Predictive optimal anticlotting treatment for segmented patient populations

针对细分患者群体的预测性最佳抗凝血治疗

• 批准号: 8913774
• 负责人: Peter J. Tonellato
• 金额: $ 24.79万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8913774
• 关键词: Accounting; Adverse event; African American; Age; Algorithms; Anticoagulants; Asians; Bayesian Modeling; Benefits and Risks; Blood coagulation; CYP2C9 gene; Cause of Death; Cessation of life; Characteristics; Clinical; Clinical Trials; Clinical Trials Design; Clinics and Hospitals; Computer Simulation; Computerized Medical Record; Costs and Benefits; Coupled; Data; Data Set; Databases; Deep Vein Thrombosis; Disease; Dose; Drug Combinations; Drug Kinetics; Environment; Face; Failure; Family; Female; Genetic; Healthcare; Hemorrhage; Heterogeneity; Hospitals; Incidence; Individual; Individual Differences; Ischemia; Lead; Medical; Methods; Modeling; Outcome; Patients; Pharmaceutical Preparations; Physiology; Population; Population Heterogeneity; Property; Protocols documentation; Provider; Publishing; Pulmonary Embolism; Race; Recording of previous events; Research; Risk; Risk Assessment; Risk Factors; Serious Adverse Event; Side; Solutions; Stroke; Subgroup; Testing; Thrombosis; Training; Transient Ischemic Attack; Treatment Cost; Treatment Protocols; Validation; Variant; Warfarin; base; clopidogrel; cost; design; efficacy testing; experience; improved; minimal risk; models and simulation; multiple drug use; novel; patient population; pharmacodynamic model; population based; prospective; response; simulation; tool; treatment planning; treatment strategy; validation studies; virtual

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.

描述（由申请人提供）：抗凝血药物可降低血栓形成的风险，并治疗可能导致中风、肺栓塞、深静脉血栓形成或其他凝血相关疾病的疾病。抗凝血药物在美国的影响和价值是戏剧性的。例如，中风是美国第三大死亡原因，每年有超过140，000人死亡。大多数中风发生率是由于缺血（87%）或短暂性脑缺血发作（TIA，~5-10%），并且通常通过使用抗凝剂（例如，华法林和达比加群）和抗血小板药（例如，氯吡格雷）。无论患者的疾病或状况导致抗凝血剂的处方，选择药物和治疗方案的最佳组合都因抗凝血药物反应的个体差异而复杂化，所述个体差异归因于遗传学（例如，华法林的差异>20倍）、生理学和免疫学。 合规在实践中，供应商使用经验，科学证据和临床试验结果的组合来开发抗凝血“最佳实践”治疗计划，旨在大致最大限度地减少患者之间的反应变异性和供应商患者人群的风险。然而，患者的高度异质性导致个体患者对这些“最佳实践”药物方案方法的反应存在差异。简而言之，对于大量异质性患者人群，没有实用的最佳抗凝治疗计划，该计划考虑了个体风险因素;药物和方案选择;并实现了卒中风险最小化。访问涵盖不同患者人群的大型综合电子病历（EMR），加上新的建模和计算模拟，为进行计算机识别和验证最佳anticlottin治疗策略提供了前所未有的机会。 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，进行并排临床模拟，比较两种或多种抗凝药物和剂量方案的结果。该方法首先将EMR数据转换为基于EMR的模拟数据，该模拟数据反映EMR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给药方案的结果和成本。最后，我们应用一种优化方法来确定最佳的治疗计划的人口部分（例如，非洲裔美国人的部分，白色女性超过50段，.）。最后，我们将对预测的最佳抗凝剂的稳健性和验证进行计算机模拟测试 治疗方案这种方法有望提供第一个环境，在该环境中，可以计算、比较和对比基于现有EMR数据集的整个人群的并行抗凝临床模拟和结果预测。这样的预测性证据可以用来指导临床试验设计，并建议改进医院范围内的抗凝血治疗计划。