Predictive optimal anticlotting treatment for segmented patient populations

说明(申请人提供)：抗凝血药物降低血栓形成的风险，治疗可能导致中风、肺栓塞、深静脉血栓形成或其他凝血相关疾病的情况。在美国，抗凝血药物的影响和价值是巨大的。例如，中风是美国的第三大死因，每年有超过14万人死亡。大多数卒中发生是由于缺血(87%)或短暂性脑缺血发作(TIA，约5-10%)，通常通过使用抗凝药物(如华法林和达比卡特兰)和抗血小板药物(如氯吡格雷)进行治疗。无论患者的疾病或状况导致处方的抗凝血剂，选择更好的药物和治疗方案的组合是复杂的，因为由于遗传(例如华法林的20倍差异)、生理学和 合规性。在实践中，提供者利用经验、科学证据和临床试验结果相结合的方式来制定抗凝“最佳实践”治疗计划，旨在大致最大限度地减少提供者患者群体中患者对患者反应的变异性和风险。然而，患者的高度异质性导致患者个体对这些“最佳实践”药物方案的反应不同。简而言之，对于大量不同种类的患者群体，没有实用的最佳抗凝治疗方案，这些方案考虑了个体风险因素、药物和方案选择，并实现了对中风的最小风险。获得覆盖不同患者群体的大型综合电子病历(EMR)，再加上新颖的建模和计算模拟，提供了一个前所未有的机会来进行计算机识别和验证最佳的抗粘连蛋白治疗策略。 我们提出了一种新的计算方法，该方法使用来自两个大型电子病历(EMR)数据库的个体患者数据和结果证据来进行并排临床模拟，比较两种或两种以上抗凝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基于电子病历的模拟数据，以反映电子病历人群的统计和个体特征。然后，我们应用先进的治疗模拟方法来预测多种药物剂量方案的结果和成本。最后，我们应用优化方法来确定人群部分(例如非裔美国人部分、50岁以上的白人女性部分等)的最优治疗计划。最后，我们将对预测的最优防凝进行稳健性和有效性的电子测试 治疗计划。这种方法有望提供第一个环境，在这种环境中，可以计算、比较和对比基于现有EMR数据集的并行抗凝血临床模拟和整个人群的结果预测。然后，这些预测性证据可以用于指导临床试验设计，并建议改善医院范围的抗凝治疗计划。

DESCRIPTION (provided by applicant): 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 bet 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 anticlottin 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.