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In Annals of neurology ; h5-index 85.0

OBJECTIVE : Machine learning (ML) algorithms have emerged as powerful predictive tools in the field stroke. Here, we examine the predictive accuracy of ML models for predicting functional outcomes using 24-hour post-treatment characteristics in the Systematic Evaluation of Patients Treated With Neurothrombectomy Devices for Acute Ischemic Stroke (STRATIS) Registry.

METHODS : ML models, adaptive boost, random forest (RF), classification and regression tree (CART), C5.0 decision tree (C5.0), support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and logistic regression (LR), and traditional LR models were used to predict 90-day functional outcome (modified Rankin Scale score 0-2). Twenty-four-hour National Institutes of Health Stroke Scale (NIHSS) was examined as a continuous or dichotomous variable in all models. Model accuracy was assessed using the area under characteristic curve (AUC).

RESULTS : The 24-Hour NIHSS score was a top-predictor of functional outcome in all models. ML models using the continuous 24-hour NIHSS scored showed moderate-to-good predictive performance (range mean AUC:0.76-0.92); however, RF (AUC:0.92±0.028) outperformed all ML models, except LASSO (AUC:0.89±0.023, p=0.0958). Importantly, RF demonstrated a significantly higher predictive value than LR (AUC:0.87±0.031, p=0.048) and traditional LR (AUC:85±0.06, p=0.035) when utilizing the 24-hour continuous NIHSS score. Predictive accuracy was similar between the 24-hour NIHSS score dichotomous and continuous ML models.

INTERPRETATION : In this substudy, we found similar predictive accuracy for functional outcome when utilizing the 24-hr NIHSS score as a continuous or dichotomous variable in ML models. ML models had moderate-to-good predictive accuracy, with RF outperforming LR models. External validation of these ML models is warranted. This article is protected by copyright. All rights reserved.

Castonguay Alicia C, Zoghi Zeinab, Zaidat Osama O, Burgess Richard, Zaidi Syed F, Mueller-Kronast Nils, Liebeskind David S, Jumaa Mouhammad A