Background— The predictive value of heart rate variability (HRV) in chronic heart failure (CHF) has never been tested in a comprehensive multivariate model using short-term laboratory recordings designed to avoid the confounding effects of respiration and behavioral factors. Methods and Results— A multivariate survival model for the identification of sudden (presumably arrhythmic) death was developed with data from 202 consecutive patients referred between 1991 and 1995 with moderate to severe CHF (age 52±9 years, left ventricular ejection fraction 24±7%, New York Heart Association class 2.3±0.7; the derivation sample). Time- and frequency-domain HRV parameters obtained from an 8′ recording of ECG at baseline and during controlled breathing (12 to 15 breaths/min) were challenged against clinical and functional parameters. This model was then validated in 242 consecutive patients referred between 1996 and 2001 (validation sample). In the derivation sample, sudden death was independently predicted by a model that included low-frequency power (LFP) of HRV during controlled breathing ≤13 ms ² and left ventricular end-diastolic diameter ≥77 mm (relative risk [RR] 3.7, 95% CI 1.5 to 9.3, and RR 2.6, 95% CI 1.0 to 6.3, respectively). The derivation model was also a significant predictor in the validation sample ( P =0.04). In the validation sample, LFP ≤11 ms ² during controlled breathing and ≥83 ventricular premature contractions per hour on Holter monitoring were both independent predictors of sudden death (RR 3.0, 95% CI 1.2 to 7.6, and RR 3.7, 95% CI 1.5 to 9.0, respectively). Conclusions— Reduced short-term LFP during controlled breathing is a powerful predictor of sudden death in patients with CHF that is independent of many other variables. These results refine the identification of patients who may benefit from prophylactic implantation of a cardiac defibrillator.

Despite its extensive use in physiological and clinical research, the analysis of HRV (heart rate variability) is still poorly supported by rigorous reliability studies. The main aim of the present study was to perform an in-depth assessment of absolute and relative reliability of standard indexes of HRV from short-term laboratory recordings. In 39 healthy subjects [mean age (min–max): 38 (26–56) years; 18 men and 21 women] we recorded 5 min of supine ECG during spontaneous and paced (15 breaths/min) breathing. The test was repeated on the next day under the same conditions. From the RR intervals we computed standard indexes of HRV: SDNN (S.D. of RR interval values), RMSSD (root-mean-square of successive RR interval differences), LF (low frequency) and HF (high frequency) power (absolute and normalized units) and LF/HF. Absolute reliability was assessed by 95% limits of random variation; relative reliability was assessed by the ICC (intraclass correlation coefficient). The sample size needed to detect a mean difference ≥30% of the between-subject S.D. was also estimated. Although there was no significant mean change between the two tests, we found that in individual subjects the second measurement was as high/low as 1.9/0.5 times (SDNN, best case) and 3.5/0.3 times (LF/HF, worst case) the first measurement, due to pure random variation. For most parameters the ICC was >0.8 (range 0.65–0.88). The estimated sample size ranged from 24–98 subjects. Reliability indexes tended to improve during paced breathing. We conclude that short-term HRV parameters are subject to large day-to-day random variations. Random error, however, represents a limited part of the between-subject variability; therefore observed differences between individuals mostly reflect differences in the subjects' error-free value rather than random error. Overall, paced breathing improves reliability.

Background. Although physical exercise improves motor aspects of Parkinson’s disease (PD), it is not clear whether it may also have a neuroprotective effect. Objective. In this 2-year follow-up study, we determined whether intensive exercise in the early stages of the disease slows down PD progression. Methods. Forty newly diagnosed patients with PD were treated with rasagiline and randomly assigned to 2 groups: MIRT Group (two 28-day multidisciplinary intensive rehabilitation treatments [MIRT], at 1-year interval) and Control Group (only drug). In both groups, Unified Parkinson’s Disease Rating Scale Section II (UPDRS II), UPDRS III, 6-minute walking test (6MWT), Timed Up-and-Go test (TUG); PD Disability Scale (PDDS), and l-dopa equivalents were assessed at baseline (T0), 6 months (T1), 1 year (T2), 18 months (T3), and 2 years (T4) later. Results. Over 2 years, UPDRS II, UPDRS III, TUG, and PDDS differentially progressed in the 2 groups: In the MIRT Group, all scores at T4 were better than at T0 (all Ps < .03). No changes were noted in the Control Group. l-dopa equivalent dosages increased significantly only in the Control Group (P = .0015), with a decrease in the percentages of patients in monotherapy (T1 40%; T2, T3, and T4 20%). In the MIRT Group, the percentages of such patients remained higher (T1 and T2 100%; T3 89%; T4 75%). Conclusions. These results suggest that MIRT might slow down the progression of motor decay, it might delay the need for increasing drug treatment, and thus, it might have a neuroprotective effect.

The autonomic regulation is non-invasively estimated from heart rate variability (HRV). Many methods utilized to assess autonomic regulation require stationarity of HRV recordings. However, non-stationarities are frequently present even during well-controlled experiments, thus potentially biasing HRV indices. The aim of our study is to quantify the potential bias of spectral, symbolic and entropy HRV indices due to non-stationarities. We analyzed HRV series recorded in healthy subjects during uncontrolled daily life activities typical of 24 h Holter recordings and during predetermined levels of robotic-assisted treadmill-based physical exercise. A stationarity test checking the stability of the mean and variance over short HRV series (about 300 cardiac beats) was utilized to distinguish stationary periods from non-stationary ones. Spectral, symbolic and entropy indices evaluated solely over stationary periods were contrasted with those derived from all the HRV segments. When indices were calculated solely over stationary series, we found that (i) during both uncontrolled daily life activities and controlled physical exercise, the entropy-based complexity indices were significantly larger; (ii) during uncontrolled daily life activities, the spectral and symbolic indices linked to sympathetic modulation were significantly smaller and those associated with vagal modulation were significantly larger; (iii) while during uncontrolled daily life activities, the variance of spectral, symbolic and entropy rate indices was significantly larger, during controlled physical exercise, it was smaller. The study suggests that non-stationarities increase the likelihood to overestimate the contribution of sympathetic control and affect the power of statistical tests utilized to discriminate conditions and/or groups.

Heart rate variability (HRV) has been conventionally analysed with time- and frequency-domain methods, which measure the overall magnitude of RR interval fluctuations around its mean value or the magnitude of fluctuations in some predetermined frequencies. Analysis of heart rate dynamics by novel methods, such as heart rate turbulence after ventricular premature beats, deceleration capacity of heart rate and methods based on chaos theory and nonlinear system theory, have gained recent interest. Recent observational studies have suggested that some indices describing nonlinear heart rate dynamics, such as fractal scaling exponents, heart rate turbulence and deceleration capacity, may provide useful prognostic information in various clinical settings and their reproducibility may be better than that of traditional indices. For example, the short-term fractal scaling exponent measured by the detrended fluctuation analysis method has been shown to predict fatal cardiovascular events in various populations. Similarly, heart rate turbulence and deceleration capacity have performed better than traditional HRV measures in predicting mortality in post-infarction patients. Approximate entropy, a nonlinear index of heart rate dynamics, which describes the complexity of RR interval behaviour, has provided information on the vulnerability to atrial fibrillation. There are many other nonlinear indices which also give information on the characteristics of heart rate dynamics, but their clinical usefulness is not as well established. Although the concepts of nonlinear dynamics, fractal mathematics and complexity measures of heart rate behaviour, heart rate turbulence, deceleration capacity in relation to cardiovascular physiology or various cardiovascular events are still far away from clinical medicine, they are a fruitful area for research to expand our knowledge concerning the behaviour of cardiovascular oscillations in normal healthy conditions as well as in disease states.