D R Sinclair1, F Chung, G Mezei. 1. Department of Anesthesia, Toronto Hospital, University of Toronto, Ontario, Canada.
Abstract
BACKGROUND: Iletrospective studies fail to identify predictors of postoperative nausea and vomiting (PONV). The authors prospectively studied 17,638 consecutive outpatients who had surgery to identify predictors. METHODS: Data on medical conditions, anesthesia, surgery, and PONV were collected in the post-anesthesia care unit, in the ambulatory surgical unit, and in telephone interviews conducted 24 h after surgery. Multiple logistic regression with backward stepwise elimination was used to develop a predictive model An independent set of patients was used to validate the model RESULTS: Age (younger or older), sex (female or male), smoking status (nonsmokers or smokers), previous PONV, type of anesthesia (general or other), duration of anesthesia (longer or shorter), and type of surgery (plastic, orthopedic shoulder, or other) were independent predictors of PONV. A 10-yr increase in age decreased the likelihood of PONV by 13%. The risk for men was one third that for women. A 30-min increase in the duration of anesthesia increased the likelihood of PONV by 59%. General anesthesia increased the likelihood of PONV 11 times compared with other types of anesthesia. Patients with plastic and orthopedic shoulder surgery had a sixfold increase in the risk for PONV. The model predicted PONV accurately and yielded an area under the receiver operating characteristic curve of 0.785+/-0.011 using an independent validation set. CONCLUSIONS: A validated mathematical model is provided to calculate the risk of PONV in outpatients having surgery. Knowing the factors that predict PONV will help anesthesiologists determine which patients will need antiemetic therapy.
BACKGROUND: Iletrospective studies fail to identify predictors of postoperative nausea and vomiting (PONV). The authors prospectively studied 17,638 consecutive outpatients who had surgery to identify predictors. METHODS: Data on medical conditions, anesthesia, surgery, and PONV were collected in the post-anesthesia care unit, in the ambulatory surgical unit, and in telephone interviews conducted 24 h after surgery. Multiple logistic regression with backward stepwise elimination was used to develop a predictive model An independent set of patients was used to validate the model RESULTS: Age (younger or older), sex (female or male), smoking status (nonsmokers or smokers), previous PONV, type of anesthesia (general or other), duration of anesthesia (longer or shorter), and type of surgery (plastic, orthopedic shoulder, or other) were independent predictors of PONV. A 10-yr increase in age decreased the likelihood of PONV by 13%. The risk for men was one third that for women. A 30-min increase in the duration of anesthesia increased the likelihood of PONV by 59%. General anesthesia increased the likelihood of PONV 11 times compared with other types of anesthesia. Patients with plastic and orthopedic shoulder surgery had a sixfold increase in the risk for PONV. The model predicted PONV accurately and yielded an area under the receiver operating characteristic curve of 0.785+/-0.011 using an independent validation set. CONCLUSIONS: A validated mathematical model is provided to calculate the risk of PONV in outpatients having surgery. Knowing the factors that predict PONV will help anesthesiologists determine which patients will need antiemetic therapy.