Abstract Introduction To develop and evaluate a predictive model to risk-stratify pediatric patients with severe obstructive sleep apnea (OSA) undergoing adenotonsillectomy, identifying those requiring post-operative ICU stay based on pre-surgical characteristics and polysomnogram (PSG) parameters. Methods A retrospective review of 352 pediatric patients ( 18 years) who underwent adenotonsillectomy with an apnea-hypopnea index (AHI) 40 on overnight PSG between 2021 and 2024 at a single children’s hospital was conducted. Four machine learning models (Logistic Regression, Decision Tree, Random Forest, and XGBoost) were developed to predict post-operative ICU admission. Data was split into training (80%) and testing (20%) sets. A pipeline was created for preprocessing, including scaling, log transformation of continuous variables, and subsequent oversampling via SMOTE to address class imbalance. Hyperparameters were optimized via randomized search with 5-fold cross-validation targeting recall. Model performance was assessed using area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (PR-AUC), Recall, F1-score, Accuracy, and Precision. Feature importance and odds ratios were examined for interpretability. Results In a 72-patient test-cohort (8 ICU and 64 non-ICU), logistic regression identified higher OAHI (OR=167.65, 95%CI 5.88–4775.52, p=0.003) and a positive respiratory viral panel (OR=22.35, 95%CI 4.47–111.64, p 0.001) as risk factors for ICU stay. Protective factors included higher nadir oxygen saturation (SpO2 nadir; OR=0.08, 95%CI 0.01–0.55, p=0.01) and older age (OR=0.11, 95%CI 0.02–0.64, p=0.014). BMI-Z score was not a significant predictor (OR 3.19, 95%CI 0.20-51.75, p=0.41). Random Forest and Logistic Regression were top performers with AUC of 0.88 and 0.86, respectively. Random Forest was the best performing model (recall 75%; F1-score 0.48), followed by logistic regression (recall 75%; F1-score 0.41). Conclusion This pilot study validates the feasibility of using machine learning to risk-stratify pediatric OSA patients for post-operative ICU admission based on demographic and PSG predictors. The high recall and transparent explainability are key factors for fostering clinical trust. As a proof of concept, this work establishes a baseline, though predictive power is limited by the dataset size. Future models trained on larger multi-institutional datasets and validated in randomized trials may offer improved performance and generalizability, serving as tools to augment clinical decision making. Support (if any)
Advano et al. (Fri,) studied this question.