Dose-conditioned machine-learning prediction of a composite target sedation state in patients undergoing propofol-sedated gastrointestinal endoscopy

Abstract

BACKGROUND: During propofol-sedated gastrointestinal endoscopy, adequate sedation must be achieved without compromising hemodynamic stability or oxygenation. We developed and temporally validated dose-conditioned machine-learning models to predict a composite target sedation state. METHODS: This single-center prospective observational study included 1,248 adults. The first 1,000 patients formed the development cohort and the subsequent 248 patients the temporal validation cohort. The composite outcome was assessed after MOAA/S-defined loss of consciousness and before endoscope insertion and required a bispectral index of 40-60, systolic blood pressure ≥80% of baseline, and peripheral oxygen saturation ≥90%. Five models were evaluated using routinely available variables collected before or during initial propofol administration, including the initial propofol dose. RESULTS: The composite target was achieved in 153 patients (15.3%) in the development cohort and 33 (13.3%) in the validation cohort. Validation AUCs ranged from 0.780 to 0.821. Random Forest had the numerically highest AUC (0.821; 95% CI, 0.748-0.889), whereas ExtraTrees had the highest average precision (0.570; 95% CI, 0.388-0.724). Calibration was suboptimal across models. With only 33 validation events, confidence intervals were wide and comparisons among models remain uncertain. CONCLUSIONS: Dose-conditioned machine-learning models showed moderate-to-good discrimination, but calibration remained suboptimal. No single model was uniformly superior. Further recalibration and independent multicenter validation are needed before clinical use.