Background and Objectives: The Recurrence Risk Estimator (RRE) is a web-based prognostic score that integrates clinical and imaging information available in the acute setting to quantify early risk of recurrent stroke. The score has been reported to have adequate calibration and good discrimination in the original derivation cohort (AUC=0.81, 95% confidence interval [CI] 0.76-0.87). In the present study, we sought to investigate the validity of RRE score in internal and external datasets. Methods: Internal validation was performed in a prospectively collected dataset from 899 consecutive patients admitted to the Massachusetts General Hospital. External validation was performed in a retrospective stroke cohort of 711 patients from 2 teaching hospitals in Brazil and South Korea. The RRE-90 score was calculated by summing up the number of independent predictors weighted by their corresponding beta-coefficients. (Available at: http://www.nmr.mgh.harvard.edu/RRE-90). We assessed the score’s predictive performance by computing the area under the receiver operating characteristic curve (AUC). Results: Internal and external validation cohorts differed from each other as well as from the original derivation cohort in age, gender, admission NIH stroke scale score, stroke risk factors, and etiologic stroke subtypes. Stroke recurrence rate during the 90-day follow-up period was 6.5% and 2.7% in the internal and external cohorts respectively. The AUC was 0.76 (95% CI 0.68-0.83) in the internal and 0.75 (95% CI 0.66-0.85) in the external validation cohort. Conclusions: RRE score performs well in independent cohorts with different baseline patient characteristics and stroke features. As a reliable, valid, and easy-to-use web-based tool, RRE may find widespread utility in identifying high- and low-risk patients for targeted stroke prevention.
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