Prediction-Driven Decision Support for Patients With Mild Stroke: A Model Based on Machine Learning Algorithms - Lin, Xinping; Lin, Shiteng; Cui, XiaoLi; Zou, Daizun; Jiang, FuPing; Zhou, JunShan; Chen, NiHong; Zhao, Zhihong; Zhang, Juan; Zou, Jianjun (Frontiers in Neurology, 2021)
Background and Purpose: Treatment for mild stroke remains an open question. We aim to develop a decision support tool based on machine learning (ML) algorithms, called DAMS (Disability After Mild Stroke), to identify mild stroke patients who will at high risk of post-stroke disability (PSD) if they only received medical therapy, more importantly, aid neurologists to make an individual clinical decision in emergency contexts. Methods: Ischemic stroke patients were prospectively recorded in the National Advanced Stroke Center of Nanjing First Hospital (China) between July 2016 and September 2020. The exclusion criteria were patients who received thrombolytic therapy, age < 18 years, lack of 3-month modified Rankin Scale (mRS), disabled before the index stroke, with an admission National Institute of Health stroke scale (NIHSS) > 5. The primary outcome was PSD, corresponding to 3-month mRS ≥ 2. We developed five ML models and assessed the area under curve (AUC) of receiver operating characteristic, calibration curve and decision curve analysis. The optimal ML model would be selected to be DAMS. In addition, SHapley Additive exPlanations (SHAP) approach was introduced to rank the feature importance. Finally, rapid-DAMS (R-DAMS) was constructed for a more urgent situation based on DAMS. Results: A total of 1905 mild stroke patients were enrolled in this study, and patients with PSD account for 23.4% (447). There was no difference in AUCs between the five models (ranged from 0.691 to 0.823). Although there was similar discriminative performance between ML models, the support vector machine model exhibited higher net benefit and better calibration (Brier score, 0.159, calibration slope, 0.935, calibration intercept, 0.035). Therefore, this model was selected to be DAMS. In addition, SHAP approach shown that the most crucial feature were NIHSS on admission. Finally, R-DAMS was constructed and there was similar discriminative performance between R-DAMS and DAMS but the former performed worse on calibration. Conclusions: DAMS and R-DAMS, as prediction-driven decision support tools, was designed to aid clinical decision for mild stroke patients in emergency contexts. In addition, even within a narrow range of baseline scores, NIHSS on admission is the strongest feature that contributed to the prediction.