title: Predicting reintubation, prolonged mechanical ventilation and death in post-coronary artery bypass graft surgery: a comparison between artificial neural networks and logistic regression models.
abstract: BACKGROUND
  In coronary artery bypass (CABG) surgery, the common complications are the need for reintubation, prolonged mechanical ventilation (PMV) and death. 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.
  
  
  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, diabetes, creatinine level, cardiopulmonary bypass, presence of preserved ventricular function, moderate and severe ventricular dysfunction and total number of grafts. The PMV was also an input for the prediction of death. 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 PMV 0.67 (CI: 0.57-0.78) and 0.72 (CI: 0.64-0.81); and for death 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, PMV and death outcomes. Thus, both models may be applicable as a predictor for these outcomes in subjects undergoing CABG.
authors: Department of Physical Therapy, Federal University of Sao Carlos, Sao Carlos, SP, Brazil.
authors: Renata G
authors: RG
authors: Mendes
authors: Computer Department, Federal University of Sao Carlos, Sao Carlos, SP, Brazil.
authors: César R
authors: CR
authors: de Souza
authors: Hospital de Base of São José do Rio Preto, Faculty of Medicine, São José do Rio Preto, SP, Brazil.
authors: Maurício N
authors: MN
authors: Machado
authors: Hospital de Base of São José do Rio Preto, Faculty of Medicine, São José do Rio Preto, SP, Brazil.
authors: Paulo R
authors: PR
authors: Correa
authors: Computer Department, Federal University of Sao Carlos, Sao Carlos, SP, Brazil.
authors: Luciana
authors: L
authors: Di Thommazo-Luporini
authors: Department of Physical Therapy, College of Applied Health Sciences, University of Illinois, Chicago, USA.
authors: Ross
authors: R
authors: Arena
authors: Cardiology Division, Department of Veterans Affairs (VA) Palo Alto Health Care System, Palo Alto, CA, USA.
authors: Jonathan
authors: J
authors: Myers
authors: Computer Department, Federal University of Sao Carlos, Sao Carlos, SP, Brazil.
authors: Ednaldo B
authors: EB
authors: Pizzolato
authors: Department of Physical Therapy, Federal University of Sao Carlos, Sao Carlos, SP, Brazil.
authors: Audrey
authors: A
authors: Borghi-Silva
keywords: computer applications; coronary artery bypass grafts; outcomes; postoperative care