Development and Internal Validation of a Perioperative Model for Predicting Early Postoperative Nausea and Vomiting After Gynecologic Laparoscopic Surgery.

Authors

Zihui Zheng, Qinzhu Wu, Yuhua Zhou

Published in

Journal of multidisciplinary healthcare. Volume 19. Pages 637369. Epub Oct 21, 2026.

Abstract

Postoperative nausea and vomiting (PONV) is a frequent complication after gynecologic laparoscopic surgery. The classic Apfel score is widely used for risk assessment, but its individual-level discrimination may be limited in this relatively homogeneous high-risk population.
We conducted a single-center prospective observational model-development study of 487 patients undergoing gynecologic laparoscopic surgery under general anesthesia. Nineteen candidate predictors were considered. Exploratory univariable screening (P<0.10), followed by backward elimination using the Akaike information criterion, was used to derive the multivariable logistic model. The blood-loss cut-point was selected by comparing candidate cut-points that retained at least 10% of patients in each group. Internal validation used 1000 bootstrap resamples in which the complete screening, cut-point selection, and backward-elimination procedure was repeated.
Overall, 216 patients (44.4%) developed 0-24 h PONV. The final eight-predictor model had an apparent AUC of 0.706 and Brier score of 0.214. After bootstrap correction of the complete development procedure, the AUC was 0.666, the Brier score was 0.229, the calibration slope was 0.793, and the calibration intercept was -0.001. The apparent AUC exceeded that of the Apfel score (0.586; paired DeLong difference 0.121, 95% CI 0.070-0.171; P<0.001).
The perioperative model showed moderate apparent discrimination, which decreased after correction for model-development optimism. The model is preliminary and requires prospective external validation, recalibration where necessary, and clinical-impact evaluation before routine use.

PMID:
42928769
Bibliographic data and abstract were imported from PubMed on 26 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.