Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence.
Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence.
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DOI：
10.1136/bmjopen-2020-048008
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发表时间：
2021-07-09
期刊：
影响因子：
2.9
通讯作者：
Moons KG
中科院分区：
文献类型：
--
作者：
Collins GS;Dhiman P;Andaur Navarro CL;Ma J;Hooft L;Reitsma JB;Logullo P;Beam AL;Peng L;Van Calster B;van Smeden M;Riley RD;Moons KG
个体预后或诊断多变量预测模型的透明报告（TRIPOD）声明和预测模型风险偏倚评估工具（PROBAST）均已发布，以改善诊断和预后预测模型研究的报告和批判性评估。本文描述了将用于开发TRIPOD语句（TRIPOD人工智能，AI）和PROBAST（PROBAST-AI）工具的扩展的过程和方法，用于应用机器学习技术的预测模型研究。TRIPOD-AI和PROBAST-AI将根据EQUATOR网络发布的指南进行开发，并将包括五个阶段。第一阶段将包括两项系统性综述（涵盖所有医学领域，特别是肿瘤学领域），以检查已发表的基于机器学习的预测模型研究的报告质量。在第2阶段，我们将使用德尔菲流程咨询不同的关键利益相关者群体，以确定考虑纳入TRIPOD-AI和PROBAST-AI的项目。第三阶段将是虚拟共识会议，以巩固和优先考虑TRIPOD-AI和PROBAST-AI中包含的关键项目。第四阶段将涉及开发TRIPOD-AI清单和PROBAST-AI工具，并编写相应的解释和阐述文件。在最后阶段，即第5阶段，我们将通过期刊、会议、博客、网站（包括TRIPOD、PROBAST和EQUATOR网络）和社交媒体传播TRIPOD-AI和PROBAST-AI。TRIPOD-AI将为基于机器学习的预测模型研究的研究人员提供报告指南，帮助他们报告读者评估研究质量和解释研究结果所需的关键细节，从而减少研究浪费。我们预计PROBAST-AI将帮助研究人员、临床医生、系统评价者和政策制定者批判性地评估基于机器学习的预测模型研究的设计、实施和分析，并提供一个强大的标准化偏倚评估工具。牛津大学中央大学研究伦理委员会于2020年12月10日授予伦理批准（R73034/RE 001）。这项研究的结果将通过同行审查出版物传播。CRD 42019140361和CRD 42019161764。
The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias ASsessment Tool (PROBAST) were both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-artificial intelligence, AI) and the PROBAST (PROBAST-AI) tool for prediction model studies that applied machine learning techniques. TRIPOD-AI and PROBAST-AI will be developed following published guidance from the EQUATOR Network, and will comprise five stages. Stage 1 will comprise two systematic reviews (across all medical fields and specifically in oncology) to examine the quality of reporting in published machine-learning-based prediction model studies. In stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-AI and PROBAST-AI. Stage 3 will be virtual consensus meetings to consolidate and prioritise key items to be included in TRIPOD-AI and PROBAST-AI. Stage 4 will involve developing the TRIPOD-AI checklist and the PROBAST-AI tool, and writing the accompanying explanation and elaboration papers. In the final stage, stage 5, we will disseminate TRIPOD-AI and PROBAST-AI via journals, conferences, blogs, websites (including TRIPOD, PROBAST and EQUATOR Network) and social media. TRIPOD-AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate PROBAST-AI will help researchers, clinicians, systematic reviewers and policymakers critically appraise the design, conduct and analysis of machine learning based prediction model studies, with a robust standardised tool for bias evaluation. Ethical approval has been granted by the Central University Research Ethics Committee, University of Oxford on 10-December-2020 (R73034/RE001). Findings from this study will be disseminated through peer-review publications. CRD42019140361 and CRD42019161764.
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