Faculty of Engineering and Natural Sciences
Designing Optimal Therapy for Stroke Prevention in Atrial Fibrillation
Beste Küçükyazıcı, McGill University
Atrial fibrillation (AF), which is an arrhythmia particularly common among the elderly, is one of the major independent risk factors of stroke. Several randomized control trials have shown that long-term antithrombotic therapy with warfarin significantly reduces the relative risk of stroke from AF. However, there are significant disadvantages to its use: cost, inconvenience, i.e., changing life style, and, most importantly, a 1% to 10% per year risk of major bleeding. Less aggressive therapies such as aspirin are often considered as an alternative to warfarin. Although, the risk of bleeding under these therapies are relatively lower, they are only associated with a limited stroke risk reduction. Given the advantages and disadvantages of such therapies, determining the optimal groups of patients to prescribe these therapies play important roles in alleviating the adverse outcomes. Although there are a number of general guidelines for stroke prevention in AF, the detailed and complex medical decisions are currently made, to a large degree, based on the experience and the intuition of the physicians.
The objective of this research is to develop an analytical framework, based on a stochastic dynamic programming model, for choosing the optimal therapy for a patient with specific case-mix and clinical variables. Our framework incorporates the probabilistic data into informed decision making, identifies the factors influencing such decisions and permits the explicit quantitative comparison of the benefits and risks of different therapies, with a patient-centered approach. This research is conducted with collaboration of Neurology and Hemorrhagic Departments of Montreal Jewish General Hospital and the analytical model was run with the clinical data of 650+ patients obtained by reading their patient charts. Our results show that applying such an analytical approach to this decision process can improve the quality adjusted life year outcomes up to 37%.
Beste Kucukyazici is a Ph.D. candidate in Management Science at McGill’s Desautels Faculty of Management and a visiting scholar at Columbia Graduate Business School. She has a B.Sc. in Industrial Engineering and M.Sc. in System Engineering. She also participated as a practitioner trainee in the CIHR Health Informatics PhD/Postdoc Strategic Training Program (CHPSTP). Ms. Kucukyazici’s research interest is on decision-making problems under uncertainty with special interest in the health care supply chain, health care and service operations, and medical decision making. She is particularly interested in the mathematical modeling and analysis of such problems by employing the methodologies of Markov decision processes, stochastic analytical models, simulation and biostatistics. Ms. Kucukyazici has published papers and papers under review in leading operations research and medical journals. She is also the recipient of the Principle’s Dissertation Award of McGill University, silver award in National Poster Competition of Canada and a honorable mention of INFORMS Bonder Scholarship in 2007 on applied research in health services.
Tuesday, 30 December 2008, 17:40-18:40, Place: FENS G032