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

What the fuck good does this prediction do for getting survivors recovered? Survivors don't want predictions of failure to recover. You tell them EXACT STROKE PROTOCOLS LEADING TO 100% RECOVERY. Anything less is useless.

 

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

Junzhao Cui^1†, Jingyi Yang^2†, Kun Zhang¹, Guodong Xu³, Ruijie Zhao⁴, Xipeng Li⁴, Luji Liu¹, Yipu Zhu¹, Lixia Zhou⁵, Ping Yu¹, Lei Xu¹, Tong Li¹, Jing Tian¹, Pandi Zhao¹, Si Yuan¹, Qisong Wang¹, Li Guo¹ and Xiaoyun Liu^1,6*

• ¹Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, China
• ²Department of Information Center, The Second Hospital of Hebei Medical University, Shijiazhuang, China
• ³Department of Neurology, Hebei Province People's Hospital, Shijiazhuang, China
• ⁴Department of Neurology, Xingtai People's Hospital, Xingtai, China
• ⁵Department of Medical Iconography, The Second Hospital of Hebei Medical University, Shijiazhuang, China
• ⁶Neuroscience Research Center, Medicine and Health Institute, Hebei Medical University, Shijiazhuang, China

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.

Introduction

Acute ischemic stroke caused by large vessel occlusion accounts for more than 40% of cases, ~80% of which occurs in the anterior circulation (1). Compared to non-large vessel occlusion (LVO) acute ischemic stroke (AIS), patients with anterior circulation large vessel occlusion (AC-LVO) stroke are considered to be at greater risk of mortality or disability before endovascular treatment (2). They tend to improve significantly after mechanical thrombectomy (3, 4). Previously reported prediction models for AC-LVO stroke such as prehospital scales (Prehospital Acute Stroke Severity scale, PASS; Cincinnati Prehospital Stroke Severity Scale, CPSSS; stroke Vision Aphasia Neglect, VAN; Rapid Arterial Occlusion Evaluation scale RACE and Field Assessment Stroke Tri-age for Emergency Destination, FAST-ED) (5–9) that are based on NIHSS, and the recently proposed model by Philipp Hendrix et al., which combines past medical history and neurologic examination (10), have focused on the identification of large vessel occlusion in patients with AIS. The main clinical purpose of the prediction scores is to identify which patients with AIS have LVO so that they can be referred to capable centers for endovascular treatment (EVT). However, accurate prediction of AIS in patients with AC-LVO remains a challenge.

Anterior circulation-LVO stroke can be further divided based on pathogenesis and severity of clinical consequences, into non-disabling and disabling stroke with the latter frequently resulting in post-stroke dependence. Nevertheless, no previous studies have predicted the risk of disabling ischemic stroke in patients with AC-LVO, which may be useful in treatment decisions and prevention.

In this study, we developed and validated two models based on machine learning algorithms with clinical variables, to predict acute ischemic stroke (Model 1) and severity of neurological impairment (Model 2) in patients with AC-LVO.

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