A stroke prediction score in the elderly: validation and Web-based application

Abstract

The objective of this study was to construct a prediction model for predicting stroke in an elderly U.S. population, and to assess the accuracy in this population of other previously published prediction models. The subjects were participants in the Cardiovascular Health Study: 2,495 men and 3,393 women age 65 years and older at baseline, and followed for 6.3 years. Among 5,711 participants free of baseline stroke, 399 strokes occurred. Sex-specific prediction equations were constructed using study variables that were most importantly related to incident stroke: age, systolic blood pressure, diabetes, ECG diagnosis of atrial fibrillation or left ventricular hypertrophy, confirmed history of cardiovascular disease, diabetes, time to walk 15 ft, and serum creatinine. The prediction rule was implemented as a risk score and in a Web-based interactive Java applet. Overall, the model predicted 5-year stroke risks ranging from less than 1 to 59%. The 20% of subjects in the highest predicted risk group had a 5-year actual stroke incidence rate of 15%, while the 20% lowest risk group had a 1% incidence. Risk scores from two other studies performed well in these study participants. Effective discrimination between low and high stroke risk in the elderly was possible in this cohort with data that are easy to obtain. Evaluation of the generalizability and clinical usefulness of this prediction model requires further research.