[Prediction of XXXX using an artificial neural network].

@entity1 @entity1274 @entity1209
OBJECTIVE: @entity1209 ( @entity1209 ) are still frequent side-effects after general anaesthesia. These unpleasant symptoms for the @entity1 can be sufficiently reduced using a multimodal antiemetic approach. However, these efforts should be restricted to risk @entity1 for @entity1209 . Thus, predictive models are required to identify these @entity1 before surgery. So far all risk scores to predict @entity1209 are based on results of logistic regression analysis. @entity1274 ( @entity1274 ) 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 @entity1274 to predict @entity1209 and compares its performance with two established simplified risk scores (Apfel's and Koivuranta's scores). METHODS: The development of the @entity1274 was based on data from 1,764 @entity1 undergoing elective surgical procedures under balanced anaesthesia. The @entity1274 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 @entity1209 ). RESULTS: The @entity1274 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 @entity1209 and those predicted by the @entity1274 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. CONCLUSION: The improved predictive accuracy achieved by the @entity1274 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.
