Assessing Population-Based Radiological Brain Health in Stroke Epidemiology (APRISE): Rationale and Design.

Abstract

Introduction: Approximately 20% of strokes in the United States are preceded by either a stroke or transient ischemic attack (TIA). Determining which stroke patients are at higher risk for recurrence allows for individualized, aggressive secondary stroke prevention. A comprehensive clinical decision tool, considering the full spectrum of radiological brain health" including small vessel disease parameters, is currently lacking. Furthermore, large-scale characterization of pre-existing radiological brain health may elucidate novel phenotypes. This study aims (1) to characterize imaging manifestations of brain health at a population level, and associated demographic and clinical risk factors at the time of index stroke and (2) to create a 90-day and three-year prediction models of cerebrovascular disease recurrence (ischemic or hemorrhagic stroke) incorporating comprehensive parameters from routine clinical imaging.

Methods: Our overall cohort was estimated to consist of 4250 patients hospitalized with stroke, including 525 with hemorrhagic and 3725 with ischemic/TIA subtypes, ascertained in the Greater Cincinnati/Northern Kentucky Stroke Study (GCNKSS) population of 1.4 million residents from January 1, 2015 through December 31, 2015. Among 3725 ischemic stroke/TIA patients, based on published and ongoing data collection, we estimated that approximately 16% will have a recurrent ischemic or hemorrhagic stroke over the subsequent three years. Among these, 80% were estimated to have MR imaging for review. Leveraging extensive clinical and demographic data already collected in the 2015 NIH-funded GCKNSS study, we will have obtained and centrally characterized magnetic resonance imaging (MRI), acute CT, and vascular data in patients with hospitalized stroke/TIAs. We will determine if and how pre-existing imaging parameters cluster using factor analysis, and identify associated demographic and clinical risk factors in multivariable modeling. We will develop short term (90-day) and long term (three-year) risk prediction models using the machine learning approach of random survival forest with internal validation, and perform Cox regression models as a sensitivity analysis.

Conclusion: The primary outcome is recurrence defined as any stroke (ischemic or hemorrhagic) occurring after index ischemic stroke or TIA event. For index ischemic strokes, the second event must within a different vascular territory if <14 days from the index event.