Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms

INTRODUCTION: Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is essential to prevent postoperative complications and optimize hospital resource utilization. Premature discharge can lead to severe issues such as respiratory or cardiovascular complications, while delays can strain hospital capacity. Machine learning algorithms offer a promising solution by leveraging large amounts of patient data to predict optimal discharge times. Unlike prior studies relying on statistical models or single-algorithm methods, this research assesses multiple ML models to predict discharge readiness, comparing them against staff evaluations and the Aldrete checklist. METHODOLOGY: We conducted a cross-sectional study of 830 patients under general anesthesia from December 2023 to April 2024, collecting demographics, surgical details, and Aldrete scores. A power analysis ensured statistical robustness, targeting a 5% accuracy improvement (minimum clinically important difference, derived from Gabriel et al., 2017), with variance (SD ≈ 0.1) from pilot data, using a two-sample t-test (power = 0.8, alpha = 0.05), confirming the sample size's adequacy. Two prediction approaches were tested: discharge timing in 15-minute intervals and binary classification (within 15 min or later). Models included Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and XGBoost, assessed via accuracy, precision, recall, F1 score, and AUC. Predictions were benchmarked against staff and Aldrete scores, with 99.5% confidence intervals (CIs) adjusting for multiple comparisons. RESULTS: he RF algorithm showed high performance in both prediction approaches. In the first approach, RF achieved an AUC of 0.75 (99.5% CI: 0.70-0.80) and accuracy of 0.87 (99.5% CI: 0.83-0.91) per staff evaluations, and an AUC of 0.87 (99.5% CI: 0.83-0.91) and accuracy of 0.71 (99.5% CI: 0.66-0.76) per Aldrete scores. In the second approach, RF recorded an AUC of 0.85 (99.5% CI: 0.81-0.89) and accuracy of 0.86 (99.5% CI: 0.82-0.90) per staff evaluations, with ANN also showing strong results (AUC = 0.88, 99.5% CI: 0.84-0.92; accuracy = 0.78, 99.5% CI: 0.74-0.82). Due to overlapping CIs, differences between models were not statistically significant (P >.005). According to the Aldrete checklist, RF, SVM, and ANN exhibited competitive predictive capability, with AUCs ranging from 0.80 to 0.86. CONCLUSION: The strong performance of Random Forest (RF) and Artificial Neural Network (ANN) models in predicting PACU discharge timing upon admission highlights their potential as effective tools for evaluating discharge readiness, as compared to staff assessments and the Aldrete checklist. This study focused on assessing these models, showing their ability to produce consistent predictions, though differences between top models were not statistically significant due to overlapping confidence intervals. Practical application of these findings to improve patient outcomes or hospital efficiency requires further investigation.