Tree-based data mining methods will be used to identify key variables in the prediction of the primary outcome, a composite of cardiac death (SCD and HF death), aborted SCD including appropriate ICD firing, and need for heart transplantation. Secondary endpoints will include all-cause mortality, ventricular tachyarrhythmias, septal myectomy or alcohol ablation, hospitalization for heart failure, atrial fibrillation, and stroke. Tree-based modeling is an exploratory technique for uncovering structure in multivariate data28;29. It is particularly useful for deriving prediction rules from a large number of screening variables or risk factors. Tree models can be used for both classification (binary outcome) and regression (continuous outcome). In either case, the collection of prediction rules (from the predictor variables) is displayed in the form of a tree. The terminology mimics that of trees: the root is the top node of the tree that displays the mean (regression) or proportion (classification) of the outcome for the entire sample. A split is the rule for creating new branches, and a leaf is a terminal node. Each node is a binary split of the predictor variable that contains two sub-groups of the sample with the largest possible difference between the groups. The same variable can appear in more than one level of the tree (recursive partitioning).