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We identified twelve model development studies and eleven evaluation studies. Investigators often did not report effective sample size, regression coefficients, handling of missing data; typically categorised continuous predictors; and used data dependent methods to build models (e.g., univariate screening of predictors). Four models were evaluated. The pooled area under the receiver operating characteristic curve (AUROCC) estimate for the Essen Stroke Risk Score (ESRS) was 0.60 (95% CI 0.59 to 0.62, ten studies), for the Stroke Prognosis Instrument II (SPI-II) was 0.62 (95% CI 0.60 to 0.64, nine studies) and a single study of the Recurrence Risk Estimator at 90 days (RRE-90) was 0.72 (95% CI 0.56 to 0.88, one study) and of the Life Long After Cerebral ischemia (LiLAC) was 0.65 (95% CI 0.61 to 0.69, one study). Conclusions The available models for recurrent stroke discriminate only modestly between patients with and without a recurrent stroke or MI. Performance may be improved by addressing commonly encountered methodological flaws.

Prediction of recurrent stroke and myocardial infarction after stroke: a systematic review of clinical prediction models