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Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors’ engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individual risk of stroke recurrence.

We analyzed clinical and socioeconomic data from a prospectively collected public health care–based data set of 41 975 patients admitted with stroke diagnosis in 88 public health centers over 6 years (2014–2020) in Catalonia-Spain. A new stroke diagnosis at least 24 hours after the index event was considered as a recurrent stroke, which was considered as our outcome of interest. We trained several supervised machine learning models to provide individualized risk over time and compared them with a Cox regression model. Models were trained to predict early, late, and long-term recurrence risk, within 90, 91 to 365, and …