INTRODUCTION: In coronary artery bypass (CABG) surgery, the common complications are the need for reintubation, prolonged mechanical ventilation ( @entity22844 ) and @entity204 . Thus, a reliable model for the prognostic evaluation of those particular outcomes is a worthwhile pursuit. The existence of such a system would lead to better resource planning, cost reductions and an increased ability to guide preventive strategies. The aim of this study was to compare different methods - logistic regression (LR) and artificial neural networks (ANNs) - in accomplishing this goal. MATERIAL AND METHODS: Subjects undergoing CABG (n = 1315) were divided into training (n = 1053) and validation (n = 262) groups. The set of independent variables consisted of age, gender, weight, height, body mass index, @entity6 , @entity1011 level, cardiopulmonary bypass, presence of preserved ventricular function, moderate and severe @entity67 and total number of grafts. The @entity22844 was also an input for the prediction of @entity204 . The ability of ANN to discriminate outcomes was assessed using receiver-operating characteristic (ROC) analysis and the results were compared using a multivariate LR. RESULTS: The ROC curve areas for LR and ANN models, respectively, were: for reintubation 0.62 (CI: 0.50-0.75) and 0.65 (CI: 0.53-0.77); for @entity22844 0.67 (CI: 0.57-0.78) and 0.72 (CI: 0.64-0.81); and for @entity204 0.86 (CI: 0.79-0.93) and 0.85 (CI: 0.80-0.91). No differences were observed between models. CONCLUSIONS: The ANN has similar discriminating power in predicting reintubation, @entity22844 and @entity204 outcomes. Thus, both models may be applicable as a predictor for these outcomes in subjects undergoing CABG.

Predicting reintubation, prolonged mechanical ventilation and XXXX in post-coronary artery bypass graft surgery: a comparison between artificial neural networks and logistic regression models.

@entity6 :: ('MESH:D003920', 'Disease') :: ['diabetes'] @entity67 :: ('MESH:D018754', 'Disease') :: ['ventricular dysfunction'] @entity1011 :: ('MESH:D003404', 'Chemical') :: ['creatinine'] @entity22844 :: ('12181', 'Species') :: ['PMV'] @entity204 :: ('MESH:D003643', 'Disease') :: ['death']
@entity204 :: (MESH:D003643,Disease) :: ['death']
