Entropy of cardiac repolarization predicts ventricular arrhythmias and mortality in patients receiving an implantable cardioverter-defibrillator for primary prevention of sudden death.

AIMS: The need for a readily available, inexpensive, non-invasive method for improved risk stratification of heart failure (HF) patients is paramount. Prior studies have proposed that distinct fluctuation patterns underlying the variability of physiological signals have unique prognostic value. We tested this hypothesis in an extensively phenotyped cohort of HF patients using EntropyXQT, a novel non-linear measure of cardiac repolarization dynamics.

METHODS AND

RESULTS

In a prospective, multicentre, observational study of 852 patients in sinus rhythm undergoing clinically indicated primary prevention implantable cardioverter-defibrillator (ICD) implantation (2003-10), exposures included demographics, history, physical examination, medications, laboratory results, serum biomarkers, ejection fraction, conventional electrocardiographic (ECG) analyses of heart rate and QT variability, and EntropyXQT. The primary outcome was first 'appropriate' ICD shock for ventricular arrhythmias. The secondary outcome was composite events (appropriate ICD shock and all-cause mortality). After exclusions, the cohort (n = 816) had a mean age of 60 ± 13 years, 28% women, 36% African Americans, 56% ischaemic cardiomyopathy, and 29 ± 16% Seattle HF risk score (SHFS) 5-year predicted mortality. Over 45 ± 24 months, there were 134 appropriate shocks and 166 deaths. After adjusting for 30 exposures, the hazard ratios (comparing the 5th to 1st quintile of EntropyXQT) for primary and secondary outcomes were 3.29 (95% CI 1.74-6.21) and 2.28 (1.53-3.41), respectively. Addition of EntropyXQT to a model comprised of the exposures or SHFS significantly increased net reclassification and the ROC curve area.

CONCLUSIONS

EntropyXQT measured during ICD implantation strongly and independently predicts appropriate shock and all-cause mortality over follow-up. EntropyXQT complements conventional risk predictors and has the potential for broad clinical application.