Work

A nomogram to predict hypoxemia in the post-anesthesia care unit after laparoscopic bariatric surgery

Year: 2025

Type: article

Abstract: BACKGROUND: Hypoxemia in the post-anesthesia care unit (PACU) is a common and distressing complication following laparoscopic bariatric surgery (LBS). Despite its clinical significance, there remains a lack of effective, integrated predictive models to guide the prevention and management of hypoxemia in the PACU for this patient population. METHODS: In this retrospective single-center study, a total of 1,220 cases were included. The database was randomly divided into training and validation cohorts at a 7:3 ratio. Using least absolute shrinkage and selection operator (LASSO) regression combined with multivariable logistic regression, we identified independent predictors and constructed a nomogram for hypoxemia prediction. The nomogram’s performance was evaluated and validated through assessment of the area under the receiver operating characteristic curve (AUC), calculation of the concordance index (C-index), generation of calibration plots, and performance of decision curve analysis (DCA). RESULTS: The study cohort of 1,220 patients was randomly divided into training (n = 854) and validation (n = 366) cohorts. A nomogram prediction model was constructed using five independent predictors: age, BMI, presence of two or more comorbidities, intraoperative opioid use ≥ 60 mg, and use of a neuromuscular blocking (NMB) reversal agent. In the training cohort, the AUC and the optimism-corrected C-index were 0.738 (95% CI 0.701–0.775) and 0.731, while in the validation cohort they were 0.735 (95% CI 0.682–0.789) and 0.715, respectively. Calibration plots demonstrated good agreement between predicted probabilities and actual observations. The DCA results confirmed the clinical utility of the nomogram across a range of threshold probabilities. CONCLUSIONS: We developed and validated a nomogram-based prediction model for postoperative hypoxemia in the PACU among patients undergoing elective LBS. This model integrates five key clinical variables and demonstrates acceptable discriminative ability and good calibration, offering a valuable tool for individualized risk assessment and perioperative management.

Source: BMC Anesthesiology

DOI: 10.1186/s12871-025-03390-7

Authors: Min Wang, Yiting Li, Qingyun Gong, Yifan Xu, Xiaodong Chen, Jingjing Fan