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

项目总结 在目前的临床模式下，大多数具有相同病理特征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合并为Meta-Mate的临床指南的方式 分析。虽然有些情况可以修改为护理的分层，但也就是或多或少地使用密集护理 基于特定风险因素的治疗、真正的个性化护理(即治疗性或 管理层是根据对患者特征的全面审查而选择的，可能包括 特定于患者的预测模型)仍然非常罕见，尽管有可能改善患者水平 结果。在这方面，一个尚未得到回答的重要问题是， 如果在大量患者中使用个性化方法，将会带来临床好处。对此 时间，考虑到大规模实施个性化护理的例子很少，不可能 直接提供答案；但是，我们可以使用现有数据通过以下方式生成可靠的近似值 计算机模拟。因此，我们建议使用之前发表的~130项NHLBI资助的随机数据 模拟个性化药物的效果并将组水平结果与结果进行比较的临床试验 预计在相同的患者群体中，不使用个性化的方法来选择治疗。具体来说， 对于这项研究中包括的每个临床试验，我们将为主要结果创建特定于ARM的预测模型 并将其应用于相反的研究小组，从而估计理论上、特定于患者的实现 主要的结果是他们被分配给了相反的试验组。然后将执行模拟 对于所有试验，其中患者分别被分配到：1)试验的治疗组，2)对照组 试验的ARM或3)具有最低不良结果概率的ARM(即预测性分配)。 然后，我们将通过比较预测分配的累积患病率来计算预测分配的净收益 该模拟的结果与将所有患者分配到治疗臂的模拟 (对于阳性试验)或将所有患者分配到对照组(对于阴性试验)。最后，我们会 汇编所有纳入试验的数据，并确定与净收益变化相关的因素 预测性分配，包括特定于试验的风险因素和预测模型的绩效指标 用于患者分配。这项研究将使我们第一次能够估计潜在的改善，在 人口水平，这将与个性化方法的广泛使用相关联 治疗选择。我们还将生成有关临床情景和情况的关键信息 这样的方法将产生最高的利益。这些信息将是高效的 并有针对性地实施未来的个性化医疗计划。

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.