| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 2924516 | Heart Rhythm | 2008 | 8 Pages |
BackgroundMorbidity and mortality due to chronic heart failure remain unacceptably high despite effective drug therapies, and the search for a better risk predictor is ongoing. Statistics derived from beat-to-beat fluctuations in heart rate or heart rate variability (HRV) have been used for this purpose, but the current predictability level is low or moderate at best.ObjectiveThe purpose of this study was to evaluate whether a recently proposed non-Gaussian index of HRV is a significant and independent mortality predictor in patients with congestive heart failure (CHF).MethodsTwenty-four-hour Holter ECGs from 108 CHF patients were evaluated. Thirty-nine (36.1%) of the patients died during the follow-up period of 33 ± 17 months. Cox proportional hazards regression analysis was performed to determine factors related to all-cause mortality. The factors evaluated derived from clinical information, including plasma brain natriuretic peptide, conventional time- and frequency-domain and fractal HRV measures, and a recently proposed non-Gaussian index λ of HRV.ResultsThe short-term (<40 beats) non-Gaussian index λ40 (hazard ratio per increment of unit standard deviation 1.64, 95% confidence interval [1.23, 2.18], P <.001) and the long-term (<1,000 beats) index λ1000 (hazard ratio 1.42, 95% confidence interval [1.07, 2.18], P <.02), together with brain natriuretic peptide (hazard ratio 2.26, 95% confidence interval [1.45, 3.53], P <.001), are significant univariate risk predictors of mortality. In a multivariate model, λ40 (1.49, [1.13, 1.96], P <.005) and brain natriuretic peptide (2.39, [1.53, 3.75], P <.001) are independent predictors of the survival statistics of patients. None of the conventional HRV measures have predicted the mortality of patients in a significant and independent manner.ConclusionThe results of this study indicate the usefulness of the short-term non-Gaussian index of HRV for risk prediction in patients with CHF.
