title: A neural network approach to predicting outcomes in heart failure using cardiopulmonary exercise testing.
abstract: OBJECTIVE
  To determine the utility of an artificial neural network (ANN) in predicting cardiovascular (CV) death in patients with heart failure (HF).
  
  
  BACKGROUND
  ANNs use weighted inputs in multiple layers of mathematical connections in order to predict outcomes from multiple risk markers. This approach has not been applied in the context of cardiopulmonary exercise testing (CPX) to predict risk in patients with HF.
  
  
  METHODS
  2635 patients with HF underwent CPX and were followed for a mean of 29 ± 30 months. The sample was divided randomly into ANN training and testing sets to predict CV mortality. Peak VO2, VE/VCO2 slope, heart rate recovery, oxygen uptake efficiency slope, and end-tidal CO2 pressure were included in the model. The predictive accuracy of the ANN was compared to logistic regression (LR) and a Cox proportional hazards (PH) score. A multi-layer feed-forward ANN was used and was tested with a single hidden layer containing a varying number of hidden neurons.
  
  
  RESULTS
  There were 291 CV deaths during the follow-up. An abnormal VE/VCO2 slope was the strongest predictor of CV mortality using conventional PH analysis (hazard ratio 3.04; 95% CI 2.2-4.2, p<0.001). After training, the ANN was more accurate in predicting CV mortality compared to LR and PH; ROC areas for the ANN, LR, and PH models were 0.72, 0.70, and 0.69, respectively. Age and BMI-adjusted odds ratios were 4.2, 2.6, and 2.9, for ANN, LR, and PH, respectively.
  
  
  CONCLUSIONS
  An ANN model slightly improves upon conventional methods for estimating CV mortality risk using established CPX responses.
authors.affiliation: Division of Cardiology, VA Palo Alto Healthcare System/Stanford University, United States. Electronic address: Drj993@aol.com.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Myers
authors.affiliation: Department of Physical Therapy, Federal University Sao Carlos, Brazil.
authors.forename: Cesar Roberto
authors.initials: CR
authors.lastname: de Souza
authors.affiliation: Department of Physical Therapy, Federal University Sao Carlos, Brazil.
authors.forename: Audrey
authors.initials: A
authors.lastname: Borghi-Silva
authors.affiliation: Division of Cardiology, University of Milano, Milano, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Guazzi
authors.affiliation: Lebauer Cardiovascular Research Foundation, Greensboro, NC, United States.
authors.forename: Paul
authors.initials: P
authors.lastname: Chase
authors.affiliation: Lebauer Cardiovascular Research Foundation, Greensboro, NC, United States.
authors.forename: Daniel
authors.initials: D
authors.lastname: Bensimhon
authors.affiliation: Department of Internal Medicine, Virginia Commonwealth University, Richmond, VA, United States.
authors.forename: Mary Ann
authors.initials: MA
authors.lastname: Peberdy
authors.affiliation: Cardiovascular Medicine, Stanford University, Palo Alto, CA, United States.
authors.forename: Euan
authors.initials: E
authors.lastname: Ashley
authors.affiliation: Department of Physical Therapy, Leonard M. Miller School of Medicine, University of Miami, Miami, FL, United States.
authors.forename: Erin
authors.initials: E
authors.lastname: West
authors.affiliation: Department of Physical Therapy, Leonard M. Miller School of Medicine, University of Miami, Miami, FL, United States.
authors.forename: Lawrence P
authors.initials: LP
authors.lastname: Cahalin
authors.affiliation: Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, United States.
authors.forename: Daniel
authors.initials: D
authors.lastname: Forman
authors.affiliation: Department of Physical Therapy, College of Applied Health Sciences, University of Illinois Chicago, Chicago, IL, United States.
authors.forename: Ross
authors.initials: R
authors.lastname: Arena
mesh_terms: D000328:Adult; D000368:Aged; D005080:Exercise Test; D005260:Female; D005500:Follow-Up Studies; D006333:Heart Failure / Q000175:diagnosis* / Q000401:mortality* / Q000503:physiopathology; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D010101:Oxygen Consumption / Q000502:physiology; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods
keywords: Cardiopulmonary exercise testing; Heart failure; Mortality; Oxygen uptake