Decision Making Model on Stroke Prevention: Warfarin or not

An interesting talk I attended at the CORS 2008 conference in Quebec City was by Beste Kucukyazici from the Faculty of Management of McGill University. The topic of the talk was “Designing Antithrombotic Therapy for Stroke Prevention in Atrial Fibrillation”.

Beste Kucukyazici showed the study of stroke patient data to see if a decision model could be derived to systematically decide on the commencing of warfarin treatment for stroke patient and its intensity. Now my question is: will OR decision models take a bigger and bigger foothold in the future of medical arena as we start to gather more useful patient data in well-planned studies? Medical doctors tend to argue that each patient has a different case, and need to be examined on an individual basis. However, if a model such as Kucukyazici’s can prove the accuracy of its decision given real patient data, then it would probably start to weaken the doctor’s argument and favour a more systematic approach. At least, such models might help reduce the complexity of doctor’s decision making process, or even reduce chances for human errors in diagnosis.

Atrial fibrillation, which is a common 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 risk of stroke, however, it also increases the risk of suffering a major bleed. Given the potential benefits and risks of warfarin treatment, the decisions that need to be made by the clinicians are two-fold: (i) whether to start the therapy, and (ii) the intensity of warfarin use. The objective of this study is to develop an analytical framework for designing the optimal antithrombotic therapy with a patient-centered approach. The approach seeks to create a rational framework for evaluating these complex medical decisions by incorporation of complex probabilistic data into informed decision making, the identification of factors influencing such decisions and permitting explicit quantitative comparison of the benefits and risks of different therapies.