and met all inclusion criteria. Most studies evaluated a single sociodemographic factor (<em>n</em> = 56, 61.5%). The most frequently evaluated sociodemographic factor was race (<em>n</em> = 59, 64.8%), followed by sex/gender (<em>n</em> = 41, 45.1%), and age (<em>n</em> = 24, 26.4%), with one study (1.1%) evaluating intersectional factors. Of all studies, 74.7% (<em>n</em> = 68) reported that bias was present, 18.7% (<em>n</em> = 17) reported bias was not present, and 6.6% (<em>n</em> = 6) did not state whether bias was present. When present, 87% of studies reported bias against groups with socioeconomic disadvantage.</div></div><div><h3>Conclusion</h3><div>Most ML algorithms that were evaluated for bias demonstrated bias on sociodemographic factors. Furthermore, most bias evaluations concentrated on race, sex/gender, and age, while other sociodemographic factors and their intersection were infrequently assessed. Given potential health equity implications, bias assessments should be completed for all clinical ML models.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111606"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient-reported outcomes and measures are under-utilised in advanced therapy medicinal products trials for orphan conditions","authors":"Andrada Ciuca ,&nbsp;Siddharth Banka ,&nbsp;Tara Clancy ,&nbsp;Simon Jones ,&nbsp;Jamie J. Kirkham ,&nbsp;William G. Newman ,&nbsp;Katherine Payne ,&nbsp;Ramona Moldovan","doi":"10.1016/j.jclinepi.2024.111617","DOIUrl":"10.1016/j.jclinepi.2024.111617","url":null,"abstract":"<div><h3>Objectives</h3><div>Advanced therapy medicinal products (ATMPs) are medicines based on genes, tissues, or cells and can include gene therapy, somatic-cell therapy, and tissue-engineered medicines. Patient-reported outcomes (PROs) are reports on health and well-being that come directly from the individual without external interpretation. Patient-reported outcome measures (PROMs) are questionnaires aimed at assessing the individual and subjective experience with health and other psychosocial aspects. The aim of the present review is to assess the extent and quality of PROs and PROMs used in orphan ATMP trials.</div></div><div><h3>Study Design and Setting</h3><div>The database from National Health Service Special Pharmacy Service horizon scanning was searched on 27 March 2024 to identify all ATMPs for orphan conditions. Clinical trial protocols were included in this review if they investigated ATMPs for orphan conditions and were published in clinical trial databases.</div></div><div><h3>Results</h3><div>A total of 100 trials were included. These accounted for 64 conditions. Only 37% (37/100) of the trials included PROs. Overall, 17 different types of PROs were identified across the trials. Quality of life (QoL) and health-related quality of life (HRQoL) were the most frequent PROs found in 18% (18/100) and 13% (13/100) of the trials, respectively. A total of 33 PROMs were identified. Of these, 57% (19/33) were HRQoL (89% [17/19]) or QoL (11% [2/19]) measures. Of the HRQoL measures identified, 71% (12/17) were disease specific and 29% (5/17) were generic. Of the non-QoL PROMs, 29% (4/14) were designed to measure pain and 71% (10/14) PROMs focused on other psychological outcomes, including anxiety and depression.</div></div><div><h3>Conclusion</h3><div>Our results show that only 37% of the orphan ATMP trials include patient-reported outcomes and measures. This highlights the urgent need for relevant PROs/PROMs that capture benefits and harms and assimilation of existing PROMs for better comparison between or within conditions. It is essential to include and reflect the patients' experience so that those intended to benefit from the research have the opportunity to influence its direction.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111617"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time adaptive randomization of clinical trials","authors":"Gui Liberali ,&nbsp;Eric Boersma ,&nbsp;Hester Lingsma ,&nbsp;Jasper Brugts ,&nbsp;Diederik Dippel ,&nbsp;Jan Tijssen ,&nbsp;John Hauser","doi":"10.1016/j.jclinepi.2024.111612","DOIUrl":"10.1016/j.jclinepi.2024.111612","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objectives&lt;/h3&gt;&lt;div&gt;To evaluate real-time (day-to-day) adaptation of randomized controlled trials (RCTs) with delayed endpoints – a “forward-looking optimal-experimentation” form of response-adaptive randomization. To identify the implied tradeoffs between lowered mortality, CIs, statistical power, potential arm misidentification, and endpoint rate change during the trial.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Study Design and Setting&lt;/h3&gt;&lt;div&gt;Using data from RCTs in acute myocardial infarction (30,732 patients in the Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Coronary Arteries, GUSTO-1) and coronary heart disease (12,218 patients in the EURopean trial On reduction of cardiac events with Perindopril in stable coronary Artery disease, EUROPA), we resample treatment-arm assignments and expected endpoints to simulate (1) real-time assignment, (2) forward-looking assignments adapted after observing a fixed number of patients (“blocks”), and (3) a variant that balances RCT and real-time assignments. Blinded real-time adaptive randomizations (RTARs) adjust day-to-day arm assignments by optimizing the tradeoff between assigning the (likely) best treatment and learning about endpoint rates for future assignments.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Despite delays in endpoints, real-time assignment quickly learns which arm is superior. In the simulations, by the end of the trials, real-time assignment allocated more patients to the superior arm and fewer patients to the inferior arm(s) resulting in less mortality over the course of the trial. Endpoint rates and odds ratios were well within (resampling) CIs of the RCTs, but with tighter CIs on the superior arm and less-tight CIs on the inferior arm(s) and the odds ratios. The variant and patient-block-based adaptation each provides intermediate levels of benefits and costs. When endpoint rates change within a trial, real-time assignment improves estimation of the end-of-trial superior-arm endpoint rates, but exaggerates differences relative to inferior arms. Unlike most response-adaptive randomizations, real-time assignment automatically adjusts to reduce biases when real changes are larger.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;Real-time assignment improves patient outcomes within the trial and narrows the CI for the superior arm. Benefits are balanced with wider CIs on inferior arms and odds ratios. Forward-looking variants provide intermediate benefits and costs. In no simulations, was an inferior arm identified as statistically superior.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Plain Language Summary&lt;/h3&gt;&lt;div&gt;Randomized controlled trials (RCT) are the gold standard in clinical trials — typically half of the patients are assigned to a new drug or procedure and the other half to a placebo (or the current best option). Typically, half of the patients might get an inferior drug or treatment. We explore a method, real-time adaptive randomization (RTAR), that uses information observed up to the t","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"178 ","pages":"Article 111612"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142669987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Results reporting for clinical trials led by medical universities and university hospitals in the nordic countries was often missing or delayed","authors":"Gustav Nilsonne ,&nbsp;Susanne Wieschowski ,&nbsp;Nicholas J. DeVito ,&nbsp;Maia Salholz-Hillel ,&nbsp;Love Ahnström ,&nbsp;Till Bruckner ,&nbsp;Katarzyna Klas ,&nbsp;Tarik Suljic ,&nbsp;Samruddhi Yerunkar ,&nbsp;Natasha Olsson ,&nbsp;Carolina Cruz ,&nbsp;Karolina Strzebonska ,&nbsp;Lars Småbrekke ,&nbsp;Mateusz T. Wasylewski ,&nbsp;Johan Bengtsson ,&nbsp;Martin Ringsten ,&nbsp;Aminul Schuster ,&nbsp;Tomasz Krawczyk ,&nbsp;Themistoklis Paraskevas ,&nbsp;Eero Raittio ,&nbsp;Cathrine Axfors","doi":"10.1016/j.jclinepi.2025.111710","DOIUrl":"10.1016/j.jclinepi.2025.111710","url":null,"abstract":"<div><h3>Objectives</h3><div>To systematically evaluate timely reporting of clinical trial results at medical universities and university hospitals in the Nordic countries.</div></div><div><h3>Study Design and Setting</h3><div>In this cross-sectional study, we included trials (regardless of intervention) registered in the European Union (EU) Clinical Trials Registry and/or ClinicalTrials.gov, completed 2016–2019 and led by a university with medical faculty or university hospital in Denmark, Finland, Iceland, Norway, or Sweden. We identified summary results posted at the trial registries and conducted systematic manual searches for results publications (eg, journal articles, preprints). We present proportions with 95% confidence intervals (CI) and medians with interquartile range (IQR). Protocol: <span><span>https://osf.io/wua3r</span><svg><path></path></svg></span>.</div></div><div><h3>Results</h3><div>Among 2112 included clinical trials, 1650 (78.1%, 95% CI 76.3%–79.8%) reported any results during our follow-up; 1097 (51.9%, 95% CI 49.8%-54.1%) reported any results within 2 years of the global completion date; and 48 (2.3%, 95% CI 1.7%–3.0%) posted summary results in the registry within 1 year. The median time from global completion date to results reporting was 690 days (IQR 1103). 856/1681 (50.9%) of ClinicalTrials.gov registrations were prospective. Denmark contributed approximately half of all trials. Reporting performance varied widely between institutions.</div></div><div><h3>Conclusion</h3><div>Missing and delayed results reporting of academically led clinical trials are a pervasive problem in the Nordic countries. We relied on trial registry information, which can be incomplete. Institutions, funders, and policymakers need to support trial teams, ensure regulation adherence, and secure trial reporting before results are permanently lost.</div></div><div><h3>Plain Language Summary</h3><div>Reporting of results from clinical trials is necessary for evidence-based clinical decision-making. We followed up reporting of clinical trials in the Nordic countries sponsored by medical universities and university hospitals. Of 2112 studies completed 2016–2019 in two major trials registries, about half reported results in any form within 24 months, and more than one in five did not report results at all. These results show that there is a need for improvement in the reporting of Nordic clinical trials.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"181 ","pages":"Article 111710"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scoping review indicates heterogeneous methods for developing and integrating patient decision aids in the context of clinical practice guidelines","authors":"Lena Fischer ,&nbsp;Leon Vincent Schewe ,&nbsp;Fülöp Scheibler ,&nbsp;Rahel Wollny ,&nbsp;Corinna Schaefer ,&nbsp;Torsten Karge ,&nbsp;Thomas Langer ,&nbsp;Jan Berghold ,&nbsp;Ivan D. Florez ,&nbsp;Andrew Hutchinson ,&nbsp;Sheyu Li ,&nbsp;Marta Maes-Carballo ,&nbsp;Zachary Munn ,&nbsp;Lilisbeth Perestelo-Perez ,&nbsp;Livia Puljak ,&nbsp;Anne Stiggelbout ,&nbsp;Dawid Pieper","doi":"10.1016/j.jclinepi.2025.111708","DOIUrl":"10.1016/j.jclinepi.2025.111708","url":null,"abstract":"<div><h3>Objectives</h3><div>To review the methods used to develop and integrate patient decision aids (PDAs) based on the recommendations of clinical practice guidelines (CPGs).</div></div><div><h3>Study Design and Setting</h3><div>We conducted a scoping review covering bibliographic databases (PubMed, Embase; searched until December 2023), gray literature, references, and expert consultations to identify eligible documents. Documents published from 2000 onwards and describing methods related to guideline-based PDA development or linking CPGs and PDAs were included. Two reviewers independently selected and analyzed the documents. Results were synthesized and presented narratively.</div></div><div><h3>Results</h3><div>Based on 24 included documents, we categorized their methods into 4 topics. For topic (1), the selection of CPG recommendations for which PDAs are (most) needed, we found a total of 14 selection factors across <em>n</em> = 11 documents, with uncertainty/variability in patient preferences and trade-offs between options being the most frequently mentioned. Topic (2) (<em>n</em> = 24) covers methods for developing and/or updating guideline-based PDAs, such as forming a multidisciplinary development group, using CPGs and their evidence summaries along with other sources as the evidence base, and using digital solutions for semi-automated development and updating. Topic (3) (<em>n</em> = 12) covers methods for PDA quality assessment and/or user testing, such as finalizing and approving the PDAs after a review and feedback process from the CPG group and an iterative user testing process. Topic (4) (<em>n</em> = 20) covers methods for linking CPGs and PDAs, often through digital strategies.</div></div><div><h3>Conclusion</h3><div>We identified heterogeneous methods for developing and integrating PDAs based on CPG recommendations. Empirical testing is required to determine the most useful and practically feasible (combination of) methods. CPG organizations should focus on establishing adequate methods for linking CPG and PDA development to foster shared decision-making.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"181 ","pages":"Article 111708"},"PeriodicalIF":7.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}