Consensus Framework for Cardiovascular Risk Prediction in a Clinical Setting

临床环境中心血管风险预测的共识框架

• 批准号: 10580110
• 负责人: Olga Demler
• 金额: $ 13.43万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10580110
• 关键词: Address; Affect; Algorithms; Area; Biological Markers; Boston; Calibration; Categories; Classification; Clinical; Consensus; Counseling; Data; Data Set; Decision Making; Development; Diagnostic; Discrimination; Electronic Health Record; Ensure; Equation; Equilibrium; Evaluation; Event; Foundations; Future; Goals; Guidelines; Healthcare; Hospitals; Individual; Logistic Regressions; Machine Learning; Measures; Methods; Modeling; Patients; Pattern; Performance; Population; Property; Prospective cohort; Public Health; Publications; Publishing; ROC Curve; Race; Risk; Risk Assessment; Risk Estimate; Risk Factors; Subgroup; Techniques; Testing; Time; Women' s Health; cardiovascular risk factor; clinical application; clinical practice; clinical prognostic; cohort; flexibility; follow-up; gradient boosting; hazard; improved; machine learning method; novel; novel marker; patient subsets; personalized medicine; predictive modeling; prognostic; prognostic model; risk prediction; risk prediction model; tool; treatment guidelines

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.

项目总结/摘要 在临床环境中准确评估心血管风险对于适当的管理非常重要 为数百万患者提供咨询服务。目前美国的治疗指南侧重于单一风险评分。这 该方法具有良好的整体性能，但在应用于临床环境时具有局限性。比如说， 重要的新标记或特定的缺失模式不能与既定的指导方针相适应， 认可的预测分数。在新的人群或亚组中，得分也经常超过或低于预测。这些 差距要求探索超越单一模型范式的风险预测方法 经典的考克斯比例风险和逻辑回归方法。与此同时，最近的出版物 质疑不可解释的模型在预后环境中的效用，特别是在高风险的情况下， 健康护理因此，我们需要在模型的复杂性和可解释性之间取得平衡 以避免在不久的将来对患者造成潜在的悲惨后果。该项目的目标是 解决这些问题。我们将评估现有共识的区分和校准方面的改进 模型，如超级学习者和极端梯度提升在临床环境中，并开发一种新的 这就是所谓的共识框架。这种新颖的方法具有共识属性，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， 在重要患者亚组中表现良好。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 模型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使用确保透明度的方法， 在医疗保健的高风险决策中至关重要，2）它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）更好的 了解预后风险预测的独特性质将决定一个更有用的选择， 预测模型所有这些因素将为数百万患者提供更好的知情治疗决策。