New strategy for clinical etiologic diagnosis of acute ischemic stroke and blood biomarker discovery based on machine learning.

Abstract

Acute ischemic stroke (AIS) is a syndrome characterized by high morbidity, prevalence, mortality, recurrence and disability. The longer the delay before proper treatment of a stroke, the greater the likelihood of brain damage and disability. Computed tomography and nuclear magnetic resonance are the primary choices for fast diagnosis of AIS in the early stage, which can provide certain information about infarction location and degree, and even the vascular distribution of lesions responsible for strokes. However, this is quite difficult to achieve in small clinics or at-home diagnoses. Hematology tests could quickly obtain a large number of pathology-related indicators, and offer an effective method for rapid AIS diagnosis when combined with the machine learning technique. To explore a reliable, predictable method for early clinical etiologic diagnosis of AIS, a retrospective study was deployed on 456 AIS patients at the early stage and 28 reference subjects without the symptoms of AIS, by means of the selected significant traits amongst 64 clinical and blood traits in conjunction with powerful machine learning strategies. Five representative biomarkers were closely related to cardioembolic (CE), 22 to large artery atherosclerosis (LAA), and 15 to small vessel occlusion (SVO) strokes, respectively. With these biomarkers, different etiologic subtypes of stroke patients were determined with high accuracy of >0.73, sensitivity of >0.73, and specificity of >0.70, which was comparable to the accuracy obtained in the emergency department by clinical diagnosis. The proposed method may offer an alternative strategy for the etiologic diagnosis of AIS at the early stage when integrating significant blood traits into machine learning.