Axford, Daniel

Abstract Aims We developed new machine learning (ML) models and externally validated existing statistical models [ischaemic stroke predictive risk score (iScore) and totalled health risks in vascular events (THRIVE) scores] for predicting the composite of recurrent stroke or all-cause mortality at 90 days and at 3 years after hospitalization for first acute ischaemic stroke (AIS). Methods and results In adults hospitalized with AIS from January 2005 to November 2016, with follow-up until November 2019, we developed three ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBOOST)] and externally validated the iScore and THRIVE scores for predicting the composite outcomes after AIS hospitalization, using data from 721 patients and 90 potential predictor variables. At 90 days and 3 years, 11 and 34% of patients, respectively, reached the composite outcome. For the 90-day prediction, the area under the receiver operating characteristic curve (AUC) was 0.779 for RF, 0.771 for SVM, 0.772 for XGBOOST, 0.720 for iScore, and 0.664 for THRIVE. For 3-year prediction, the AUC was 0.743 for RF, 0.777 for SVM, 0.773 for XGBOOST, 0.710 for iScore, and 0.675 for THRIVE. Conclusion The study provided three ML-based predictive models that achieved good discrimination and clinical usefulness in outcome prediction after AIS and broadened the application of the iScore and THRIVE scoring system for long-term outcome prediction. Our findings warrant comparative analyses of ML and existing statistical method–based risk prediction tools for outcome prediction after AIS in new data sets.

Introduction: Machine learning models (MLM) hold promise for improved risk stratification and to guide personalized care. Hypothesis: MLM would outperform conventional statistical (CS) methods and existing iScore for predicting composite of recurrent stroke or death at 90 days and 3 years after ischemic stroke (IS) Methods: We built 3 MLM (random forest [RF], support vector machine [SVM], and extreme gradient boosting decision trees [XGBoost]) and two CS models (logistic regression [LR] and least absolute shrinkage and selection operator [LASSO]) to predict composite outcome at 90 days or 3 years after AIS in a retrospective cohort of 721 adults. The performance of these models was evaluated against the existing iScore to predict outcomes after AIS. Results: Composite outcome occurred in11% at 90 days and in 34% at 3 years after AIS. Performance metrics for predicting 90-day and 3-year composite outcome were comparable across all study models except SM LR. Measurements for the area under the receiver operating characteristic curve for each model in validation set was as follow. 1) 90-day prediction: 0.779 for RF, 0.771 for SVM, 0.772 for EXBOOST, 0.613 for LR, 0.811 for LASSO, and 0.720 for iScore; 2) 3-year prediction: 0.743 for RF, 0.777 for SVM, and 0.773 for EXBOOST, 0.724 for LR, 0.790 for LASSO, and 0.710 for iScore. Conclusions: ML-based algorithms did not improve prediction of composite outcome at 90 days or 3 years after AIS compared with CS-based LASSO. iScore was successfully validated and compared broadening its generalizability and applicability.