Developing a machine learning-based prediction model for postinduction hypotension.

ABSTRACT: Arterial hypotension is a common and often unintended event during surgery under general anesthesia, associated with increased postoperative complications, such as kidney injury, myocardial injury, and stroke. Postinduction hypotension (PIH) is influenced by patient-specific factors, chronic medication use, and anesthetic induction regimens. Traditional predictive models struggle with this complexity, making machine learning (ML) a promising alternative due to its ability to handle complex datasets and identify hidden patterns. This study aimed to develop and validate an ML-based model for predicting PIH and identifying key clinical predictors. A retrospective cohort study of 20,309 adult patients undergoing non-obstetric surgery under general anesthesia with intravenous induction was cond

SUBMITTER: Katsin M 

PROVIDER: S-EPMC12474600 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature