Journal of Clinical Epidemiology最新文献

{"title":"Development and Validation of Case-Ascertainment Algorithms for Hypertensive Disorders of Pregnancy Using Longitudinal Electronic Health Records Data.","authors":"Yeyi Zhu, Emily Z Wang, Amanda N Ngo, Mara B Greenberg, Assiamira Ferrara","doi":"10.1016/j.jclinepi.2025.112007","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112007","url":null,"abstract":"<p><strong>Objectives: </strong>Hypertensive disorders of pregnancy (HDP), including chronic hypertension, gestational hypertension, and preeclampsia/eclampsia, is a leading cause of maternal and perinatal morbidity. Accurate identification of individual HDP subtypes in electronic health records (EHRs) is critical for research and surveillance but remains a challenge. We aimed to develop and validate EHR-based case-ascertainment algorithms for individual HDP conditions using medical chart review.</p><p><strong>Study design and setting: </strong>We conducted a validation study within the Blood Pressure in Pregnancy, Obesity, Diabetes and Perinatal Outcomes (BIPOD) cohort at Kaiser Permanente Northern California, comprising 441,147 singleton pregnancies from 2011 to 2021. Using a stratified sampling approach, we selected 980 pregnancies for medical chart review: 200 chronic hypertension, 280 gestational hypertension, 300 preeclampsia/eclampsia, and 200 normotensive pregnancies. Following the American College of Obstetricians and Gynecologists diagnosis criteria, we developed HDP case-ascertainment algorithms incorporating clinician diagnosis codes, antihypertensive medications, systolic/diastolic blood pressure, and laboratory test results. Normotension was defined as not meeting HDP definitions throughout pregnancy. Positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity were calculated, with weighting to account for sampling design. Minimum validity thresholds were set as 80% PPV, 90% NPV, 80% sensitivity, and 90% specificity.</p><p><strong>Results: </strong>Algorithms for chronic and gestational hypertension demonstrated high diagnostic validity across all definitions, with all performance statistics exceeding the minimum thresholds. All definitions were retained in the final algorithms for chronic hypertension [weighted PPV: 87.0% (95% CI 86.4%-87.6%), NPV: 99.5% (99.5%-99.5%); sensitivity: 84.9% (84.2%-85.5%); specificity 99.6% (99.6%-99.6%)] and gestational hypertension [weighted PPV 91.4% (91.1%-91.7%); NPV: 99.5% (99.5%-99.5%); sensitivity: 94.0% (93.7%-94.2%); specificity: 99.3% (99.2%-99.3%)]. For preeclampsia/eclampsia, only the definition using inpatient diagnosis had acceptable validity (PPV: 94.9%), while definitions using outpatient diagnoses or laboratory results had poor PPV (0.0%-8.0%). Weighted performance for the preeclampsia/eclampsia final algorithm using inpatient diagnosis was high: PPV 94.9% (94.5%-95.2%); NPV 99.5% (99.5%-99.5%); sensitivity 88.8% (88.3%-89.3%); specificity 99.8% (99.8%-99.8%). Similarly, normotensive had high validation performance: PPV 99.5% (99.5%-99.5%); NPV 91.4% (91.2%-91.7%); sensitivity 98.6% (98.6%-98.7%); specificity 96.7% (96.5%-96.8%).</p><p><strong>Conclusion: </strong>EHR-based case-ascertainment algorithms for HDP demonstrated high validity in a large, diverse population. These algorithms can facilitate accurate HDP phenotyping in population-based studi","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112007"},"PeriodicalIF":5.2,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145259969","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":"Continuous predicted risks should be retained when deploying clinical prediction models.","authors":"Robin Blythe, Rex Parsons, Marcus E H Ong, Adrian Barnett","doi":"10.1016/j.jclinepi.2025.112009","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112009","url":null,"abstract":"<p><strong>Objective: </strong>Clinical prediction models are used to obtain predicted risks of a diagnosis or future event. While models can produce continuous predicted probabilities, these are often dichotomised or categorised into risk groups using probability thresholds for the sake of operational convenience. Thresholds or risk groups may be required, but discarding the continuous probability is not, and risks throwing away information that can be useful for prioritising patients. The economic value of continuous risk prediction estimates is not well understood.</p><p><strong>Study design and setting: </strong>We simulated the impact of ranking patients by predicted risks compared to using risk groups alone when faced with resource constraints at varying levels of model discrimination and event prevalence. We evaluated model performance in terms of positive predictive value, sensitivity, and mean rank of true positives under different levels of model calibration. We then applied our findings to a machine learning-based ordinal scoring system using real data from a large tertiary Singaporean emergency department.</p><p><strong>Results: </strong>Using predicted probabilities to rank patients by predicted risk led to model performance benefits when compared to risk groups alone. The benefits of avoiding outcome dichotomisation increased as model discrimination and outcome prevalence increased, and ranking was robust to poor model calibration. Repeating this analysis on Singaporean emergency department data showed that benefits of ranking were greatest when resource constraints were highest.</p><p><strong>Conclusion: </strong>Using continuous probabilities to prioritise patients within risk groups shows the potential for economic benefits. Future prediction models should share equations for deriving continuous risk scores, and deployed models should consider using these scores together with clinical judgement for patient prioritisation.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112009"},"PeriodicalIF":5.2,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253527","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":"Reporting guidelines can be used to foster reporting of evidence-based research principles: A cross-sectional study.","authors":"Tea Kabić, Marija Šimundić Munitić, Ishanka Weerasekara, Malgorzata M Bala, Joanna Zajac, Matthias Briel, Dawid Pieper, Livia Puljak","doi":"10.1016/j.jclinepi.2025.112008","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112008","url":null,"abstract":"<p><strong>Objectives: </strong>The purpose of our study was to examine the presence and the extent of items related to an evidence-based research (EBR) approach in reporting guidelines, as well as their features and how these items were implemented. We evaluated whether protocol and complete report guidelines make sufficient/any recommendations to foster EBR.</p><p><strong>Study design and setting: </strong>This cross-sectional study included the most recent version of any reporting guideline that included a checklist published on the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Network website. Only guidelines that were applicable to the whole report were included. Two authors independently screened the guidelines and extracted data on whether the checklist and its accompanying article(s) incorporated EBR principles, including justification for a new study, optimal design of a relevant and necessary new study, contextualization of new results in relation to earlier studies, and whether they defined prior evidence.</p><p><strong>Results: </strong>Of 635 checklists published on the EQUATOR website as of August 1, 2024, we analyzed 219 pertaining to the whole report. Among these, 9 (4.1%) checklists included items addressing EBR for justifying a new study, 7 (3.2%) for optimally designing a relevant and necessary new study, and 3 (1.4%) for placing new results in the context of earlier research. The corresponding figures in accompanying articles were 5 (2.3%), 2 (0.9%), and 2 (0.9%), respectively.</p><p><strong>Conclusion: </strong>Currently, the majority of reporting guidelines do not explicitly incorporate EBR principles. Core EBR principles are applied primarily at the design phase of the research, where protocol guidance plays a critical upstream role. Reporting guidelines can complement this by requesting transparent reporting, particularly contextualization of results. To better support EBR and reduce research waste, it is essential to strengthen protocol guidance, align standards across protocols and final reports, and improve the uptake and implementation of these guidelines during the planning stages of research.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112008"},"PeriodicalIF":5.2,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234075","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":"Network meta-analysis of prediction models using aggregate or individual participant data - A scoping review and recommendations for reporting and conduct.","authors":"Maerziya Yusufujiang, Johanna A A Damen, Demy L Idema, Ewoud Schuit, Karel G M Moons, Valentijn M T de Jong","doi":"10.1016/j.jclinepi.2025.112006","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112006","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Prediction models are essential in clinical decision-making for estimating the probability of current (diagnosis, screening) or future (prognosis) outcomes. Network meta-analysis (NMA) serves as a powerful tool to compare the performance of multiple prediction models simultaneously. However, there is hardly any guidance on methods and reporting for studies employing NMA to evaluate prediction models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To provide an overview of NMAs assessing prediction model (external validation) performance, regardless of whether they use aggregate data (AD) or individual participant data (IPD). Additionally, we offer recommendations for improving the reporting and conduct of NMAs in prediction model research.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We searched PubMed and Embase up to 1&lt;sup&gt;st&lt;/sup&gt; September 2025 to identify studies that addressed the evaluation of diagnostic or prognostic prediction model performance using NMA. We included articles that employed NMA to compare and assess at least three prediction models. We summarized the identified studies based on, e.g., their application (diagnostic vs. prognostic), data use (AD vs. IPD), medical contexts in which the models were assessed, and evaluation metrics applied (e.g., discrimination, calibration, and (re)classification). Additionally, we examined the statistical approaches employed, the NMA assumptions (such as consistency, transitivity, and exchangeability), and the ranking methods used for model comparison.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;After screening 2,436 articles, 28 were included. Twenty-six studies (92.9%) used AD, while two (7.1%) used IPD. Hospital care was the most common setting (n = 22; 78.6%), with respirology (n = 7; 25.0%) and cardiology (n = 5; 17.9%) as the most frequently studied clinical domains. Key NMA assumptions were addressed differently across the 28 NMAs: 14.3% (n = 4) discussed transitivity, similarity, or exchangeability, and 53.6% (n = 15) tested for consistency. The statistical approach also varied, with 60.7% of studies (n=17) reporting Bayesian methods and 17.9% (n=5) reporting frequentist approaches. SUCRA (Surface Under the Cumulative Ranking) was the predominant ranking method (n = 18). Most NMAs included 5-10 models in the network, with five NMAs analyzing more than 20 models. Performance metrics varied, with 39.3% of studies (n = 11) reporting discrimination measures, such as C statistics, while none reported calibration metrics. Sensitivity or specificity was provided in 64.3% of studies (n = 18), and no articles reported advanced decision-analytic metrics like Decision Curve Analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This scoping review highlights the limited and diverse use of NMA methods in evaluating prediction models, with a predominant reliance on aggregate rather than individual participant data, and inconsistent consideration of key NMA assumptions and model performance metrics. We provide r","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112006"},"PeriodicalIF":5.2,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234108","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":"186 ","pages":"Article 111899"},"PeriodicalIF":5.2,"publicationDate":"2025-10-01","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":"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":"10.1016/j.jclinepi.2025.111902","url":null,"abstract":"<p><strong>Objectives: </strong>We aimed to assess paths taken by clinical prediction models (CPMs) 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 and 2020 by performing a forward citation search. We estimated 5- and 10-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 10-year probabilities of validation were 0.13 (0.06-0.19) and 0.16 (0.08-0.23), respectively. Only 1 article had a CPM with impact assessment during follow-up (10-year probability: 0.01 [0-0.04]). Among the 34 (31%) articles with a survey response, 17 (50%) had CPMs that had been used in clinical practice, in a median of five sites (interquartile range: 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":5.2,"publicationDate":"2025-10-01","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":"Balancing methodological rigor and simplicity in ROBUST-RCT: is optional assessment of bias domains the ideal path forward","authors":"Mohamed Attauabi","doi":"10.1016/j.jclinepi.2025.111891","DOIUrl":"10.1016/j.jclinepi.2025.111891","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"186 ","pages":"Article 111891"},"PeriodicalIF":5.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530970","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":"Response to the letter ‘Balancing methodological rigor and simplicity in ROBUST-RCT: is optional assessment of bias domains the ideal path forward’","authors":"Ying Wang,&nbsp;Gordon Guyatt","doi":"10.1016/j.jclinepi.2025.111892","DOIUrl":"10.1016/j.jclinepi.2025.111892","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"186 ","pages":"Article 111892"},"PeriodicalIF":5.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530973","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":"The challenges and opportunities of artificial intelligence and their impact on the journal","authors":"David Tovey,&nbsp;Andrea C. Tricco","doi":"10.1016/j.jclinepi.2025.111989","DOIUrl":"10.1016/j.jclinepi.2025.111989","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"186 ","pages":"Article 111989"},"PeriodicalIF":5.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314054","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":"Sometimes different, often the same: guidance on how recruitment and retention in trials need to be tailored to enhance participation of ethnic minority groups: a qualitative interview study in England.","authors":"Shoba Dawson, Bārbala Ostrovska, Shaun Treweek, Katie Gillies, Miles Witham, Declan Devane, Kamlesh Khunti, Peter Bower, Adwoa Parker, Irene Soulsby, Heidi Green","doi":"10.1016/j.jclinepi.2025.112005","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112005","url":null,"abstract":"<p><strong>Objective: </strong>Ethnic minority groups are consistently underrepresented in trials, limiting the generalisability and equity of research outcomes. This study explored the acceptability of existing recruitment and retention interventions for individuals from ethnic minority backgrounds.</p><p><strong>Study design and setting: </strong>We conducted semi-structured interviews with 20 adults from ethnic minority groups identified as \"non-white British\" in England. We used maximum variation purposive sampling to ensure diversity in age, gender, ethnicity, urban/rural residence, and religious background. Data were analysed thematically using an inductive and deductive approach.</p><p><strong>Results: </strong>Twenty adults (aged 20-70; 60% female) took part in the interviews. Findings were categorised into two main areas: themes specific to ethnic minority groups and themes related to recruitment and retention interventions. Cultural and linguistic considerations were considered critical for effective recruitment and retention. Opinions were mixed on the involvement of family members in decision-making about trial participation. Building trust within ethnic minority groups was deemed essential for encouraging participation.</p><p><strong>Participants: </strong>Highlighted the need for transparent communication when collecting and using ethnicity data and expressed concerns about potential tokenism. All participants emphasised the importance of providing clear information to support informed decision-making including why their ethnicity data was collected. Most participants preferred receiving invitation letters from their general practitioners (GPs), viewing them as more trustworthy due to an established relationship rather than text messages or emails from an unknown source, while some favoured emails, as they were more likely to read them. Participants preferred a layered information approach that was concise but prioritised content on risks, benefits, and trial relevance. These preferences are consistent with what other groups have reported previously. Higher financial incentives were sometimes perceived as indicators of increased trial risk.</p><p><strong>Conclusion: </strong>Recruitment and retention strategies must be tailored and co-designed to reflect the needs and preferences of ethnic minority groups. Clear, culturally sensitive, and transparent communication about trial aims, risks, and benefits is essential for building trust and enhancing informed decision-making for participation. Inclusive practices are vital for improving equitable representation in research.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112005"},"PeriodicalIF":5.2,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214312","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}