Objectives: To stratify patients recently discharged from hospital with heart failure (HF) according to their risk of death and/or hospitalisation for worsening HF (WHF), to enable timely and appropriate monitoring and intervention. Methods: Data from the TEN-HMS study were used in this analysis. Chi-square automatic interaction detector (CHAID) decision trees were constructed using a 10-fold cross-validation to predict events at 1-year and compared with logistic regression (LR) models using ROC curve analysis. Results: 284 patients were used for training and 160 patients available at 4-month for validation. Amino-terminal pro-brain natriuretic peptide (NT-proBNP) was the strongest predictor of mortality identifying groups with high (>13,492 pg/ml), medium (3127-13,492 pg/ml) and low (<= 3127 pg/ml) risk, followed by MI, systolic blood pressure, age, heart rhythm, study randomisation group and serum sodium. NT-proBNP was also the strongest predictor for death or hospitalization for WHF identifying grou Conclusions: Decision trees are an alternative classification method used to differentiate risk in patients with HF. The resultant models are concise, free of subjective variables and understood easily by clinicians. Further exploration of their potential and validation in other data-sets is justified. (C) 2011 Elsevier Ireland Ltd. All rights reserved.