Development and validation of machine learning-based model for mortality prediction in patients with acute basilar artery occlusion receiving endovascular treatment: multicentric cohort analysis

作者全名："Liu, Chang; Huang, Jiacheng; Kong, Weilin; Chen, Liyuan; Song, Jiaxing; Yang, Jie; Li, Fengli; Zi, Wenjie"

作者地址："[Liu, Chang; Huang, Jiacheng; Kong, Weilin; Chen, Liyuan] Chongqing Med Univ, Affiliated Hosp 2, Dept Neurol, Chongqing, Peoples R China; [Huang, Jiacheng; Kong, Weilin; Song, Jiaxing; Yang, Jie; Li, Fengli; Zi, Wenjie] Army Med Univ, Xinqiao Hosp, Dept Neurol, Chongqing, Peoples R China"

通信作者："Li, FL; Zi, WJ (通讯作者)，Army Med Univ, Xinqiao Hosp, Dept Neurol, Chongqing, Peoples R China."

来源：JOURNAL OF NEUROINTERVENTIONAL SURGERY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000957151700001

JCR分区：Q1

影响因子：4.5

年份：2023

卷号：　

期号：　

开始页：　

结束页：　

文献类型：Article; Early Access

关键词：Stroke; Intervention

摘要："BackgroundPredicting mortality in stroke patients using information available before endovascular treatment (EVT) is an essential component for supporting clinical decision-making. Although the mortality rate of acute basilar artery occlusion (ABAO) after EVT has reached 40%, few studies have focused on predicting mortality in these individuals. Thus, we aimed to develop and validate a machine learning-based mortality prediction tool based on preoperative information for ABAO patients receiving EVT. MethodsThe derivation cohort comprised patients from southern provinces of China in the BASILAR registry. The model (POSITIVE: Predicting mOrtality of baSilar artery occlusion patIents Treated wIth EVT) was trained and optimized using a fivefold cross-validation method in which hyperparameters were selected and fine-tuned. This model was retrospectively tested in patients from the northern provinces of China from the BASILAR registry. A prospective test of POSITIVE was performed on consecutive patients from two hospitals between January 2020 and June 2022. ResultsExtreme gradient boosting was employed to construct the POSITIVE model, which achieved the best predictive performance among the eight machine learning algorithms and showed excellent discrimination (area under the curve (AUC) 0.83, 95% confidence interval (95% CI) 0.80 to 0.87) and calibration (Hosmer-Lemeshow test, P>0.05) in the development cohort. AUC yielded by the POSITIVE model for the retrospective test was 0.79 (95% CI 0.71 to 0.85), higher than that obtained by traditional models. Prospective comparisons showed that the POSITIVE model achieved the highest AUC (0.82, 95% CI 0.74 to 0.90) among all prediction models. ConclusionWe developed a machine learning algorithm and retrospective and prospective testing with multicentric cohorts, which exhibited a solid predictive performance and may act as a convenient reference to guide decision-making for ABAO patients. The POSITIVE model is presented online for user-friendly access."

基金机构："National Natural Science Foundation of China [82071323, 82001264]; Chongqing Technology Innovation and Application Development Project [2022TIAD-KPX0017]"

基金资助正文："This work was supported by National Natural Science Foundation of China (No. 82071323, 82001264) and Chongqing Technology Innovation and Application Development Project (No. 2022TIAD-KPX0017)."