Using AI on Routine Clinical and Imaging Data from Acute Stroke Encounter to Predict Post-Stroke Vascular Contributions to Cognitive Impairment (AI - RESPECT)

Project Summary Poststroke cognitive impairment (PSCI) was found to be common in various research studies. PSCI is ideally recognized through cognitive screening and test but they are not standard clinical practices and hence stroke recovery and prevention of recurrent strokes may be undermined by concurrent but poorly recognized cognitive issues, e.g., patient compliance to follow blood pressure control medication may be poorer among those with PSCI. Therefore, a significant unmet need for optimizing poststroke care is to recognize patients at high risk of PSCI to tailor for them an appropriate stroke recovery and recurrent stroke prevention strategy. With many of the plausible determinants of PSCI being available in electronic health record (EHR) systems, machine learning (ML) methods to process routine clinical data to predict risk of PSCI is highly feasible. In the parent project, we will combine a large retrospective dataset from EHR and a smaller prospective dataset with more accurate ascertainment of PSCI based on purposefully administered cognitive tests, serving as gold- standard. The necessity of prospective cognitive tests to accurately ascertain PSCI further allows us to explore biological and physiological variables related to pathologies of Alzheimer disease and related dementia (ADRD). We will pursue three specific aims in the parent project: 1) Learn to predict PSCI using routine neuro images and EHR data from large clinical cohorts; 2) Use prospective data to adapt and validate models learned from existing clinical cohorts; 3) Phenotype PSCI with cognitive tests, physiological, and biological metrics one-year poststroke. In the supplement project, we will conduct APOE genotyping of all prospectively enrolled subjects, measure ~120 protein biomarkers, in addition to pTau217 and NfL, of neuronal injury, amyloid tau pathologies, inflammation, vascular and metabolism, synuclein and synaptic disorders, and neurodegeneration. Our specific aim for the supplement project is to enrich AI approaches to predict and understand post stroke cognitive impairment with APOE genotyping and multi-pathology plasma- based protein biomarkers.