PREDICTIVE OPTIMAL ANTICLOTTING TREATMENT FOR SEGMENTED PATIENT POPULATIONS

项目摘要 抗凝血药物降低血栓形成的风险，治疗可能导致中风、肺部疾病的疾病 血栓、深静脉血栓形成或其他与血液凝结有关的疾病。抗凝血的影响和价值 在美国，药物治疗是戏剧性的。例如，中风是美国第三大死因，超过 每年有14万人死亡。大多数中风的发生是由于缺血(87%)或短暂性缺血 发作(TIA，~5%-10%)，通常通过使用包括抗凝剂的抗凝药物(例如， 华法林和达比加兰)和抗血小板药物(如氯吡格雷)。无论病人的疾病或状况如何 确定抗凝血剂处方，选择最佳药物组合和治疗方案 由于遗传原因，抗凝药物反应的个体差异使情况变得复杂(例如，20倍 华法林的差异)、生理和依从性。在实践中，提供商结合使用经验、 科学证据和临床试验结果，以发展抗凝“最佳实践”治疗计划，旨在 粗略地将提供者患者群体中的患者对患者响应的变异性和风险降至最低。 然而，患者的高度异质性导致患者个体对这些疾病的反应不同 “最佳实践”药物方案方法。简而言之，目前还不存在实用的最优防凝处理方案 考虑个体危险因素的大的异质患者群体；药物和方案选择； 并将中风的风险降至最低。访问大型综合电子病历(EMR) 覆盖不同的患者群体，再加上新颖的建模和计算模拟，提供了 在硅胶识别和验证最佳抗凝治疗方面获得前所未有的机会 战略。 我们提出了一种新的计算方法，它使用两个患者的个人数据和结果证据 大型电子病历(EMR)数据库，用于进行并行临床模拟比较 两种或两种以上抗凝血药物和剂量方案的结果。该方法首先将电子病历数据转换为电子病历- 基于反映电子病历人群的统计和个体特征的模拟数据。然后我们 应用先进的治疗模拟方法来预测多个药物剂量方案的结果和成本。 最后，我们应用一种优化方法来确定最优的治疗方案 人口(例如，非洲裔美国人部分，50岁以上的白人女性部分，…)。最后，我们将在 对预测的最优防凝治疗方案的稳健性和有效性进行了电子测试。这种方法， 承诺提供第一个并排抗凝血临床模拟和结果的环境 基于现有电子病历数据集的对整个人口的预测可以计算、比较和 形成了鲜明对比。这样的预测性证据可以用来指导临床试验设计，并建议 改善全医院范围的抗凝治疗计划。

Project Summary Anticlotting drugs reduce risk to thrombosis and treat conditions that might lead to stroke, pulmonary embolism, deep vein thrombosis or other blood clotting related disease. The impact and value of anticlotting medication in the U.S. is dramatic. For example, stroke is the third leading cause of death in the U.S. with over 140,000 deaths annually. The majority of stroke incidences are due to ischemia (87%) or transient ischemic attack (TIA, ~5-10%) and are typically managed by the use of anticlotting drugs including anticoagulants (e.g., warfarin and dabigatran) and antiplatelets (e.g., clopidogrel). Whatever the patient's disease or condition leading to a prescription of an anticlotting agent, selecting the best combination of drug and treatment protocol is complicated by the individual differences in anticlotting drug response due to genetics (e.g. >20-fold difference for warfarin), physiology, and compliance. In practice, providers use a combination of experience, scientific evidence and clinical trial results to develop anticlotting “best practice” treatment plans designed to roughly minimize the patient-to-patient response variability and risks across the provider's patient population. However, the high degree of patient heterogeneity causes variations in individual patient response to these “best practice” drug-protocol approaches. In short, no practical optimal anticlotting treatment plan exists for large heterogeneous patient populations that accounts for individual risk factors; drug and protocol options; and achieves minimal risk to stroke. Access to large comprehensive electronic medical records (EMR) covering diverse patient populations, coupled with novel modeling and computational simulations provides an unprecedented opportunity to conduct in silico identification and validation of optimal anticlotting treatment strategies. We propose a novel computational approach that uses individual patient data and outcome evidence from two large electronic medical record (EMR) databases to conduct side-by-side clinical simulations comparing outcomes for two or more anticlotting drug and dose protocols. The approach first converts EMR data to EMR- based simulated data that reflects the statistical and individual characteristics of the EMR population. We then apply advanced treatment simulation methods to predict outcomes and costs of multiple drug-dosing protocols. Finally, we apply an optimization approach to identify the optimal treatment plans for segments of the population (e.g. the African American segment, white females over 50 segment, …). Finally, we will conduct in silico tests of the robustness and validation of the predicted optimal anticlotting treatment plan. This approach, promises to provide the first environment in which side-by-side anticlotting clinical simulations and outcome predictions for an entire population based on existing EMR data sets can be calculated, compared and contrasted. Such predictive evidence can then be used to guide clinical trial designs, and suggest improvements to hospital-wide anticlotting treatment plans.