Predictive optimal anticlotting treatment for segmented patient populations


Principal Investigator: Peter J Tonellato
Abstract: 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.
Funding Period: 2013-09-15 - 2017-08-31
more information: NIH RePORT

Detail Information

Research Grants30

  1. Mechanisms Underlying Chronic Lung Pathology
    Wayne Mitzner; Fiscal Year: 2013
    ..This tightly integrated, synergistic program will thereby provide new insights into the mechanisms that underlie the pathogenic progression of chronic lung diseases. ..
  2. Indiana University Center for Pediatric Pharmacology
    Jamie L Renbarger; Fiscal Year: 2013
    ..The direct outcome of these studies will be new biomarkers and predictive signatures that will increase the precision of the existing dosing schemas used in the treatment of childhood cancer. ..
  3. Hopkins Center for Eliminate Cardiovascular Health Disparities
    Lisa A Cooper; Fiscal Year: 2013
    ..abstract_text> ..
  4. Notch-Mediated Expansion of Cord Blood Progenitors for Stem Cell Transplant
    Colleen Delaney; Fiscal Year: 2013
    ..Our goal is to now determine the clinical efficacy of this approach in a phase II clinical trial. ..