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Original research
Development and validation of machine learning-based model for mortality prediction in patients with acute basilar artery occlusion receiving endovascular treatment: multicentric cohort analysis
  1. Chang Liu1,
  2. Jiacheng Huang1,2,
  3. Weilin Kong1,2,
  4. Liyuan Chen1,
  5. Jiaxing Song2,
  6. Jie Yang2,
  7. Fengli Li2,
  8. Wenjie Zi2
  1. 1Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
  2. 2Department of Neurology, Xinqiao Hospital, Army Medical University, Chongqing, China
  1. Correspondence to Dr Wenjie Zi, Department of Neurology, Xinqiao Hospital, Army Medical University, Chongqing, China; ziwenjie{at}126.com; Fengli Li, Department of Neurology, Xinqiao Hospital, Army medical university, Chongqing, China; lifengli01{at}163.com

Abstract

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.

  • Stroke
  • Intervention

Data availability statement

No data are available. Data are available from the corresponding author upon reasonable requests.

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Data availability statement

No data are available. Data are available from the corresponding author upon reasonable requests.

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Footnotes

  • 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.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.