Predicting postoperative nausea and vomiting with the application of an artificial neural network

Abstract

Background: Several medications have proved to be useful in preventing postoperative nausea and vomiting (PONV). However, routine antiemetic prophylaxis is not cost-effective. We evaluated the accuracy and discriminating power of an artificial neural network (ANN) to predict PONV.

Methods: We analysed data from 1086 in-patients who underwent various surgical procedures under general anaesthesia without antiemetic prophylaxis. Predictors used for ANN training were selected by computing the value of chi(2) statistic and information gain with respect to PONV. The configuration of the ANN was chosen by using a software tool. Then the training of the ANN was performed based on data from a training set (n=656). Testing validation was performed with the remaining patients (n=430) whose outcome regarding PONV was unknown to the ANN. Area under the receiver operating characteristic (ROC) curves were used to quantify predictive performance. ANN performance was compared with those of the Naïve Bayesian classifier model, logistic regression model, simplified Apfel score and Koivuranta score.

Results: ANN accuracy was 83.3%, sensitivity 77.9% and specificity 85.0% in predicting PONV. The areas under the ROC curve follow: ANN, 0.814 (0.774-0.850); Naïve Bayesian classifier, 0.570 (0.522-0.617); logistic regression, 0.669 (0.623-0.714); Koivuranta score, 0.626 (0.578-0.672); simplified Apfel score, 0.624 (0.576-0.670). ANN discriminatory power was superior to those of the other predicting models (P<0.05).

Conclusions: The ANN provided the best predictive performance among all tested models.