Abstract TP382: A Bayesian Network for Reasoning on Acute Ischemic Stroke Intervention

Abstract

INTRODUCTION: As endovascular treatment options for acute ischemic stroke evolve, it is important to study the conditions and decisions that lead to successful outcomes so that future interventions may be better tailored for each individual. Bayesian networks provide a method for studying the conditional dependencies between variables and have been applied in other areas of medicine. In this work, we present a Bayesian network of acute ischemic stroke for analyzing the dependencies between a patient’s clinical and imaging presentation, therapeutic interventions, and outcomes. METHODS: 790 unique episodes of acute ischemic stroke from the last five years (2006-2012) were retrieved from our institution’s quality improvement repository. A subset of variables from each case was extracted and modeled in a Bayesian network. Variable selection and connectivity was guided by a review of current practice guidelines and the domain knowledge of clinical investigators. Conditional probabilities between variables were then calculated using expectation maximization. RESULTS: The Bayesian network may be manipulated through a graphical user interface to investigate the likelihood of different clinical scenarios. For example, evidence regarding patient presentation may be set and then combinations of interventions may be applied to observe possible outcomes. Additionally, the model accommodates the integration of utility nodes to support exploration of a patient’s projected quality-adjusted life expectancy (QALE) as a function of their clinical state and variable treatment decisions. CONCLUSION: The presented model supports a pertinent set of variables and provides an initial tool for clinicians and researchers to study how combinations of patient evidence and interventions affect outcomes in acute stroke. This information may be useful for suggesting subsequent investigations on methods for improving existing treatment protocols.