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Background <p>The objective of this study was to develop and validate a dynamic nomogram for predicting postoperative nausea and vomiting (PONV) following laparoscopic procedures.</p> Methods <p>Clinical data were prospectively collected from adult patients undergoing laparoscopic procedures between March 10, 2025, and May 22, 2025. Patient demographics and clinical characteristics were used to develop a dynamic nomogram for predicting PONV. Variable screening and predictor selection were performed using least absolute shrinkage and selection operator (LASSO) regression, followed by refinement through multivariable logistic regression to construct the nomogram. The area under the curve (AUC) was used to objectively quantify the discriminative ability of the model. Internal validation was performed using bootstrapping, and model performance was further evaluated using calibration and decision curve analysis (DCA).</p> Results <p>Of the 413 patients enrolled, 127 (30.8%) developed PONV within 24&#xa0;h postoperatively. A nomogram incorporating six predictors was developed. The AUC of the prediction model was 0.704 (95% confidence interval [CI]: 0.648‒0.759), and internal validation using bootstrapping was 0.728 (95% CI: 0.674‒0.782). The model demonstrated good calibration, and the DCA revealed a satisfactory net benefit for patients when the probability threshold ranged from 0.12 to 0.54. This indicates the model’s clinical utility for supporting personalized decision-making.</p> Conclusions <p>We developed a dynamic nomogram for PONV risk prediction in laparoscopic surgery, demonstrating its adequate performance and providing intuitive clinical decision support.</p> Trial registration <p>The trial was registered in the Chinese Clinical Trial Registry (Registration No: ChiCTR2500098281; Date: March 05, 2025).</p>

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A dynamic nomogram for predicting postoperative nausea and vomiting after laparoscopic surgery: a prospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月9日

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  • Yufan Lu,
  • Xuezheng Lin,
  • Beiyan Ruan,
  • Shu Liang,
  • Ying Wang,
  • Lin Wang

摘要

Background

The objective of this study was to develop and validate a dynamic nomogram for predicting postoperative nausea and vomiting (PONV) following laparoscopic procedures.

Methods

Clinical data were prospectively collected from adult patients undergoing laparoscopic procedures between March 10, 2025, and May 22, 2025. Patient demographics and clinical characteristics were used to develop a dynamic nomogram for predicting PONV. Variable screening and predictor selection were performed using least absolute shrinkage and selection operator (LASSO) regression, followed by refinement through multivariable logistic regression to construct the nomogram. The area under the curve (AUC) was used to objectively quantify the discriminative ability of the model. Internal validation was performed using bootstrapping, and model performance was further evaluated using calibration and decision curve analysis (DCA).

Results

Of the 413 patients enrolled, 127 (30.8%) developed PONV within 24 h postoperatively. A nomogram incorporating six predictors was developed. The AUC of the prediction model was 0.704 (95% confidence interval [CI]: 0.648‒0.759), and internal validation using bootstrapping was 0.728 (95% CI: 0.674‒0.782). The model demonstrated good calibration, and the DCA revealed a satisfactory net benefit for patients when the probability threshold ranged from 0.12 to 0.54. This indicates the model’s clinical utility for supporting personalized decision-making.

Conclusions

We developed a dynamic nomogram for PONV risk prediction in laparoscopic surgery, demonstrating its adequate performance and providing intuitive clinical decision support.

Trial registration

The trial was registered in the Chinese Clinical Trial Registry (Registration No: ChiCTR2500098281; Date: March 05, 2025).