Functional Correlates of Fractal Behavior of HRV in COPD Patients

Abstract

Although ECG monitoring demonstrating heart rate abnormalities and sudden death is common in chronic obstructive pulmonary disease (COPD), autonomic imbalance (AI) is occasionally assessed in these patients. Heart rate variability (HRV) is a well-recognized tool in AI investigation. It has been suggested that nonlinear HRV analysis might provide more valuable information than traditional time-domain indexes (TDI). Fractal (F) analysis is an emerging nonlinear technique and this is one of the first studies on HRV F-features in COPD. Aim of the study was to evaluate if HRV F-behavior reflects COPD severity better than TDI. We studied 40 COPD patients and 10 normal subjects. All underwent 24h-Holter ECG, measuring TDI (SD, PNN50, MSSD). F-analysis was calculated: 1)by the F-dimension (FD) extracted from beat-to-beat series (RR) by Higuchi’s algorithm; 2)by the slope (beta) of the RR power spectral density. Both FD and SD showed a significant (p<0.0001) difference between N and COPD pts (FD = 1.35±0.06 and 1.70±0.12; SD = 57±10 and 37±10 respectively), while beta (N=-1.02±0.14; COPD=-1.05± 0.21), PNN50 (N=9.2±6.5; COPD=6.5±7.1) and MSSD (N=1089±738; COPD=824±700) did not detect significant difference. About the main clinical parameters, only FD exhibited a significant negative correlation both with FVC (p=0.01, r=-0.39) and FEV1 (p=0.02, r=-0.36), interestingly reflecting the disease severity. Between the two F-algorithms, only FD appears more sensitive to AI changes. Results suggest that HRV F-features can be candidates as relevant measure of the overall physiologic and functional status in COPD. Decreased FD-detected HRV could help improving risk stratification, treatment evaluation and disease’s AI understanding.