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

ABSTRACT:

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 2

SUBMITTER: Maroufi SS 

PROVIDER: S-EPMC11934757 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature