Computational simulation of the potential improvement in clinical outcomes of cardiovascular diseases with the use of a personalized predictive medicine approach

使用个性化预测医学方法对心血管疾病临床结果的潜在改善进行计算模拟

• 批准号: 10580116
• 负责人: Cedric Manlhiot
• 金额: $ 12.28万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10580116
• 关键词: Affect; Cardiovascular Diseases; Caring; Characteristics; Clinical; Clinical Research; Clinical Trials; Computer Simulation; Conduct Clinical Trials; Control Groups; Data; Eligibility Determination; Funding; Future; Goals; Guidelines; Medical; Medicine; Meta-Analysis; Methodology; Methods; National Heart, Lung, and Blood Institute; Outcome; Outcome Study; Pathology; Patients; Pediatric cardiology; Performance; Pilot Projects; Population; Prevalence; Probability; Publishing; Randomized; Randomized, Controlled Trials; Recommendation; Regression Analysis; Risk; Risk Factors; Risk Reduction; Sampling; Selection for Treatments; Stratification; Therapeutic; Time; Uncertainty; adverse outcome; arm; design; effective intervention; effective therapy; future implementation; hazard; improved; improved outcome; online resource; outcome prediction; patient population; personalized approach; personalized care; personalized medicine; personalized predictions; prediction algorithm; predictive modeling; primary outcome; programs; randomized trial; randomized, clinical trials; simulation; standard of care; treatment arm; treatment choice; treatment group; treatment strategy

Project summary Under the current clinical paradigm, the majority of patients sharing a pathology tend to be treated in a similar manner according to clinical guidelines that are based on previously conducted clinical trials combined into meta- analyses. While some situations are amendable to the stratification of care, that is using more or less intensive therapy based on the presence of specific risk factors, true personalization of care (i.e. therapeutic or management selected based on a comprehensive review of patient characteristics and possibly including patient-specific prediction models) remains exceedingly rare despite the potential for improved patient-level outcomes. One important question in this regard that has not yet been answered is the extent to which a personalized approach would result in clinical benefits should it be used in a large number of patients. At this time, given the paucity of examples of large scale implementation of personalized care, it is not possible to directly provide an answer; however, we could use existing data to generate a reliable approximation through computer simulations. Thus, we propose to use data from ~130 previously published NHLBI funded randomized clinical trials to simulate the effect of personalized medicine and compare the group-level outcomes to results expected in the same patient population without using a personalized approach to treatment choice. Specifically, for each clinical trial included in this study, we will create arm-specific prediction models for the primary outcome and apply it to the opposite study group, thus estimating the theoretical, patient-specific probability of achieving the primary outcome had they been assigned to the opposite trial arm. Simulations will then be performed separately for all trials where patients are respectively assigned to: 1) the treatment arm of the trial, 2) the control arm of the trial or 3) to whatever arm carries the lowest probability of adverse outcomes (i.e. predictive allocation). We will then calculate the net benefit of predictive allocation by comparing the cumulative prevalence of outcomes in that simulation vs. either the simulation where all patients are assigned to either the treatment arm (for positive trials) or where all patients are assigned to the control arm (for negative trials). Finally, we will compile the data from all included trials and identify factors that are associated with changes in the net benefit of predictive allocation, including both trial-specific risk factors and performance metrics of the prediction model used for patient allocation. This study will allow us, for the first time, to estimate the potential improvement, at the population level, that would be associated with the widespread utilization of a personalized approach to treatment choice. We will also generate crucial information in regards to the clinical scenarios and situations where such an approach would generate the highest benefits. This information will be essential for the efficient and targeted implementation of future personalized medicine programs.

项目总结