Key result
Machine learning combined with clinical and blood traits identified etiologic subtypes of acute ischemic stroke with >0.73 accuracy, >0.73 sensitivity, and >0.70 specificity.
Why the study?
Imaging is primary for early acute ischemic stroke diagnosis but difficult in small clinics or at-home settings, prompting exploration of machine learning and blood biomarkers for early etiologic diagnosis.
Does a machine learning model using blood biomarkers accurately diagnose the etiologic subtype of acute ischemic stroke in early-stage patients?
Observational (n=484)
Does a machine learning model using blood biomarkers accurately diagnose the etiologic subtype of acute ischemic stroke in early-stage patients?
A machine learning approach using blood biomarkers can predict the etiologic subtype of acute ischemic stroke with high accuracy, offering a potential alternative for early diagnosis.
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.
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Zhang et al. (2022) conducted an observational in Acute ischemic stroke (AIS) (n=484). Machine learning combined with clinical and blood traits vs. Clinical diagnosis was evaluated on Determination of different etiologic subtypes of stroke patients. Machine learning combined with clinical and blood traits identified etiologic subtypes of acute ischemic stroke with >0.73 accuracy, >0.73 sensitivity, and >0.70 specificity.
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