Predicting Ischemic Stroke Patients to Transfer for Endovascular Thrombectomy Using Machine Learning: A Case Study

Introduction: Endovascular Thrombectomy (EVT) is highly effective for ischemic stroke patients with a large vessel occlusion. EVT is typically only offered at urban hospitals; therefore, patients are transferred for EVT from hospitals that solely offer thrombolysis. There is uncertainly around patient selection for transfer, which results in a large number of futile transfers. Machine Learning (ML) may be able to provide a model that better predicts patients to transfer for EVT. Methods: Data from Nova Scotia Canada from January 1 2018 to December 31 2022 was used. Four supervised binary classification ML algorithms were applied: logistic regression, decision tree, random forest, and support vector machine. We also applied an ensemble method using the results of these 4 classification algorithms. The data was split into 80% training and 20% testing, and 5-fold cross-validation was employed. Missing data were accounted for by K-Nearest Neighbour’s Algorithm. Model performance was assessed using accuracy, futile transfer rate and false negative rate. Results: 5156 ischemic stroke patients were identified during the time period. After exclusions, a final dataset of 93 patients resulted. The accuracy of logistic regression, decision tree, random forest, support vector machine and ensemble models were 68%, 79%, 74%, 63%, and 68% respectively. The futile transfer rate with random forest and decision was 0% and 18.9% respectively, and the false negative rate was 5.37 and 4.3% respectively Conclusion: ML models can potentially reduce futile transfer rates, but future studies with larger datasets are needed to validate this finding and generalize it to other systems.