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: 9678754
• 负责人: Peter J. Tonellato
• 金额: $ 23.5万
• 依托单位: UNIVERSITY OF MISSOURI-COLUMBIA
• 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 2018-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9678754
• 关键词: PREDICTIVE; OPTIMAL; ANTICLOTTING; TREATMENT; SEGMENTED

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.

项目摘要 抗凝血药物可降低血栓形成的风险，并治疗可能导致中风、肺动脉高压和其他疾病的疾病。 栓塞、深静脉血栓形成或其他凝血相关疾病。抗凝血的影响和价值 美国的药物治疗是戏剧性的。例如，中风是美国第三大死亡原因， 每年有14万人死亡。大多数中风发生率是由于缺血（87%）或短暂性缺血引起的 发作（TIA，~5-10%），通常通过使用抗凝药物（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如何 导致抗凝血剂的处方，选择药物和治疗方案的最佳组合， 由于遗传学导致的抗凝血药物反应的个体差异（例如>20倍）， 华法林的差异）、生理学和依从性。在实践中，提供者使用经验， 科学证据和临床试验结果，以制定抗凝血“最佳实践”治疗计划， 大致最小化提供者的患者群体中的患者对患者的反应可变性和风险。 然而，患者的高度异质性导致个体患者对这些药物的反应存在差异。 “最佳做法”药物议定书办法。简而言之，没有实际的最佳抗凝血治疗计划存在， 考虑到个体风险因素的大量异质性患者人群;药物和方案选择; 并且实现最小的中风风险。访问大型综合电子病历（EMR） 覆盖不同的患者人群，加上新颖的建模和计算模拟， 进行计算机识别和验证最佳抗凝治疗的前所未有的机会 战略布局 我们提出了一种新的计算方法，使用个体患者数据和来自两个 大型电子病历（EMR）数据库进行并行临床模拟， 两种或多种抗血栓药物和剂量方案的结果。该方法首先将EMR数据转换为EMR- 基于模拟数据，反映了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部分， 人群（例如，非裔美国人群体、50岁以上的白色女性群体.）。最后，我们将在 预测的最佳抗凝治疗计划的稳健性和验证的计算机测试。这种方法， 有望提供第一个环境，其中并排抗凝临床模拟和结果 基于现有EMR数据集的整个人群的预测可以被计算，比较， 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 改善医院范围内的抗凝血治疗计划。

项目成果

A national agenda for the future of pathology in personalized medicine: report of the proceedings of a meeting at the Banbury Conference Center on genome-era pathology, precision diagnostics, and preemptive care: a stakeholder summit.

• DOI: 10.1309/ajcp9gdnlwb4gaci
• 发表时间: 2011-05
• 期刊: American journal of clinical pathology
• 影响因子: 3.5
• 作者: Tonellato PJ;Crawford JM;Boguski MS;Saffitz JE
• 通讯作者: Saffitz JE

The future of genomics in pathology.

• DOI: 10.3410/m4-14
• 发表时间: 2012-01-01
• 期刊: F1000 medicine reports
• 作者: Wall, Dennis P;Tonellato, Peter J
• 通讯作者: Tonellato, Peter J

Using simulation and optimization approach to improve outcome through warfarin precision treatment

使用模拟和优化方法通过华法林精准治疗改善结果

• DOI: 10.1142/9789813235533_0038
• 发表时间: 2018
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: Chih;Lu He;Kourosh Ravvaz;John A. Weissert;P. Tonellato
• 通讯作者: P. Tonellato

Personalized Anticoagulation: Optimizing Warfarin Management Using Genetics and Simulated Clinical Trials.

• DOI: 10.1161/circgenetics.117.001804
• 发表时间: 2017-12
• 期刊: Circulation. Cardiovascular genetics
• 作者: Ravvaz K;Weissert JA;Ruff CT;Chi CL;Tonellato PJ
• 通讯作者: Tonellato PJ