title: [Prediction of postoperative nausea and vomiting using an artificial neural network].
abstract: OBJECTIVE
  Postoperative nausea and vomiting (PONV) are still frequent side-effects after general anaesthesia. These unpleasant symptoms for the patients can be sufficiently reduced using a multimodal antiemetic approach. However, these efforts should be restricted to risk patients for PONV. Thus, predictive models are required to identify these patients before surgery. So far all risk scores to predict PONV are based on results of logistic regression analysis. Artificial neural networks (ANN) can also be used for prediction since they can take into account complex and non-linear relationships between predictive variables and the dependent item. This study presents the development of an ANN to predict PONV and compares its performance with two established simplified risk scores (Apfel's and Koivuranta's scores).
  
  
  METHODS
  The development of the ANN was based on data from 1,764 patients undergoing elective surgical procedures under balanced anaesthesia. The ANN was trained with 1,364 datasets and a further 400 were used for supervising the learning process. One of the 49 ANNs showing the best predictive performance was compared with the established risk scores with respect to practicability, discrimination (by means of the area under a receiver operating characteristics curve) and calibration properties (by means of a weighted linear regression between the predicted and the actual incidences of PONV).
  
  
  RESULTS
  The ANN tested showed a statistically significant ( p<0.0001) and clinically relevant higher discriminating power (0.74; 95% confidence interval: 0.70-0.78) than the Apfel score (0.66; 95% CI: 0.61-0.71) or Koivuranta's score (0.69; 95% CI: 0.65-0.74). Furthermore, the agreement between the actual incidences of PONV and those predicted by the ANN was also better and near to an ideal fit, represented by the equation y=1.0x+0. The equations for the calibration curves were: KNN y=1.11x+0, Apfel y=0.71x+1, Koivuranta 0.86x-5.
  
  
  CONCLUSIONS
  The improved predictive accuracy achieved by the ANN is clinically relevant. However, the disadvantages of this system prevail because a computer is required for risk calculation. Thus, we still recommend the use of one of the simplified risk scores for clinical practice.
authors.affiliation: Klinik für Innere Medizin, Kreiskrankenhaus Günzburg.
authors.forename: M
authors.initials: M
authors.lastname: Traeger
authors.forename: A
authors.initials: A
authors.lastname: Eberhart
authors.forename: G
authors.initials: G
authors.lastname: Geldner
authors.forename: A M
authors.initials: AM
authors.lastname: Morin
authors.forename: C
authors.initials: C
authors.lastname: Putzke
authors.forename: H
authors.initials: H
authors.lastname: Wulf
authors.forename: L H J
authors.initials: LH
authors.lastname: Eberhart
mesh_terms: D001185:Artificial Intelligence; D002138:Calibration; D016208:Databases, Factual; D006801:Humans; D016014:Linear Models; D016571:Neural Networks, Computer; D020250:Postoperative Nausea and Vomiting / Q000175:diagnosis*; D011237:Predictive Value of Tests; D018570:Risk Assessment; D012307:Risk Factors