Abstract
Background and Purpose: Accurate prediction of functional outcome after stroke would provide evidence for reasonable post-stroke management. This study aimed to develop a machine learning-based prediction model for 6-month unfavorable functional outcome in Chinese acute ischemic stroke (AIS) patient.Methods: We collected AIS patients at National Advanced Stroke Center of Nanjing First Hospital (China) between September 2016 and March 2019. The unfavorable outcome was defined as modified Rankin Scale score (mRS) 3–6 at 6-month. We developed five machine-learning models (logistic regression, support vector machine, random forest classifier, extreme gradient boosting, and fully-connected deep neural network) and assessed the discriminative performance by the area under the receiver-operating characteristic curve. We also compared them to the Houston Intra-arterial Recanalization Therapy (HIAT) score, the Totaled Health Risks in Vascular Events (THRIVE) score, and the NADE nomogram.Results: A total of 1,735 patients were included into this study, and 541 (31.2%) of them had unfavorable outcomes. Incorporating age, National Institutes of Health Stroke Scale score at admission, premorbid mRS, fasting blood glucose, and creatinine, there were similar predictive performance between our machine-learning models, while they are significantly better than HIAT score, THRIVE score, and NADE nomogram.Conclusions: Compared with the HIAT score, the THRIVE score, and the NADE nomogram, the RFC model can improve the prediction of 6-month outcome in Chinese AIS patients.
Highlights
Stroke is a leading cause of mortality and disability [1]
Incorporating age, National Institutes of Health Stroke Scale score at admission, premorbid modified Rankin Scale score (mRS), fasting blood glucose, and creatinine, there were similar predictive performance between our machine-learning models, while they are significantly better than Houston Intra-arterial Recanalization Therapy (HIAT) score, Totaled Health Risks in Vascular Events (THRIVE) score, and NADE nomogram
Compared with the HIAT score, the THRIVE score, and the NADE nomogram, the random forest classifier (RFC) model can improve the prediction of 6-month outcome in Chinese acute ischemic stroke (AIS) patients
Summary
Stroke is a leading cause of mortality and disability [1]. In developing countries, the prevalence of stroke is increasing as the population ages. Accurate prediction of functional outcome after stroke would provide evidence for reasonable post-stroke management and improve the allocation of health care resources. Several prognostic models have been developed to predict the clinical outcome after stroke, such as Houston Intra-arterial Recanalization Therapy (HIAT) score, Totaled Health Risks in Vascular Events (THRIVE) score and NADE nomogram [3,4,5]. They are generally based on regression model with the assumption of a linear relationship between variables and the outcomes. This study aimed to develop a machine learning-based prediction model for 6-month unfavorable functional outcome in Chinese acute ischemic stroke (AIS) patient
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