Abstract
OBJECTIVES: To develop a risk assessment model (DAnish REgister Ischaemic Stroke Classifier, DARE-ISC) for predicting 1-year primary ischaemic stroke/systemic embolism (SE) in the general population. Secondly, to validate the accuracy DARE-ISC in atrial fibrillation (AF) patients where well-established models and risk scores exist. DESIGN: Retrospective cohort study. DARE-ISC was developed using gradient boosting decision trees with information from 375 covariates including baseline information on relevant diagnoses, demographic characteristics, registered health-services, lifestyle-related covariates, hereditary stroke components, drug prescriptions and stress proxies. SETTING: Danish nationwide registries. PARTICIPANTS: All Danish individuals aged ≥18 from 2010 to 2017 (n=35 519 348 person-years). The model was trained on the 2010-2016 cohorts with validation in the 2017 cohort. PRIMARY AND SECONDARY OUTCOME MEASURES: Model optimisation and validation were performed through comparison of the area under the receiver operating characteristic curve (AUC) and average precision scores. Additionally, the relative importance of the model covariates was derived using SHAP values. RESULTS: DARE-ISC had an AUC (95% CI) of 0.874 (0.871 to 0.876) in the general population. In AF patients, DARE-ISC was superior to the GARFIELD-AF risk model and CHA(2)DS(2)-VASc score with AUC of 0.779 (95% CI 0.75 to 0.806), 0.704 (95% CI 0.674 to 0.732) and 0.681 (95% CI 0.652 to 0.709), respectively. Furthermore, in AF patients, DARE-ISC had an average threefold and fourfold higher ratio of correctly identified strokes compared with the GARFIELD-AF risk model and CHA(2)DS(2)-VASc score, as indicated by average precision scores of 0.119, 0.041 and 0.034, respectively. CONCLUSIONS: DARE-ISC had a very high stroke prediction accuracy in the general population and was superior to the GARFIELD-AF risk model and CHA(2)DS(2)-VASc score for predicting ischaemic stroke/SE in AF patients.