A Presurgical Unfavorable Prediction Scale of Endovascular Treatment for Acute Ischemic Stroke

OBJECTIVE: To develop a prognostic prediction model of endovascular treatment (EVT) for acute ischemic stroke (AIS) induced by large-vessel occlusion (LVO), this study applied machine learning classification model light gradient boosting machine (LightGBM) to construct a unique prediction model. METHODS: A total of 973 patients were enrolled, primary outcome was assessed with modified Rankin scale (mRS) at 90 days, and favorable outcome was defined using mRS 0–2 scores. Besides, LightGBM algorithm and logistic regression (LR) were used to construct a prediction model. Then, a prediction scale was further established and verified by both internal data and other external data. RESULTS: A total of 20 presurgical variables were analyzed using LR and LightGBM. The results of LightGBM algorithm indicated that the accuracy and precision of the prediction model were 73.77 and 73.16%, respectively. The area under the curve (AUC) was 0.824. Furthermore, the top 5 variables suggesting unfavorable outcomes were namely admitting blood glucose levels, age, onset to EVT time, onset to hospital time, and National Institutes of Health Stroke Scale (NIHSS) scores (importance = 130.9, 102.6, 96.5, 89.5 and 84.4, respectively). According to AUC, we established the key cutoff points and constructed prediction scale based on their respective weightings. Then, the established prediction scale was verified in raw and external data and the sensitivity was 80.4 and 83.5%, respectively. Finally, scores >3 demonstrated better accuracy in predicting unfavorable outcomes. CONCLUSION: Presurgical prediction scale is feasible and accurate in identifying unfavorable outcomes of AIS after EVT.