Abstract

Using a risk stratification model to guide clinical practice often requires the choice of a cutoff—called the decision threshold—on the model’s output to trigger a subsequent action such as an electronic alert. Choosing this cutoff is not always straightforward. We propose a flexible approach that leverages the collective information in treatment decisions made in real life to learn reference decision thresholds from physician practice. Using the example of prescribing a statin for primary prevention of cardiovascular disease based on 10-year risk calculated by the 2013 pooled cohort equations, we demonstrate the feasibility of using real-world data to learn the implicit decision threshold that reflects existing physician behavior. Learning a decision threshold in this manner allows for evaluation of a proposed operating point against the threshold reflective of the community standard of care. Furthermore, this approach can be used to monitor and audit model-guided clinical decision making following model deployment.

中文翻译：

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从总体临床医生行为中学习风险分层模型的决策阈值

摘要

使用风险分层模型来指导临床实践通常需要在模型的输出上选择一个截止值（称为决策阈值）以触发后续操作，例如电子警报。选择这个截止点并不总是那么简单。我们提出了一种灵活的方法，利用现实生活中做出的治疗决策中的集体信息，从医生实践中学习参考决策阈值。以根据 2013 年汇总队列方程计算的 10 年风险为心血管疾病的一级预防开出他汀类药物的示例，我们证明了使用真实世界数据学习反映现有医生行为的隐式决策阈值的可行性。以这种方式学习决策阈值允许对照反映社区护理标准的阈值来评估提议的操作点。此外，该方法可用于在模型部署后监控和审计模型引导的临床决策。