Clinical prognostic models help inform decision-making by estimating a patient's risk of experiencing an outcome in the future. The net benefit is increasingly being used to assess the clinical utility of models. By calculating an appropriately weighted average of the true and false positives of a model, the net benefit assesses the value added by a binary decision policy obtained when thresholding a model. Although such 'treat or not' decisions are common, prognostic models are also often used to tailor and personalise the care of patients, which implicitly involves the consideration of multiple interventions at different risk thresholds. We extend the net benefit to consider multiple decision thresholds simultaneously, by taking a weighted area under a rescaled version of the net benefit curve, deriving the continuous net benefit. In addition to the consideration of a continuum of interventions, we also show how the continuous net benefit can be used for populations with a range of optimal thresholds for a single treatment, due to individual variations in expected treatment benefit or harm, highlighting limitations of current proposed methods that calculate the area under the decision curve. We showcase the continuous net benefit through two examples of cardiovascular preventive care, comparing two modelling choices using the continuous net benefit. The continuous net benefit informs researchers of the clinical utility of models during selection, development, and validation, and helps decision makers understand their usefulness, improving their viability towards implementation.

翻译：临床预后模型通过估计患者未来发生结局的风险来辅助决策。净效益正日益被用于评估模型的临床效用。通过计算模型真阳性与假阳性的适当加权平均值，净效益评估了通过模型阈值化获得的二元决策策略所增加的价值。尽管此类“治疗与否”的决策很常见，但预后模型也常被用于定制和个体化患者护理，这隐含着在不同风险阈值下考虑多种干预措施的需求。我们扩展了净效益的概念，通过计算重标度净效益曲线下的加权面积，同时考虑多个决策阈值，从而推导出连续净效益。除了考虑连续干预措施外，我们还展示了连续净效益如何用于存在单一治疗最佳阈值范围的人群（由于个体预期治疗获益或危害的差异），并指出了当前计算决策曲线下面积方法的局限性。我们通过两个心血管预防护理的案例展示了连续净效益的应用，使用连续净效益比较了两种建模选择。连续净效益帮助研究者在模型选择、开发和验证过程中了解其临床效用，并协助决策者理解模型的价值，从而提升其实施可行性。