Abstract Rationale Propofol-based sedation in the intensive care unit (ICU) is associated with clinically significant hemodynamic instability, including hypotension. Previous studies have identified hypotension as a key predictor of adverse outcomes in critically ill patients. However, no validated tools currently exist to systematically predict this risk. Therefore, this study aimed to develop and externally validate machine-learning (ML) models for predicting the risk of hypotension in ICU patients receiving propofol sedation. Methods We developed ML models using retrospective data from adult, mechanically ventilated ICU patients who received propofol infusion for at least 24 hours across four Mayo Clinic sites between 2018 and 2024. The primary outcome was hypotension, defined as ≥ 2 consecutive MAP readings ≤60 mmHg within 15 minutes during the first 6 hours of propofol sedation. The MIMIC-IV dataset was used for external validation. Features were selected using a union of Boruta and LASSO-penalised logistic regression. Four algorithms (elastic net-penalised logistic regression, random forest, LightGBM, and multilayer perceptron) were tuned using Bayesian optimization to minimize cross-entropy loss during leave-one-site-out cross-validation (LOSO-CV). The algorithm with the lowest cross-entropy loss during LOSO-CV was chosen as the best-performing algorithm. Classification thresholds were selected to target sensitivity ≥80%. Model performance was evaluated using area under the receiver operating characteristic curve (AUC-ROC), calibration slope and intercept, sensitivity, and specificity. Feature importance was evaluated using SHapley Additive exPlanations (SHAP). Results The development cohort included 16,361 patients from Mayo Clinic, of whom 3,482 (21.3%) developed hypotension. The external validation cohort comprised 4,684 patients from MIMIC-IV, with 115 (2.5%) experiencing hypotension. Patient characteristics included age, admission Sequential Organ Failure Assessment (SOFA) scores, starting propofol dose, ICU admission source, and comorbidities. The best-performing algorithm was LightGBM. The average AUC-ROC during LOSO-CV was 0.70 (95% confidence interval CI 0.68-0.71), versus 0.64 during external validation. The average calibration slope and intercept during LOSO-CV was 1.08 (95% CI 0.89-1.27) and 0.08 (95% CI -0.25-0.41), respectively, versus 0.78 and -2.07 during external validation. The average sensitivity and specificity during LOSO-CV was 0.81 (95% CI 0.79-0.83) and 0.47 (95% CI 0.42-0.52), respectively, versus 0.54 and 0.65 during external validation. Conclusions We developed and externally validated an ML-based classifier that demonstrates moderate discrimination for predicting propofol-induced hypotension in mechanically ventilated ICU patients. This tool may facilitate early identification of high-risk patients and enable proactive hemodynamic management strategies. Further prospective validation and assessment of clinical impact are warranted. This abstract is funded by: None
Deng et al. (Fri,) studied this question.