Prediction models are increasingly proposed for guiding treatment decisions, but most fail to address the special role of treatments, leading to inappropriate use. This paper highlights the limitations of using standard prediction models for treatment decision support. We identify 'causal blind spots' in three common approaches to handling treatments in prediction modelling and illustrate potential harmful consequences in several medical applications. We advocate for an extension of guidelines for development, reporting, clinical evaluation and monitoring of prediction models to ensure that the intended use of the model is matched to an appropriate risk estimand. For decision support this requires a shift towards developing predictions under the specific treatment options under consideration ('predictions under interventions'). We argue that this will improve the efficacy of prediction models in guiding treatment decisions and prevent potential negative effects on patient outcomes.

翻译：预测模型越来越多地被提议用于指导治疗决策，但大多数模型未能解决治疗的特殊作用，导致不当使用。本文强调了标准预测模型在治疗决策支持中的局限性。我们识别出预测建模中三种常见处理治疗方式所导致的“因果盲点”，并在若干医学应用中展示了潜在的有害后果。我们主张扩展预测模型的开发、报告、临床评估及监测指南，以确保模型的预期用途与适当的风险估计目标相匹配。对于决策支持，这要求转向在特定治疗选项下开发预测（即“干预条件下的预测”）。我们认为，这将提高预测模型在指导治疗决策中的有效性，并防止对患者结局产生潜在的负面影响。