Julius Center for Health Sciences and Primary Care and Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands (K.G.M., J.B.R.).
Kleijnen Systematic Reviews, York, United Kingdom (R.F.W., M.W.).
Ann Intern Med. 2019 Jan 1;170(1):W1-W33. doi: 10.7326/M18-1377.
Prediction models in health care use predictors to estimate for an individual the probability that a condition or disease is already present (diagnostic model) or will occur in the future (prognostic model). Publications on prediction models have become more common in recent years, and competing prediction models frequently exist for the same outcome or target population. Health care providers, guideline developers, and policymakers are often unsure which model to use or recommend, and in which persons or settings. Hence, systematic reviews of these studies are increasingly demanded, required, and performed. A key part of a systematic review of prediction models is examination of risk of bias and applicability to the intended population and setting. To help reviewers with this process, the authors developed PROBAST (Prediction model Risk Of Bias ASsessment Tool) for studies developing, validating, or updating (for example, extending) prediction models, both diagnostic and prognostic. PROBAST was developed through a consensus process involving a group of experts in the field. It includes 20 signaling questions across 4 domains (participants, predictors, outcome, and analysis). This explanation and elaboration document describes the rationale for including each domain and signaling question and guides researchers, reviewers, readers, and guideline developers in how to use them to assess risk of bias and applicability concerns. All concepts are illustrated with published examples across different topics. The latest version of the PROBAST checklist, accompanying documents, and filled-in examples can be downloaded from www.probast.org.
在医疗保健中，预测模型使用预测因素来估计个体出现某种情况或疾病（诊断模型）或将来出现某种情况或疾病（预后模型）的概率。近年来，关于预测模型的出版物越来越多，对于相同的结局或目标人群，往往存在相互竞争的预测模型。医疗保健提供者、指南制定者和决策者通常不确定应该使用哪个模型，也不确定在哪些人群或环境中使用。因此，对这些研究进行系统评价的需求、要求和实施越来越多。预测模型系统评价的一个关键部分是检查偏倚风险和对目标人群和环境的适用性。为了帮助评论者进行这一过程，作者开发了 PROBAST（预测模型风险偏倚评估工具），用于开发、验证或更新（例如，扩展）预测模型，包括诊断和预后模型。PROBAST 通过一个涉及该领域专家的共识过程开发。它包括 4 个领域（参与者、预测因素、结局和分析）的 20 个信号问题。本解释和说明文件描述了纳入每个领域和信号问题的基本原理，并指导研究人员、评论者、读者和指南制定者如何使用这些问题来评估偏倚风险和适用性问题。所有概念都用不同主题的已发表示例来说明。PROBAST 清单的最新版本、配套文件和填写示例可从 www.probast.org 下载。
