Clinical prediction models (CPMs) are tools that compute the risk of an outcome given a set of patient characteristics and are routinely used to inform patients, guide treatment decision-making, and resource allocation. Although much hope has been placed on CPMs to mitigate human biases, CPMs may potentially contribute to racial disparities in decision-making and resource allocation. While some policymakers, professional organizations, and scholars have called for eliminating race as a variable from CPMs, others raise concerns that excluding race may exacerbate healthcare disparities and this controversy remains unresolved. The Guidance for Unbiased predictive Information for healthcare Decision-making and Equity (GUIDE) provides expert guidelines for model developers and health system administrators on the transparent use of race in CPMs and mitigation of algorithmic bias across contexts developed through a 5-round, modified Delphi process from a diverse 14-person technical expert panel (TEP). Deliberations affirmed that race is a social construct and that the goals of prediction are distinct from those of causal inference, and emphasized: the importance of decisional context (e.g., shared decision-making versus healthcare rationing); the conflicting nature of different anti-discrimination principles (e.g., anticlassification versus antisubordination principles); and the importance of identifying and balancing trade-offs in achieving equity-related goals with race-aware versus race-unaware CPMs for conditions where racial identity is prognostically informative. The GUIDE, comprising 31 key items in the development and use of CPMs in healthcare, outlines foundational principles, distinguishes between bias and fairness, and offers guidance for examining subgroup invalidity and using race as a variable in CPMs. This GUIDE presents a living document that supports appraisal and reporting of bias in CPMs to support best practice in CPM development and use.

临床预测模型 （CPM） 是根据一组患者特征计算结果风险的工具，通常用于通知患者、指导治疗决策和资源分配。尽管人们寄予厚望 CPM 以减轻人类偏见，但 CPM 可能会导致决策和资源分配方面的种族差异。虽然一些政策制定者、专业组织和学者呼吁从 CPM 中消除种族这一变量，但其他人则担心排除种族可能会加剧医疗保健差异，而这一争议仍未解决。医疗保健决策和公平的无偏见预测信息指南 （GUIDE） 为模型开发人员和卫生系统管理员提供了关于在 CPM 中透明使用种族和减轻跨上下文的算法偏见的专家指南，这些指南通过多元化的 14 人技术专家小组 （TEP） 的 5 轮修改后的 Delphi 流程开发。审议确认了种族是一种社会建构，预测的目标与因果推理的目标不同，并强调了：决策背景的重要性（例如，共同决策与医疗保健配给）;不同反歧视原则的冲突性质（例如，反分类原则与反从属原则）;以及在种族身份具有预后信息的情况下，识别和平衡在实现种族意识与种族意识 CPM 的公平相关目标中权衡的重要性。 该指南包含在医疗保健中开发和使用 CPM 的 31 个关键项目，概述了基本原则，区分了偏见和公平性，并为检查亚组无效性和在 CPM 中使用种族作为变量提供了指导。本指南提供了一个动态文档，支持对 CPM 中的偏差进行评估和报告，以支持 CPM 开发和使用的最佳实践。