Jingwei Li1,2,3,4,5, Wencheng Zhu6, Junshan Zhou7, Wenwei Yun8, Xiaobo Li9, Qiaochu Guan1, Weiping Lv1, Yue Cheng1, Huanyu Ni10, Ziyi Xie1, Mengyun Li1, Lu Zhang1, Yun Xu1,2,3,4,5, Qingxiu Zhang1,2,3,4,5. 1. Department of Neurology of Drum Tower Hospital, Medical School and the State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing, China. 2. Institute of Brain Sciences, Nanjing University, Nanjing, China. 3. Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China. 4. Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China. 5. Nanjing Neurology Clinic Medical Center, Nanjing, China. 6. The Institute of Software, Chinese Academy of Sciences, Beijing, China. 7. Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China. 8. Department of Neurology, Changzhou No.2 People's Hospital Affiliated to Nanjing Medical University, Changzhou, China. 9. Department of Neurology, Northern Jiangsu People's Hospital, Clinical Medical School of Yangzhou University, Yangzhou, China. 10. Department of Pharmacy of Drum Tower Hospital, Medical School, Nanjing University, Nanjing, China.
Abstract
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.
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.
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Authors: Romain Bourcier; Mayank Goyal; David S Liebeskind; Keith W Muir; Hubert Desal; Adnan H Siddiqui; Diederik W J Dippel; Charles B Majoie; Wim H van Zwam; Tudor G Jovin; Elad I Levy; Peter J Mitchell; Olvert A Berkhemer; Stephen M Davis; Imad Derraz; Geoffrey A Donnan; Andrew M Demchuk; Robert J van Oostenbrugge; Michael Kelly; Yvo B Roos; Reza Jahan; Aad van der Lugt; Marieke Sprengers; Stephane Velasco; Geert J Lycklama À Nijeholt; Wagih Ben Hassen; Paul Burns; Scott Brown; Emmanuel Chabert; Timo Krings; Hana Choe; Christian Weimar; Bruce C V Campbell; Gary A Ford; Marc Ribo; Phil White; Geoffrey C Cloud; Luis San Roman; Antoni Davalos; Olivier Naggara; Michael D Hill; Serge Bracard Journal: JAMA Neurol Date: 2019-04-01 Impact factor: 18.302