Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100

Receiver operating characteristics (ROCs) of XGB prediction models with clinical features, imaging features, both clinical and imaging features, best-performing features, and SPAN-100 for predicting a 90-day mRS score of >2. For all patients and recanalized and nonrecanalized patients, the AUCs of models with the best-performing features were higher than those in SPAN-100, and statistical significance was reached in the total and nonrecanalized groups. The AUCs for machine learning models with the 6 best-performing features in the total cohort and recanalized and nonrecanalized groups were 0.80, 0.79, and 0.82, respectively. The AUCs for SPAN-100 were 0.78, 0.76, and 0.78, respectively. The AUCs of XGB models with the best-performing features were higher than those in SPAN-100 and reached statistical significance for the total cohort (P < .05) and the nonrecanalized patients (P < .001). In the recanalized group, the difference was not significant (P = .05).