Dynamic Risk Prediction of Life-Threatening Mass Effect After Ischemic Stroke

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PROJECT SUMMARY/ABSTRACT Dr. Ong is a neurologist and young investigator pursuing patient-oriented clinical research. For this K-23 submission, she will develop a novel framework for dynamic risk prediction of life-threatening mass effect after ischemic stroke using both baseline and longitudinal variables through the first 120 hours after admission. A K23 award will provide her with the means to acquire critical career development skills that will enable her to execute and build upon her project including: 1) dynamic risk prediction and trajectory analysis; 2) multi-modal methods of neuroprognostication; 3) clinical trial design; and 4) professional development. These objectives will help Dr. Ong to achieve her long-term career goal of becoming an independent clinical investigator of data- driven tools that support clinical decision making and optimize outcomes after acute brain injury. Dr. Ong has recruited a multi-disciplinary mentorship team to assist her in executing her project and achieving scientific independence. She will be co-mentored by Dr. David Greer, an R01 funded clinician scientist with expertise in neurocritical care, neuroprognostication, and clinical study design, and Dr. Emelia Benjamin, an R01 funded leading cerebrovascular epidemiologist and risk prediction specialist with extensive mentorship experience. Dr. Josée Dupuis, Chair of Biostatistics at Boston University School of Public Health, will serve as a methodologic mentor overseeing Dr. Ong’s progress in dynamic modeling strategies. Dr. Ong’s overarching hypothesis is that dynamic risk models that update their predictions with newly available longitudinal data will improve prediction of Life-Threatening Mass Effect and better support clinical decision making in real-time. Aim 1 will use a retrospective medical record dataset of 3000 large stroke patients to identify variables trajectories predictive of radiographic LTME, and use this information to develop updating multivariable dynamic risk models of LTME comprised of baseline and longitudinal variables for the first 120 hours of admission. In Aim 2, she will study the relationship of hourly quantitative pupillometry and LTME through the prospective recruitment of 60 patients with large stroke, and develop an exploratory dynamic multivariable model of LTME using Dr. Ong’s proposed research is significant because improving LTME prediction can facilitate more timely life- and function-sparing interventions. Her research is innovative because she will develop and apply a novel dynamic risk modeling framework to predict secondary injury following ischemic stroke, and study the new promising longitudinal variable, quantitative pupillometry. predictive variables identified in Aim 1 as well as hourly pupillometry data. Her aims, training plan, and interdisciplinary mentorship team will prepare Dr. Ong to become an independent investigator of data-driven tools that support clinical decision making and optimize outcomes after acute brain injury. The anticipated results will be strong preliminary data for a R01 proposal testing the effect of dynamic LTME assessments on time to intervention and outcome in clinical practice.