Predicting major neurologic improvement and long-term outcome after thrombolysis using artificial neural networks

Objective: To develop artificial neural network (ANN)-based functional outcome prediction models for patients with acute ischemic stroke (AIS) receiving intravenous thrombolysis based on immediate pretreatment parameters. Methods: The derived cohort consisted of 196 patients with AIS treated with intravenous thrombolysis between 2009 and 2017 at Shuang Ho Hospital in Taiwan. We evaluated the predictive value of parameters associated with major neurologic improvement (MNI) at 24 h after thrombolysis as well as the 3-month outcome. ANN models were applied for outcome prediction. The generalizability of the model was assessed through 5-fold cross-validation. The performance of the models was assessed according to the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), Results: The parameters associated with MNI were blood pressure (BP), heart rate, glucose level, consciousness level, National Institutes of Health Stroke Scale (NIHSS) score, and history of diabetes mellitus (DM). The parameters associated with the 3-month outcome were age, consciousness level, BP, glucose level, hemoglobin A1c, history of DM, stroke subtype, and NIHSS score. After adequate training, ANN Model 1 to predict MNI achieved an AUC of 0.944. Accuracy, sensitivity, and specificity were 94.6%, 89.8%, and 95.9%, respectively. ANN Model 2 to predict the 3-month outcome achieved an AUC of 0.933, with accuracy, sensitivity, and specificity of 88.8%, 94.7%, and 86.5%, respectively. Conclusions: The ANN-based models achieved reliable performance to predict MNI and 3-month outcomes after thrombolysis for AIS. The models proposed have clinical value to assist in decision-making, especially when invasive adjuvant strategies are considered.