Background Predicting 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.
Methods The 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.
Results Extreme 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.
Conclusion We 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.
Data availability statement
No data are available. Data are available from the corresponding author upon reasonable requests.
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CL, JH, WK and LC contributed equally.
Contributors CL: Drafting/revision of the manuscript for content, including medical writing for content, major role in the acquisition of data, analysis or interpretation of data. JH: Study concept or design, analysis, or interpretation of data. WK: Study concept or design. JS: Major role in the acquisition of data. JY: Study concept or design, analysis, or interpretation of data. LC: Drafting/revision of the manuscript for content, including medical writing for content, study concept or design. W-JZ and F-LL were responsible for guaranteeing the integrity of the entire study, study design, literature research, statistical analysis, manuscript editing and final approval of this manuscript. All authors contributed to the article and approved the submitted version.
Funding 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).
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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