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.

项目摘要 在当前的临床范式下，大多数共享病理学的患者倾向于接受类似的治疗 根据基于先前进行的临床试验的临床准则的方式 分析。虽然某些情况是对护理分层的修正 基于特定危险因素的存在，真正的护理个性化（即治疗或 根据对患者特征的全面审查，可能包括 尽管有可能提高患者级别，但患者特定的预测模型仍然极为罕见 结果。在这方面，一个尚未回答的重要问题是 如果大量患者将其使用，则个性化方法将导致临床益处。在这个 鉴于很少大规模实施个性化护理的例子，不可能 直接提供答案；但是，我们可以使用现有数据通过 计算机模拟。因此，我们建议使用〜130的数据先前发表的NHLBI资助的数据 临床试验以模拟个性化医学的效果，并将小组级别的结果与结果进行比较 预计在同一患者人群中没有使用个性化治疗方法。具体来说， 对于本研究中包括的每个临床试验，我们将为主要结果创建ARM特定的预测模型 并将其应用于相对的研究组，从而估算了实现理论，特定于患者的概率 主要结果是将它们分配到对面的审判部门。然后将进行仿真 分别分别将患者分配给患者的所有试验分别：1）试验的治疗组，2）对照 试验的臂或3）任何手臂都带有不良结果的最低概率（即预测分配）。 然后，我们将通过比较累积的患病率来计算预测分配的净益处 结果在模拟中与所有患者分配给任何一个治疗臂的模拟 （对于积极的试验）或将所有患者分配到对照组（用于阴性试验）的地方。最后，我们会的 收集所有纳入试验的数据，并确定与净福利变化相关的因素 预测分配，包括预测模型的特定特定风险因素和绩效指标 用于患者分配。这项研究将使我们首次估计潜在的改进 人口水平，这与广泛利用个性化方法有关 治疗选择。我们还将在临床方案和情况下产生关键信息 这种方法将产生最高的好处。这些信息对于高效至关重要 并针对未来个性化医学计划的实施。