A neural network approach to predicting outcomes in heart failure using cardiopulmonary exercise testing.

Objectives: 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.

Conclusion: An ANN model slightly improves upon conventional methods for estimating CV mortality risk using established CPX responses.