Machine Learning-Based Model for Predicting Incidence and Severity of Acute Ischemic Stroke in Anterior Circulation Large Vessel Occlusion

Abstract

Objectives: Patients with anterior circulation large vessel occlusion 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 anterior circulation large vessel occlusion (AC-LVO), based on medical history and neuroimaging data of patients on admission.Methods: A total of 1,100 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 to AC- LVO and 387 presented with the non-acute ischemic cerebrovascular event. Among patients with the non-acute ischemic cerebrovascular events, 173 with prior stroke or TIA were excluded. Finally, 927 patients with AC-LVO were entered into 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 to AC- LVO and 51 asymptomatic AC-LVO patients, were retrospectively reviewed. We developed four machine learning models [logistic regression (LR), regularized LR (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 area under the receiver operating characteristic curve (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% 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, 254 (53.9%) and 31 (37.8%) 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.

Prognosis of ischemic stroke predicted by machine learning based on multi-modal MRI radiomics

“…The area under the ROC curve for ischemic stroke was 0.929. Cui et al (22) applied ML to develop and validate the incidence and severity of acute ischemic stroke in 1,100 patients. The combination of ML methods (e.g., complex neural networks) with imaging omics seems particularly promising, especially for the identification and segmentation of small lesions (23)(24)(25).…”

Brain frailty associated with stroke events in anterior circulation large artery occlusion

“…Details of the study protocol, baseline characteristics, and main results have been published previously [ 19 , 20 ]. In summary, we retrospectively analyzed 1100 patients with AC-LAO who were consecutively hospitalized at the Second Hospital of Hebei Medical University, North China, between June 2016 and April 2018.…”

An exploration on the machine-learning-based stroke prediction model

“…They can establish risk models by learning from existing medical test or survey data of patients. These models are used for disease prediction ( 12 , 13 ), diagnosis of disease severity ( 14 ), and evaluation of disease prognosis ( 15 , 16 ). Our study explored the value of machine-learning methods for stroke prediction.…”