Evidence map›Paper›PMID 41803717›Full record

ArticleBMC anesthesiology2026

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

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

Abstract readValidation Study
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Article in BMC anesthesiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Yufan LuDepartment of Anesthesia Surgery, Taizhou Central Hospital (Taizhou University Hospital), Zhejiang, China.
Xuezheng LinDepartment of Anesthesia Surgery, Taizhou Central Hospital (Taizhou University Hospital), Zhejiang, China.
Beiyan RuanDepartment of Anesthesia Surgery, Taizhou Central Hospital (Taizhou University Hospital), Zhejiang, China.
Shu LiangDepartment of Anesthesia Surgery, Taizhou Central Hospital (Taizhou University Hospital), Zhejiang, China.
Ying WangDepartment of Anesthesia Surgery, Taizhou Central Hospital (Taizhou University Hospital), Zhejiang, China.
Lin WangDepartment of Anesthesia Surgery, Taizhou Central Hospital (Taizhou University Hospital), Zhejiang, China. wanglin7164@163.com.

Funding

Medical Science and Technology Project of Zhejiang Province 2024KY1814Zhejiang Provincial Department of Education Y202147015
6 · The paper itself

Abstract

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

methodsClinical 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).

resultsOf 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.

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

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

Indexed as

LaparoscopyNomogramsPostoperative Nausea and VomitingAdultFemaleHumansMaleMiddle AgedProspective StudiesDynamic nomogramLaparoscopic surgeryLogistic regressionPostoperative nausea and vomitingPrediction model

Identifiers

PMID41803717
PMCPMC13085308

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.