A web-based dynamic predictive model for postoperative nausea and vomiting in patient receiving gynecological laparoscopic surgery.

Abstract

The aim of this study was to develop a web-based dynamic prediction model for postoperative nausea and vomiting (PONV) in patients undergoing gynecologic laparoscopic surgery. The patients (N = 647) undergoing gynecologic laparoscopic surgery were included in this observational study. The candidate risk-factors related to PONV were included through literature search. Lasso regression was utilized to screen candidate risk-factors, and the variables with statistical significance were selected in multivariable logistic model building. The web-based dynamic Nomogram was used for model exhibition. Accuracy and validity of the experimental model (EM) were evaluated by generating receiver operating characteristic (ROC) curves and calibration curves. Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model. Decision curve analysis (DCA) was used to evaluate the clinical practicability of the risk prediction model. Ultimately, a total of five predictors including patient-controlled analgesia (odds ratio [OR], 4.78; 95% confidence interval [CI], 1.98-12.44), motion sickness (OR, 4.80; 95% CI, 2.71-8.65), variation of blood pressure (OR, 4.30; 95% CI, 2.41-7.91), pregnancy vomiting history (OR, 2.21; 95% CI, 1.44-3.43), and pain response (OR, 1.64; 95% CI, 1.48-1.83) were selected in model building. Assessment of the model indicates the discriminating power of EM was adequate (ROC-areas under the curve, 93.0%; 95% CI, 90.7%-95.3%). EM showed better accuracy and goodness of fit based on the results of the calibration curve. The DCA curve of EM showed favorable clinical benefits. This dynamic prediction model can determine the PONV risk in patients undergoing gynecologic laparoscopic surgery.