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Predicting Delayed Extubation After General Anesthesia in Postanesthesia Care Unit Patients Using Machine Learning:

Jianwei Luo1,2, Shaoman Lin1, Liman Wang1

  • 1Department of Anesthesiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 Yanjiang West Road, Guangzhou, 510120, China, 86 020-81332199.

JMIR Medical Informatics
|November 11, 2025
PubMed
Summary

Machine learning models can predict delayed extubation after general anesthesia, improving patient care. The extreme gradient boosting model showed the best performance in identifying patients at risk, aiding clinical decisions.

Keywords:
XGBoostdelayed extubationextreme gradient boostinggeneral anesthesiamachine learning algorithmspostanesthesia care unit

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Area of Science:

  • Anesthesiology and Perioperative Medicine
  • Artificial Intelligence in Healthcare
  • Clinical Informatics

Background:

  • Delayed extubation post-general anesthesia is linked to increased complications, prolonged hospital stays, and higher mortality rates.
  • Current risk assessment methods for delayed extubation often rely on subjective clinical judgment or basic tools.
  • Machine learning (ML) presents an opportunity for real-time risk evaluation, but research using single-algorithm models is limited.

Purpose of the Study:

  • To identify key risk factors associated with delayed extubation in patients undergoing general anesthesia.
  • To develop and validate a robust ML-based prediction model for delayed extubation.

Main Methods:

  • Utilized data from 4779 postanesthesia care unit patients (September 2023 - May 2024).
  • Developed six ML models: k-nearest neighbor, decision tree, extreme gradient boosting (XGBoost), random forest, light gradient boosting machine, and artificial neural network.
  • Assessed model performance using AUC, sensitivity, specificity, accuracy, F1-score, Brier score, and calibration curves; Hosmer-Lemeshow test evaluated goodness-of-fit.

Main Results:

  • The XGBoost model achieved the highest performance with an Area Under the Curve (AUC) of 0.750 (95% CI 0.703-0.796).
  • Achieved sensitivity of 0.734 (95% CI 0.635-0.827) and specificity of 0.647 (95% CI 0.623-0.673).
  • Model calibration was acceptable (Brier score: 0.0505) with good fit (Hosmer-Lemeshow test: P=.287).

Conclusions:

  • Developed ML models effectively identify patients at risk for delayed extubation.
  • These models support personalized clinical decision-making and optimize resource allocation in postanesthesia care units.
  • Facilitates proactive management to mitigate complications associated with delayed extubation.