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Explainable Machine Learning Analysis of Perioperative Factors Associated with Clinically Significant Emergence
Jung A Lim1, Jonghae Kim2, Minju Kong2
1Department of Anesthesiology and Pain Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea.
Medicina (Kaunas, Lithuania)
|June 26, 2026
Summary
Machine learning models can predict emergence agitation (EA) in children after sevoflurane anesthesia. This study developed an explainable model to identify key risk factors for EA in pediatric ophthalmic surgery patients.
Area of Science:
- Anesthesiology
- Machine Learning
- Pediatric Surgery
Background:
- Emergence agitation (EA) is a frequent complication in pediatric patients recovering from sevoflurane anesthesia, especially after ophthalmic procedures.
- Current risk stratification methods for EA are limited in clinical applicability and validation.
- There is a need for reliable tools to predict individualized EA risk.
Purpose of the Study:
- To develop and internally validate an explainable machine learning model for estimating individualized EA risk in children undergoing ophthalmic surgery.
- To identify key perioperative factors contributing to EA using machine learning interpretability techniques.
Main Methods:
- A retrospective cohort study of 1029 children (3-7 years) undergoing ophthalmic surgery under sevoflurane anesthesia.
- Development and validation of four machine learning algorithms (logistic regression, random forest, XGBoost, CatBoost) using 5-fold cross-validation.
- Performance evaluation included discrimination, calibration, and classification metrics; SHAP analysis was used for interpretability.
Main Results:
- EA occurred in 52.8% of patients.
- The XGBoost model demonstrated strong performance with an AUPRC of 0.827 and sensitivity of 0.796.
- SHAP analysis highlighted airway management and anesthetic-related variables as significant predictors of EA.
Conclusions:
- Machine learning analysis successfully identified perioperative risk factors for EA in pediatric ophthalmic surgery.
- The developed model offers preliminary insights into risk stratification for EA.
- External validation is necessary before clinical implementation of the model.
