Abstract
Accurate prediction of tracheostomy after craniotomy for supratentorial intracerebral hemorrhage (sICH) remains challenging. This study aimed to develop, externally validate, and interpret a machine learning model for individualized risk prediction. A retrospective multicenter cohort was constructed, including 738 patients from Weifang People’s Hospital and 186 from Weifang Hospital of Traditional Chinese Medicine who underwent craniotomy between January 2017 and December 2024. Predictor variables were screened using least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression. Logistic regression, random forest, and extreme gradient boosting (XGBoost) models were trained with repeated 10-fold cross-validation and assessed for discrimination, calibration, and clinical utility. Five key predictors were identified: Glasgow Coma Scale, age, hematoma volume, operative time, and serum bicarbonate. In external validation, XGBoost demonstrated the most balanced and robust performance, with an AUROC of 0.86 and a Brier score of 0.15, and showed superior net benefit on decision curve analysis. SHapley Additive exPlanations confirmed clinical plausibility, and a web-based dynamic nomogram was developed for individualized prediction. This explainable XGBoost model provides reliable and interpretable estimation of postoperative tracheostomy risk, facilitating evidence-based perioperative decision-making and resource allocation in neurocritical care.
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Data availability
The datasets analyzed during the current study are not publicly available due to institutional data-use regulations and patient confidentiality policies. De-identified data and analytic code are available from the corresponding author upon reasonable request and approval by the participating institutions.
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Acknowledgements
The authors sincerely thank the Department of Neurosurgery teams at Weifang People’s Hospital and Weifang Hospital of Traditional Chinese Medicine for their valuable assistance in data management and case verification.
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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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F.Q. and Q.Li conceived and designed the study. F.Q. and X.X. collected and curated the clinical data. F.Q. performed data preprocessing, feature selection, model development, visualization, and web deployment. F.Q. drafted the manuscript. H.Y., Y.C., D.T., Y.W., and Q.Liu contributed to manuscript revision. Q.Li supervised the project, interpreted the findings, and critically revised the manuscript. All authors reviewed and approved the final version of the manuscript.
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Qiao, F., Xue, X., Yu, H. et al. Explainable machine learning prediction of tracheostomy after craniotomy for supratentorial intracerebral hemorrhage. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41953-x
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DOI: https://doi.org/10.1038/s41598-026-41953-x
