A machine learning-based prediction model achieved an AUC of 0.732 in predicting postinduction hypotension, identifying propofol dose, preinduction blood pressures, and beta-blocker use as key predictors.
Cohort (n=20,309)
No
An ML-based model using preinduction blood pressures, propofol dose, and beta-blocker use demonstrated moderate predictive capability (AUC 0.732) for postinduction hypotension.
Absolute Event Rate: 0.732% vs 0.639%
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 conducted. The primary outcome was the occurrence of PIH, defined as mean arterial pressure (MAP) < 55 mmHg within 10 min post-induction. Data were split into training and validation sets using k-fold cross-validation. The model's predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC), and feature importance was assessed using SHapley Additive exPlanations (SHAP) values. PIH occurred in 4,948 patients (24.4%). Key predictors included preinduction systolic and mean arterial pressures, propofol dose, and beta-blocker use. The ML model achieved an AUC of 0.732 in predicting PIH. The ML-based model demonstrated significant predictive capability for PIH, identifying key clinical predictors. This model holds the potential for improving preoperative planning and patient risk stratification. However, further validation through prospective studies is necessary to confirm these findings.
Katsin et al. (Mon,) conducted a cohort in Postinduction hypotension (PIH) (n=20,309). Machine learning-based prediction model (Random Forest) vs. Logistic regression model was evaluated on Area under the receiver operating characteristic curve (AUC) for predicting postinduction hypotension. A machine learning-based prediction model achieved an AUC of 0.732 in predicting postinduction hypotension, identifying propofol dose, preinduction blood pressures, and beta-blocker use as key predictors.