Journal of Clinical Epidemiology最新文献

{"title":"Analysis of opinions from studies citing use of COMET outcomes taxonomy supports it use in health research: descriptive study.","authors":"Theo Christodoulou, Paula R Williamson, Susanna Dodd","doi":"10.1016/j.jclinepi.2025.111917","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.111917","url":null,"abstract":"<p><strong>Objective: </strong>In order to support use of the COMET taxonomy for health outcomes, its suitability and applicability must be assessed more broadly beyond that of its early piloting phases. Demonstration of its suitability in practice would provide further support for its use in aiding the development of core outcome sets, systematic reviews and searching in online resources, therefore aiding knowledge dissemination.</p><p><strong>Study design and setting: </strong>A citation analysis identified published studies where the taxonomy had been applied. Analysis of these publications aimed to understand the type of publication, clinical area, reason for taxonomy use or adaptation, and any comments made by researchers who had applied the taxonomy.</p><p><strong>Results: </strong>Of 315 papers identified, 200 were sampled and 193 publications relating to 184 projects were analysed. Nearly one third (58, 30%) of publications were related to the development of core outcome sets and half (98, 51%) were related to the development of reviews (systematic, scoping and literature). In two thirds (123, 67%) of the projects the taxonomy was applied for the classification of health outcomes and the vast majority (117, 95%) of these did so without making any changes.</p><p><strong>Conclusion: </strong>This research confirms the taxonomy is sufficiently comprehensive and granular for the classification of all patient outcomes in health research. Its application can highlight a lack of attention being paid towards outcomes most important to patients. We encourage the adoption of this classification system to facilitate evidence searching.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111917"},"PeriodicalIF":5.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144776831","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":"Combining multiple imputation with internal model validation in clinical prediction modeling: a systematic methodological review.","authors":"Sinclair Awounvo, Meinhard Kieser, Manuel Feißt","doi":"10.1016/j.jclinepi.2025.111916","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.111916","url":null,"abstract":"<p><strong>Objective: </strong>We aim to investigate how multiple imputation (MI) is combined with internal model validation (IMV) in clinical prediction modeling (CPM) studies, with particular emphasis on the challenges of balancing predictive performance, methodological complexity, and resource demands.</p><p><strong>Study design and setting: </strong>We searched PubMed, Web of Science, and MathSciNet for \"multiple imputation\" and \"validation\" and reviewed all CPM articles published until December 2023. Studies were categorized based on whether MI was performed prior to IMV (MI-prior-IMV) or during IMV (MI-during-IMV). Moreover, strategy choice was described in terms of key study parameters.</p><p><strong>Results: </strong>Out of 683 publications screened, 108 were included in the final analysis. MI-prior-IMV was applied in 85% of them. MI-during-IMV studies had larger sample sizes (2,005 vs. 1,212) and higher missing rates (30% vs. 28.5%) than MI-prior-IMV studies. The MI methods used were MICE in 77 (92%) and MCMC in 7 (8%) of 84 studies. IMV ⟳MI studies exclusively used MCMC for MI. MI-during-IMV studies performed fewer imputations compared to MI-prior-IMV studies (10 vs. 15). Moreover, MI-during-IMV studies mostly opted for sample-split (50%) followed by cross-validation (31%), and bootstrap (19%) as IMV methods. In contrast, MI-during-IMV studies mostly applied bootstrap (63%) followed by cross-validation (24%) and sample-split (13%).</p><p><strong>Conclusion: </strong>MI-prior-IMV is predominantly applied over MI-during-IMV, probably due to its relative simplicity regarding comprehension and implementation. MI-during-IMV studies involve larger sample sizes and higher missing rates, potentially explaining their conservativeness regarding runtime and complexity. Future studies should systematically evaluate the complexity-benefits trade-offs of different strategies, offering clearer guidance on optimal strategies for various settings.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111916"},"PeriodicalIF":5.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144776832","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":"Capturing the influx of living systematic reviews: a systematic methodological survey.","authors":"Clareece R Nevill, Amin Sharifan, Aoife O'Mahony, Hadiqa Tahir, Will Robinson, Urvi Modha, Lara A Kahale, Assem M Khamis, Elie A Akl, Ellesha A Smith, Alex J Sutton, Suzanne C Freeman, Nicola J Cooper","doi":"10.1016/j.jclinepi.2025.111904","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.111904","url":null,"abstract":"<p><strong>Objective: </strong>Living systematic reviews (LSRs) are an emerging type of review that continuously updates as new evidence becomes available. A previous methodological survey conducted in 2021 identified and studied all health-based LSRs. Since then, the landscape has changed, including the on-going accumulation of COVID-19 research and availability of automation tools. Furthermore, various methods and guidance exist for conducting LSRs and review authors are often encouraged to explore opportunities to maximise dissemination. We conducted a LSR survey update to describe LSRs in a 'post-COVID' era. Our objectives were to summarise the uptake of LSRs, describe their characteristics, including methodological and communicative characteristics, and identify patterns in LSR attributes.</p><p><strong>Study design and setting: </strong>We systematically searched for new LSRs and any updates - including updates from LSRs identified previously - published between May 2021 - March 2023 in any health field. Eligible articles were identified and data extracted and combined with data from the original survey. Outcomes broadly included LSR characteristics and uptake, and methodological and communicative characteristics. Analyses were descriptive and included visualisations to explore distributions, combinations, and any time effects of characteristics.</p><p><strong>Results: </strong>A total of 549 records across 168 individual LSRs were identified (of which 92 LSRs were newly detected). Whilst the presence of COVID-19 LSRs dominated in later years, there was an increased uptake in non-COVID-19 LSRs; the former were found to search the evidence and update/publish results more frequently. Where reported, the approach to conducting updates varied considerably, including a wide range of pre-specified frequencies and/or triggers. Of the 337 updates, 25.5% reported on ongoing studies, and among LSRs with published results, 58.5% utilised the GRADE system. The proportion of LSRs with a centralised platform for sharing results was higher amongst i) those that included updates, ii) Cochrane reviews, iii) non-COVID-19 LSRs, and iv) funded LSRs. Few LSRs included interactive features.</p><p><strong>Conclusion: </strong>The number of LSRs is growing at an accelerating rate, but this survey illustrates that there are still methodological limitations and challenges that carefully need addressing. Key areas for improvement include more explicit pre-specified updating strategies and better use of web-based platforms for disseminating results.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111904"},"PeriodicalIF":7.3,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144719108","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":"Lack of Methodological Rigor and Limited Coverage of Generative AI in Existing AI Reporting Guidelines: A Scoping Review.","authors":"Xufei Luo, Bingyi Wang, Qianling Shi, Zijun Wang, Honghao Lai, Hui Liu, Yishan Qin, Fengxian Chen, Xuping Song, Long Ge, Lu Zhang, Zhaoxiang Bian, Yaolong Chen","doi":"10.1016/j.jclinepi.2025.111903","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.111903","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to systematically map the development methods, scope, and limitations of existing artificial intelligence (AI) reporting guidelines in medicine and to explore their applicability to generative AI (GAI) tools, such as large language models (LLMs).</p><p><strong>Study design and setting: </strong>We reported a scoping review adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). Five information sources were searched, including MEDLINE (via PubMed), EQUATOR Network, CNKI, FAIRsharing, and Google Scholar, from inception to December 31, 2024. Two reviewers independently screened records and extracted data using a predefined Excel template. Data included guideline characteristics (e.g., development methods, target audience, AI domain), adherence to EQUATOR Network recommendations, and consensus methodologies. Discrepancies were resolved by a third reviewer.</p><p><strong>Results: </strong>68 AI reporting guidelines were included. 48.5% focused on general AI, while only 7.4% addressed GAI/LLMs. Methodological rigor was limited: 39.7% described development processes, 42.6% involved multidisciplinary experts, and 33.8% followed EQUATOR recommendations. Significant overlap existed, particularly in medical imaging (20.6% of guidelines). GAI-specific guidelines (14.7%) lacked comprehensive coverage and methodological transparency.</p><p><strong>Conclusion: </strong>Existing AI reporting guidelines in medicine have suboptimal methodological rigor, redundancy, and insufficient coverage of GAI applications. Future and updated guidelines should prioritize standardized development processes, multidisciplinary collaboration, and expanded focus on emerging AI technologies like LLMs.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111903"},"PeriodicalIF":7.3,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676414","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":"External validation, impact assessment and clinical utilization of clinical prediction models: a prospective cohort study.","authors":"Banafsheh Arshi, Laura Elizabeth Cowley, Eline Rijnhart, Kelly Reeve, Luc J Smits, Laure Wynants","doi":"10.1016/j.jclinepi.2025.111902","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.111902","url":null,"abstract":"<p><strong>Objectives: </strong>We aimed to assess paths taken by clinical prediction models (CPM) after development by quantifying external validation, impact assessment, and utilization in clinical practice.</p><p><strong>Study design and setting: </strong>We followed a random sample of 109 regression-based CPM development articles published between 1995-2020 by performing a forward citation search. We estimated five- and ten-year probabilities of validation and impact assessment after development of CPMs using Kaplan-Meier analysis. In addition, we conducted a survey among the authors of the development articles to determine whether the CPMs had been used in clinical settings.</p><p><strong>Results: </strong>Eighteen (17%) CPM development articles reported a CPM that was externally validated after development. Five- and ten-year probabilities of validation were 0.13 (0.06 to 0.19) and 0.16 (0.08 to 0.23), respectively. Only one article had a CPM with impact assessment during follow-up (ten-year probability: 0.01 [0 to 0.04]). Among the 34 (31%) articles with a survey response, 17 (50%) had CPMs that had been used in clinical practice, in a median of 5 sites (IQR: 1-347). Of these models, only 4 (24%) were externally validated, and none had undergone impact assessment.</p><p><strong>Conclusion: </strong>Despite evidence of utilization in clinical settings, few models are externally validated after development, and published impact assessment is scarce. To prevent compromising patient safety, it is crucial to intensify efforts to promote external validation and impact assessment of prediction models.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111902"},"PeriodicalIF":7.3,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660952","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":"Improving the Reporting and Use of Trial Results in Clinical Trials Registries: Global Practices, Barriers, and Recommendations.","authors":"Tabea Kaul, Johanna Aa Damen, Anna Lene Seidler, Melina Willson, Ghassan Karam, Demy Idema, Mike Kusters, Mary Ann Dowsett, Tala Ibrahim Hasan Abutahoun, Lotty Hooft, Kylie E Hunter","doi":"10.1016/j.jclinepi.2025.111901","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.111901","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background and objectives: &lt;/strong&gt;Recent initiatives promoted results reporting on clinical trials registries to improve transparency and reduce publication bias. However, local reports suggest that results reporting on registries is often inadequate, limiting their usefulness for evidence synthesis. We aimed to i) provide an overview of results reporting practices across clinical trials registries globally, ii) identify barriers and facilitators to reporting and using results from trials registries, and iii) develop recommendations to improve reporting and usability of results in trials registries.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Study design and setting: &lt;/strong&gt;Three-part mixed methods study. Part 1: Descriptive analysis of results reporting practices for randomised controlled trials (RCTs) starting between 2010 and 2022 across six trials registries (one from each WHO region), with an in-depth analysis focusing on reporting formats and accessibility. Part 2: Two separate online surveys targeting trial registrants and evidence users. Part 3: Discussion amongst author group to generate recommendations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Part 1: Our sample included 201,265 RCTs, with 17% (33,163 trials) reporting some form of results on a registry. A subset showed 63% of posted results accessible in the registry record, with 64 to 98% of results data available in a reusable format. Part 2: 86% (194/225) of registrants were aware of registry results reporting possibilities, but time, effort, and fear of publishing interference were barriers. For evidence users, 51% (36/70) had used registry results, with barriers including mistrust of non-peer-reviewed data and difficulty locating results. Part 3: Recommendations include standardizing registry interfaces, addressing misconceptions, and fostering trust in registry-reported results.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Results reporting practices on registries are increasing. Improving these requires better infrastructure, policies, training, and funding. With adequate support, registries can become essential for transparent and efficient evidence dissemination, enhancing research quality, and reducing duplication.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Plain language summary: &lt;/strong&gt;Clinical trials registries are online databases where researchers register medical studies and share their status, details, and results. These registries exist globally, and allow researchers to track ongoing studies and emerging evidence on a topic. They also enable the public to identify trials they may be interested to participate in. Although regulations require researchers to share study results on registries within a year of study completion, only a fraction of results are currently available. Our project 1) evaluated the reporting of study results across different trials registries, 2) surveyed individuals involved in reporting and using study findings from registries, and 3) developed recommendations to improve availability and usefulness of t","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111901"},"PeriodicalIF":7.3,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651152","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":"Assessment of traditional and novel effect measures for time-to-event endpoints: a meta-epidemiological study of published oncological trials","authors":"Qiao Huang ,&nbsp;Rong Peng ,&nbsp;Rui-Qing Cai ,&nbsp;Yong-Bo Wang ,&nbsp;Si-Yu Yan ,&nbsp;Xiang-Ying Ren ,&nbsp;Xian-Tao Zeng ,&nbsp;Ying-Hui Jin","doi":"10.1016/j.jclinepi.2025.111900","DOIUrl":"10.1016/j.jclinepi.2025.111900","url":null,"abstract":"<div><h3>Objectives</h3><div>Time-to-event endpoints are essential for evaluating treatment efficacy in oncology trials. The hazard ratio, although commonly used, captures only the relative effect and may not suffice in diverse clinical contexts. Traditional measures based on incidence rates and restricted mean survival time, along with novel measures based on average hazard (AH), offer both relative (ratio-based) and absolute (difference-based) perspectives. However, these measures have not been systematically evaluated in real-world oncology trials, limiting their practical application.</div></div><div><h3>Methods</h3><div>This meta-epidemiological study analyzed individual patient data reconstructed from Kaplan–Meier curves of 46 randomized controlled oncology trials published in five high-impact journals, involving 35,994 patients and 52 curves. The reconstruction used a validated algorithm to build patient-level data from published curves. Seven effect measures were evaluated: hazard ratio, incidence rate ratio and difference, restricted mean survival time ratio and difference, and AH ratio and difference. Pairwise concordance among these measures was assessed using visualizations, median differences, Spearman rank correlation, and intraclass correlation coefficients.</div></div><div><h3>Results</h3><div>There was a high agreement in clinical direction and statistical significance among the 7 measures. Among the four ratio-based (relative) measures, hazard ratio, incidence rate ratio, and ratio of AH demonstrated high agreement, with median differences ≤0.004, correlations &gt;0.96, and intraclass correlation coefficients &gt;0.87. The restricted mean survival time ratio showed substantial inconsistency, with other relative measures being approximately 1.25 times higher. The nonproportionality of hazards further increased this ratio to 1.38. Among the three difference-based (absolute) measures, incidence rate difference and AH difference were closely aligned, whereas the restricted mean survival time difference exhibited substantial variability, with median ratios of treatment effect magnitude exceeding 300.</div></div><div><h3>Conclusion</h3><div>AH–based measures provide promising alternatives to the hazard ratio by incorporating both relative and absolute perspectives. The restricted mean survival time based measures offer clinically relevant insights but exhibited substantial differences in magnitude; therefore, they should be interpreted independently and not directly compared with other measures. Incidence rate–based measures may serve as practical approximations when other metrics are unavailable. Routine reporting of multiple effect measures in oncology trials can enhance clinical interpretation and support more nuanced, evidence-based decision-making.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"186 ","pages":"Article 111900"},"PeriodicalIF":5.2,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144621116","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":"Review of methods to deal with the misalignment of times of eligibility, start of follow up and treatment assignment in studies explicitly aimed at emulating target trials.","authors":"Matthew McIntyre, Sally Yaacoub, Elodie Perrodeau, Raphael Porcher, Viet-Thi Tran","doi":"10.1016/j.jclinepi.2025.111898","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.111898","url":null,"abstract":"<p><strong>Objectives: </strong>Real world evidence based on observational data from cohorts, registries and healthcare databases are increasingly used to assess the effectiveness of therapeutic interventions. One challenge when analyzing observational data are risks of biases due to misalignment of times of eligibility, start of follow-up, and treatment assignment. We aimed to describe the methods used to generate alignment or to account for non-alignment in these studies; and to estimate the proportion of studies for which a low-cost change would limit the risk of biases associated with misalignment of the times.</p><p><strong>Study design and setting: </strong>We analyzed 199 studies explicitly aiming at emulating a target trial identified in a previous systematic review from Hansford et al. Two reviewers extracted the times of eligibility, start of follow-up, and treatment assignment and the methods used by authors to generate alignment or to account for misalignment of time points.</p><p><strong>Results: </strong>Out of the 199 studies, 181 (91%) reported the times of eligibility, start of follow-up, and treatment assignment. All time points were aligned for 93/181 (51%), with 73 using no specific method, 18 emulating a sequence of target trials and 2 using other methods to generate alignment. In contrast, 88/181 (49%) studies had misalignment of time points, of which 29 used a method to correct for misalignment during analysis (24 studies used a cloning, censoring and weighting approach, 4 randomly allocated patients with early events, and 1 randomly allocated all participants to the study groups with subsequent censoring when they deviated from the randomly allocated intervention). Out of 59/88 (67%) studies that did not use any method to address non-alignment, 46/59 (78%) could have applied low-cost changes to account for misalignment.</p><p><strong>Conclusion: </strong>Approximately half of the studies explicitly aiming to emulate a target trial included had alignment of times of eligibility, start of follow-up, and treatment assignment, either by design or using a method generating alignment. Among studies with misalignment, about 67% did not account for it in the analysis while 78% could have applied low-cost changes to reduce bias.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111898"},"PeriodicalIF":7.3,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144610189","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":"Re: living recommendations do not 'flip-flop' - Examining the probability of directional changes to recommendations in living guidelines.","authors":"Dheeraj Sharma","doi":"10.1016/j.jclinepi.2025.111899","DOIUrl":"10.1016/j.jclinepi.2025.111899","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111899"},"PeriodicalIF":7.3,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602175","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":"Anchor-based predictive modeling and receiver operating characteristic curve estimates of patient acceptable symptom state for the forgotten joint score in total knee arthroplasty patients stratified by age and gender","authors":"Glory Uche Abugu ,&nbsp;Nicholas Holloway ,&nbsp;Philip Riches ,&nbsp;Jon Clarke ,&nbsp;Mario Ettore Giardini ,&nbsp;Swati Chopra","doi":"10.1016/j.jclinepi.2025.111897","DOIUrl":"10.1016/j.jclinepi.2025.111897","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to estimate patient acceptable symptom state (PASS) thresholds in the Forgotten Joint Score (FJS) 1 year following primary total knee arthroplasty (PTKA) while investigating the impact of patients’ characteristics on PASS thresholds.</div></div><div><h3>Study Design and Setting</h3><div>This cohort study used data from patients who underwent PTKA at a public hospital in Scotland between April 2021 and December 2022. Assessment of FJS (0-100, high-low knee awareness) was completed 1 year postoperatively. A single-item question about satisfaction with the operated knee was completed at 1 year and served as the anchor for estimating PASS thresholds. Anchor-based predictive modeling (adjusted and unadjusted) and receiver operating characteristic (ROC) curve methods were used to determine PASS thresholds. The impact of patient characteristics on PASS threshold values was investigated by calculating stratified PASS values based on gender and age groups.</div></div><div><h3>Results</h3><div>A total of 1832 PTKAs were performed between April 2021 and December 2022, of which 1359 (74%) had complete data comprising the study cohort. The median age and body mass index of patients included in the study were 70 years and 31.2 <span><math><mrow><msup><mrow><mtext>kg</mtext><mo>/</mo><mi>m</mi></mrow><mn>2</mn></msup></mrow></math></span>, respectively, with 54% being females. The proportion of satisfied patients was 84%. A moderate positive correlation between FJS and patient satisfaction was found (<em>r</em> = 0.64, <em>P</em> &lt; .001), which supports the validity of the external anchor. PASS thresholds for the entire cohort were 31 (ROC method) and ∼33 points (predictive modeling method). Larger PASS values were found for male patients and patients aged ≥70 years compared to their female and younger counterparts. The adjusted predictive modeling estimate was 15.3; given that the data do not meet the assumption of normal distribution, we consider this threshold might be biased and must be interpreted with circumspection.</div></div><div><h3>Conclusion</h3><div>A postoperative FJS of ≥33 points can be used as a reference guide to evaluate successful achievement of a “forgotten joint” in a Scottish population. Patients’ characteristics impact PASS estimates and should be considered when interpreting outcome scores.</div></div><div><h3>Plain Language Summary</h3><div>After knee replacement surgery, patients want their new knee to feel natural. The FJS measures how much a person notices their artificial knee in daily life. The score goes from 0 to 100, with higher numbers meaning the knee feels more natural. We studied data from over 1300 patients in Scotland using modern statistical methods. Our results show that most patients need a score of at least 33 to feel satisfied with their knee 1 year after surgery, while men and people aged ≥70 years seem to need higher scores.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"185 ","pages":"Article 111897"},"PeriodicalIF":7.3,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602174","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}