LAUSR

The risk of sudden cardiac death (SCD) has long been known to by dynamic. For example, Solomon and colleagues demonstrated that the months after a myocardial infarction represents a transient high-risk period where the absolute rate of SCD is acutely elevated before declining to a basal and lower rate.1 Likewise, in patients with implantable cardioverter-defibrillators (ICDs), the observed distribution of ICD therapies is non-random, with clear clustering of ventricular arrhythmias.2 The reality of this dynamic SCD risk is buttressed against the typically cross-sectional nature in which we deploy risk assessment in the care of patients. Missing, therefore, is a risk assessment approach that flexibly captures dynamic changes in SCD risk over the course of a patient’s lifetime. Such a strategy could have significant implications for prevention of SCD events and, possibly, maximization of ICD benefit. In this issue of the Journal, Rohde and colleagues sought to identify dynamic risk factors for SCD in patients with systolic heart failure (HF).3 The cohort included 8399 patients from the PARADIGM-HF (Prospective Comparison of ARNI with ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure) trial,4 a randomized controlled comparison of sacubitril/valsartan and enalapril in subjects with chronic, systolic HF and left ventricular ejection fraction (LVEF) ≤40%. Leveraging pre-specified serial assessment during the trial, the authors employed a multivariable model of time-updated markers to identify dynamic predictors of SCD risk. To assess the relationship between trajectories of clinical and serological markers with SCD risk, they performed a ‘look-back’ analysis, working retrospectively from the time of death. A third analysis involved a machine learning algorithm [classification and regression tree (CART) analysis] to identify dynamic predictors of risk. Cognizant of the relevance of competing risk, the authors used logistic regression models