Consensus Framework for Cardiovascular Risk Prediction in a Clinical Setting

项目摘要/摘要 在临床环境中准确评估心血管风险对于适当的管理是重要的 并为数以百万计的患者提供咨询。目前美国的治疗指南侧重于单一风险评分。这 该方法具有较好的整体性能，但在临床应用中存在一定的局限性。例如, 重要的新标记或特定的缺失模式不能用既定的指导方针来适应- 认可的预测分数。在新的人群或亚群中，得分也经常高于或低于预期。这些 差距要求探索超越单一模型范式的风险预测方法 经典的COX比例风险和Logistic回归方法。与此同时，最近的出版物 质疑无法解释的模型在预测环境中的效用，特别是在 医疗保健。因此，我们需要在模型的复杂性和可解释性之间取得平衡 为了避免在不久的将来给患者带来潜在的悲惨后果。这个项目的目标是 解决这些问题。我们将评估现有共识在歧视和校准方面的改进 超级学习器和极端梯度助推器等模型在临床设置中并开发出一种新的 被称为共识框架的方法。这种新的方法具有一致性，因为它 结合多个已发布和验证的风险模型，不仅确保良好的整体性能，而且 在重要的患者亚群中表现良好。共识框架适用于临床 实践，因为它可以处理有限的信息或额外的风险因素。我们还将评估具体的 预后风险预测的性质以及它们如何为选择最合适的类别提供信息 模特们。该项目与公共卫生有关，因为1)共识模型的可解释性 我们建议在评估其质量和局限性时确保透明度，这是 医疗保健中高风险决策的至高无上的重要性，2)他们适应缺失的灵活性 数据或已知风险因素的可用性将产生更个性化的治疗决策，3)更好 对预后风险预测的独特属性的理解将决定更具信息量的选择 预后模型。所有这些因素将使数百万患者在知情的情况下做出更好的治疗决定。

Project Summary/Abstract Accurate assessment of cardiovascular risk in clinical settings is important for the appropriate management and counseling of millions of patients. Current US treatment guidelines focus on a single risk score. This approach has good overall performance but has limitations when applied to clinical setting. For example, important new markers or specific missing patterns cannot be accommodated with established, guideline- endorsed prediction scores. Scores also often over or under predict in new populations or subgroups. These gaps call for exploring approaches to risk prediction that go beyond a single model paradigm and beyond classical Cox proportional hazards and logistic regression methods. At the same time, recent publications question the utility of uninterpretable models in prognostic settings especially in high-stakes situations in healthcare. Therefore we need to strike a balance between the sophistication of a model and its interpretability in order to avoid potentially tragic consequences for patients in the near future.The goal of this project is to address these issues. We will evaluate the improvement in discrimination and calibration of existing consensus models such as the Super Learner and eXtreme Gradient Boosting in clinical settings and develop a novel method called the Consensus Framework. This novel method has the consensus property because it combines multiple published and validated risk models to ensure not only good overall performance but also good performance in important subgroups of patients. The Consensus Framework is adapted to clinical practice because it can handle limited information or additional risk factors. We will also assess specific properties of prognostic risk prediction and how they inform the selection of the most appropriate class of models. This project is relevant to public health because 1) the interpretability of the consensus models that we propose to use ensures transparency in the assessment of their quality and limitations, which is of paramount importance in high-stakes decision making in healthcare, 2) their flexibility to accommodate missing data or the availability of known risk factors will produce more personalized treatment decisions, 3) a better understanding of the unique properties of prognostic risk prediction will dictate a more informative choice of prognostic model. All these factors will lead to better informed treatment decisions for millions of patients.