Validation and Refinement of Scores to Predict Stroke Risk: Prospective Cohort Study.

Background: In China, the "8+2" stroke risk score has been widely used to identify individuals at high risk of stroke, despite insufficient evidence confirming its predictive ability for stroke events. Objective: We aimed to validate the risk score's ability to predict the risk of stroke within a 10-year timeframe in community cohort populations and to optimize the scoring method to improve its predictive accuracy. Methods: By reviewing previous literature to obtain the parameters for constructing the logistic regression model and the Rothman-Keller model, the risk threshold points of the models were determined using a sample of 100,000 participants. For this population-based cohort study, 22,259 community residents were recruited in 2013 from one urban and rural monitoring site in Ningxia, China. The occurrence of stroke was established by a combination of self-reporting and review of hospitalization electronic records (the International Statistical Classification of Diseases and Related Health Problems 10th Revision: I60-63). A logistic regression model and a Rothman-Keller model were used to refine the 8-factor stroke risk score to predict the 10-year stroke risk. The performance of the model was assessed by the area under the receiver operating characteristic curve and net reclassification improvement. Results: The threshold points for low and medium risk in the logistic regression model and the Rothman-Keller model are risk scores of 0.062 and 0.002, respectively. The threshold points for medium and high risk are risk scores of 0.165 and 0.005, respectively. A total of 11,692 community residents aged 40 years or older who met the inclusion criteria completed the 10-year follow-up. According to the "8+2" stroke risk score, the stroke incidence in the low-risk (n=8908), medium-risk (n=1074), and high-risk groups (n=1710) was 4.5%, 14.7%, and 12.3%, respectively. The logistic regression model and the Rothman-Keller model demonstrated significant differences in area under the receiver operating characteristic curve values when compared to the "8+2" stroke risk score (Z=2.60, P=.001; Z=3.47, P=.009, respectively). However, no significant difference was observed between the logistic regression model and the Rothman-Keller model (Z=0.688, P=.49). Relative to the risk score, the absolute net reclassification improvement of the Rothman-Keller model was 0.051 (P=.01) and of the logistic regression model was 0.010 (P=.62). Conclusions: Our study confirmed that the "8+2" stroke risk score does not effectively predict stroke events. But the Rothman-Keller model may enhance the ability to identify individuals at high risk for stroke. Future research should incorporate more specific biomarkers and multimodal imaging features to develop more accurate risk prediction models.