Dynamic changes in cardiovascular and systemic parameters prior to sudden cardiac death in heart failure with reduced...

Prognostic models of sudden cardiac death (SCD) typically incorporate data at only a single time-point. We investigated independent predictors of SCD addressing the impact of integrating time-varying covariates to improve prediction assessment.

We studied 8399 patients enrolled in the PARADIGM-HF trial and identified independent predictors of SCD (n = 561, 36% of total deaths) using time-updated multivariable-adjusted Cox models, classification and regression tree (CART), and logistic regression analysis. Compared with patients who were alive or died from non-sudden cardiovascular deaths, patients who suffered a SCD displayed a distinct temporal profile of New York Heart Association (NYHA) class, heart rate and levels of three biomarkers (albumin, uric acid and total bilirubin), with significant differences observed more than 1 year prior to the event (P_interaction  < 0.001). In multivariable models adjusted for baseline covariates, seven time-updated variables independently contributed to SCD risk (incremental likelihood chi-square = 46.2). CART analysis identified that baseline variables (implantable cardioverter-defibrillator use and N-terminal prohormone of B-type natriuretic peptide levels) and time-updated covariates (NYHA class, total bilirubin, and total cholesterol) improved risk stratification. CART-defined subgroup of highest risk had nearly an eightfold increment in SCD hazard (hazard ratio 7.7, 95% confidence interval 3.6-16.5; P < 0.001). Finally, changes over time in heart rate, NYHA class, blood urea nitrogen and albumin levels were associated with differential risk of sudden vs. non-sudden cardiovascular deaths (P < 0.05).

Beyond single time-point assessments, distinct changes in multiple cardiac-specific and systemic variables improved SCD risk prediction and were helpful in differentiating mode of death in chronic heart failure.