Epidemiology and reporting characteristics of systematic reviews of clinical prediction models: a scoping review

IF 5.2 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Journal of Clinical Epidemiology Pub Date : 2025-03-21 DOI:10.1016/j.jclinepi.2025.111763
Yunhui Xia , Mei Zhang , Yunliang Yao , Tingting Cai , Hangfeng Mo , Jiantong Shen , Jianlin Lou
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Abstract

Objectives

This study aimed to explore research trends and areas of interest in systematic reviews (SRs) and meta-analysis of clinical prediction models (CPMs), while summarizing their conduct and reporting characteristics.

Study Design and Setting

A scoping review was conducted, with searches performed in PubMed, Embase, and Cochrane Library from inception to January 7, 2023. Pairs of reviewers independently screened potentially eligible studies. Data on bibliographic and methodological characteristics were collected and analyzed descriptively.

Results

A total of 1004 SRs published between 2001 and 2023 were included. The number of SRs increased significantly after 2020, with the majority originating from Europe (44.1%) and Asia (26.7%). Populations and outcomes were categorized into 19 and 34 classifications, respectively. The general population was the most frequently targeted (38.7%), and mortality was the most common outcome (18.9%). The prediction or diagnosis of neoplasms in the general population was the most prevalent focus (7.2%). Prognostic models were included only in 69.6% of SRs, while diagnostic models were included in 16.8%; 13.6% included both. The number of primary studies included in SRs ranged from 1 to 495, and the models ranged from 1 to 731. Most SRs lacked standardized reporting: 88.3% did not frame their review questions using established frameworks, and 79.8% did not follow standardized checklists for data extraction. Quality and risk of bias assessments were reported in 76.5% of SRs, with the Prediction model Risk of Bias Assessment Tool (27.9%) and the Quality Assessment of Diagnostic Accuracy Studies-2 tool (17.0%) being the most common. Narrative synthesis was the predominant method for evidence summarization (63.5%), while meta-analysis was conducted in 36.5%. Measures of model performance were summarized in 80.5% of SRs, with discrimination being the most frequently reported (67.7%). Only 5.2% assessed the certainty of evidence. Moreover, 42.2% of SRs published a protocol, 76.0% clearly stated support, and 91.1% stated competing interests.

Conclusion

The number of SRs of CPMs has grown substantially, with increasing diversity in populations and outcomes. However, significant variability in conduct and reporting was observed. Future SRs should strictly follow well-developed guidelines, and a dedicated study assessing the reporting quality and risk of bias in SRs of CPMs is warranted.

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临床预测模型系统综述的流行病学和报告特点:范围综述。
目的:本研究旨在探讨临床预测模型(cpm)的系统评价(SRs)和荟萃分析的研究趋势和兴趣领域,同时总结其行为和报告特点。研究设计和设置:进行了范围审查,并在PubMed, Embase和Cochrane图书馆进行了从成立到2023年1月7日的搜索。成对的审稿人独立筛选潜在的合格研究。收集文献和方法特征数据并进行描述性分析。结果:共纳入2001 - 2023年间发表的1004篇SRs。2020年之后,SRs的数量显著增加,其中大多数来自欧洲(46.7%)和亚洲(24.1%)。人群和结果分别被分为19和34类。普通人群是最常见的目标(38.7%),死亡是最常见的结果(18.9%)。一般人群中肿瘤的预测或诊断是最普遍的焦点(7.2%)。预后模型仅被纳入69.6%的SRs,而诊断模型被纳入16.8%;13.6%的人两者都有。纳入SRs的初步研究数为1 ~ 495,模型数为1 ~ 731。大多数SRs缺乏标准化的报告:88.3%的SRs没有使用已建立的框架来构建他们的审查问题,79.8%的SRs没有遵循标准化的数据提取清单。76.5%的SRs报告了偏倚评估的质量和风险,其中PROBAST(27.9%)和QUADAS-2(17.0%)工具最为常见。叙事综合是证据总结的主要方法(63.5%),荟萃分析占36.5%。80.5%的SRs总结了模型性能的度量,歧视是最常见的报告(67.7%)。只有5.2%的人评估了证据的确定性。此外,42.2%的sr发布了协议,76.0%明确表示支持,91.1%表示有竞争利益。结论:cpm上的SRs数量大幅增加,种群和结果的多样性增加。然而,观察到在行为和报告方面存在显著差异。未来的SRs应严格遵循完善的指导方针,并有必要进行专门的研究,评估cpm SRs的报告质量和偏倚风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Clinical Epidemiology
Journal of Clinical Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
12.00
自引率
6.90%
发文量
320
审稿时长
44 days
期刊介绍: The Journal of Clinical Epidemiology strives to enhance the quality of clinical and patient-oriented healthcare research by advancing and applying innovative methods in conducting, presenting, synthesizing, disseminating, and translating research results into optimal clinical practice. Special emphasis is placed on training new generations of scientists and clinical practice leaders.
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