Functional Outcome Prediction in Acute Ischemic Stroke

Abstract

BACKGROUND Stroke is one of the most prevalent neurological diseases and causes of disability worldwide. Functional outcome prediction models can assist the treatment decision process and optimize acute ischemic stroke health care. Current models often use a limited set of input features to predict functional outcome, although combining various types of features could improve model performance. Furthermore, they often incorporate follow-up information, while prediction models applicable in the acute setting are desirable. METHODS We trained an ensemble model consisting of five machine learning models with leave-one-out cross-validation to predict the binarized modified Rankin Scale score three months after stroke onset in patients with acute ischemic stroke caused by a large vessel occlusion who received endovascular treatment. We used clinical variables, treatment variables and lesion loads derived from registration of a stroke population-specific neuroanatomical CT brain atlas with the follow-up non-contrast enhanced CT scan as input features. RESULTS Taking into account five performance metrics (accuracy, AUC, sensitivity, specificity and F1-score), the ensemble model and support vector machine (SVM) seemed to achieve the best performances out of the six models (ensemble model and the five individual machine learning models), with AUC values up to 0.76 and 0.77 respectively. The highest accuracy obtained with the ensemble model was 0.69, and with the SVM 0.72. Little variance in performance was found between the various sets of input features. CONCLUSION Although similar performances compared to current literature were obtained, conventional machine learning models might not be sophisticated enough to capture the complex interactions between input features for functional outcome prediction in acute ischemic stroke.