Using Radiologic Brain Health to Predict Recurrence After Ischemic Stroke and Transient Ischemic Attack
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background and Objectives: Approximately one-fourth of all strokes are recurrent, which is associated with a higher risk of morbidity/death. Identifying stroke survivors at greatest risk of recurrence could help direct secondary prevention efforts, but prior models based on clinical data have limited predictive ability. Neuroimaging markers of brain health may be uniquely able to capture the duration and severity of traditional and nontraditional risk factors for recurrence, and we examined whether they could improve the prediction of stroke recurrence beyond clinical factors alone using advanced machine-learning techniques. Methods: Using a well-validated approach, all ischemic strokes and TIAs during 2015 were ascertained in a representative population of the United States. Standard-of-care neuroimaging was collected for all patients across 18 different hospitals and centrally read/analyzed for more than 20 different measures of brain health. Only patients who underwent standard of care MRI were included. Patients were then followed for 3 years to assess for stroke recurrence. Using both clinical and neuroimaging variables, prediction models of recurrence at 90-days and 3-years were built using random survival forests. Results: 0.001).Among imaging variables, global cortical atrophy scale, microbleed count, deep white matter hyperintensities (WMH), and baseline CT ASPECTS were highly important variables at both time points. Discussion: Neuroimaging data can improve the prediction of stroke recurrence but only moderately improves discriminative ability. Other strategies will be needed to better identify stroke patients at highest risk of recurrence, including dynamic prediction models or other non-imaging biomarkers.
Source: Neurology Open Access
Authors: David Robinson, Pooja Khatri, Heidi Sucharew, Brady Williamson, Vivek Khandwala, Thomas C. Maloney, Lily Wang, Mary Gaskill-Shipley, Rebecca Cornelius, Thomas Tomsick, David Wang, Paul S. Horn, Shantala Gangatirkar, Janice Carrozzella, Robert J. Stanton, Dawn Kleindorfer, Brett Kissela, Achala Vagal
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7164161082