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Background <p>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.&#xa0;</p> Methods <p>&#xa0;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).</p> &#xa0;Results <p>The study cohort of 1,220 patients was randomly divided into training (<i>n</i> = 854) and validation (<i>n</i> = 366) cohorts. A nomogram prediction model was constructed using five independent predictors: age, BMI, presence of two or more comorbidities, intraoperative opioid use ≥ 60&#xa0;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.</p> Conclusions <p>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.</p>

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

  • 期刊论文

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  • 发表日期：

    2025年10月17日

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

摘要

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.