Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion - Cui, Junzhao; Yang, Jingyi; Zhang, Kun; Xu, Guodong; Zhao, Ruijie; Li, Xipeng; Liu, Luji; Zhu, Yipu; Zhou, Lixia; Yu, Ping; Xu, Lei; Li, Tong; et al. (Frontiers in Neurology, 2021)
Objectives: Patients with anterior circulation large vessel occlusion (AC-LVO) are at high risk of acute ischemic stroke, which could be disabling or fatal. In this study, we applied machine learning to develop and validate two prediction models for acute ischemic stroke (model 1) and severity of neurological impairment (model 2), both caused by AC-LVO. Methods: A total of 1100 patients with AC- LVO from the Second Hospital of Hebei Medical University in North China were enrolled, of which 713 patients presented with acute ischemic stroke (AIS) related with AC- LVO and 387 presented with non-acute ischemic cerebrovascular event. Among patients with non-acute ischemic cerebrovascular event, 173 with prior stroke or TIA were excluded. Finally, 927 patients with AC-LVO were entered into in the derivation cohort. In the external validation cohort, 150 patients with AC-LVO from the Hebei Province People’s Hospital, including 99 patients with AIS related with AC- LVO and 51 asymptomatic AC-LVO patients, were retrospectively reviewed. We developed four machine learning models [logistic regression (LR), regularized logistic regression (RLR), support vector machine (SVM), and random forest (RF)], whose performance was internally validated using 5-fold cross-validation. The performance of each machine learning model for the ROC-AUC was compared and the variables of each algorithm were ranked. Results: In model 1, among the included patients with AC-LVO, 713 (76.9%) and 99 (66%) suffered an acute ischemic stroke in the derivation and external validation cohorts, respectively. The ROC-AUC of LR, RLR and SVM were significantly higher than that of the RF in the external validation cohorts [0.66 (95% confidence interval (CI) 0.57-0.74) for LR, 0.66 (95% CI 0.57-0.74) for RLR, 0.55 (95% CI 0.45-0.64) for RF and 0.67 (95% CI 0.58-0.76) for SVM]. In model 2, 217 (46.1%) and 51 (62.2%) patients suffered disabling ischemic stroke in the derivation and external validation cohorts, respectively. There was no difference in AUC among the four machine learning algorithms in the external validation cohorts. Conclusions: Machine learning methods with multiple clinical variables have the ability to predict acute ischemic stroke and the severity of neurological impairment in patients with AC-LVO.