Applicability of Risk Scores for Postoperative Nausea and Vomiting in a Taiwanese Population Undergoing General Anaesthesia. Issue 4 (July 2015)

• Record Type: Journal Article
• Authors: Wu, Y. H.; Sun, H. S.; Wang, S. T.; Tseng, C-C. A.
• Is Part Of: Anaesthesia and intensive care. Volume 43:Issue 4(2015)
• Page Start: 473
• Page End: 478
• Publication Date: 2015-07
• Subjects: postoperative nausea and vomiting -- car sickness -- Apfel score -- Koivuranta score; Anesthesiology -- Periodicals; Intensive Care Units -- Periodicals; 617.96
• DOI: 10.1177/0310057X1504300409
• Languages: English
• ISSNs: 0310-057X

Abstract

Five popular scoring systems for postoperative nausea and vomiting (PONV) were validated and compared with two new predictive models in a Taiwanese population. Nine hundred and ninety-two patients receiving general anaesthesia in a tertiary hospital were investigated in a prospective observational cohort study. Patient demographic data and the incidence of nausea or vomiting in the first 24 hours after surgery were recorded. The overall incidence of PONV was 42%. The area under the curve (AUC) of the five published PONV risk scoring systems was 0.62 to 0.67. Logistic regression analysis in this study cohort showed that female sex and a history of PONV/car sickness were the only statistically significant independent risk factors for PONV (likelihood ratio test P <0.001). The AUCs of our two-predictor and gender-only models were 0.668 and 0.643, respectively (Nagelkerke R 2 =0.122 and 0.109). Goodness-of-fit showed that a two-predictor model predicted an outcome that was in agreement with the observed outcome ( P =0.973). Both the two-predictor model and the Apfel score had a similar AUC that was significantly different from the AUCs of the other models. The AUC for the gender-only model in our population was similar to that of the simplified Koivuranta and the Palazzo and Evans scores (AUC=0.659 and 0.632; P =0.137 and 0.513 respectively). All AUCs had only moderate discrimination power but our female gender-only model was much simpler. Using female gender as the only predictor of PONV had predictive power with 75% sensitivity and 54% specificity.