Heart rate variability spectral indices for haemodynamic classification of haemodialysis patients.
The usefulness of spectral indices extracted from the heart rate variability (HRV) in discriminating between hypotension-prone and hypotension-resistant haemodialysis patients was investigated. In 30 patients, classified as hypotension resistant (stable group) or hypotension prone (unstable group), beat-to-beat heart period was measured during haemodialysis sessions terminated without collapses. HRV was analysed in the frequency domain combining classic autoregressive spectral estimation with two eigen decomposition-based techniques: the reduced rank approximation (RRA) of the autocorrelation matrix and the Pisarenko harmonic decomposition (PHD). Five spectral indices were obtained: the ratio between the powers in the LF and HF bands (LF/HF), the same ratio calculated after application of RRA (LF/HFRRA), the frequency of the main oscillatory component of HRV estimated through PHD with a decomposition order equal to 1 (F1) and equal to 2 (F2) and the difference between the frequencies of the two oscillatory components resolved in the latter cas (Fd). The performances of these indices in discriminating between the two groups of patients were evaluated estimating the misclassification probability (Pm) of a Bayesian quadratic classifier. The HRV spectral pattern was markedly different: in the stable patients power was mainly in the low-frequency band, whereas in the unstable group it was mainly in the high-frequency band. The frequency of the main oscillatory component was significantly greater in the unstable group than in the stable one. Spectral indices displayed good discrimination power, increasing with the length of the dialysis interval. Best performances were achieved by LF/HFRRA both over short dialysis periods (Pm approximately 12% over 20 min intervals) and over longer periods (Pm = 3.3% over 160 min); similar results were obtained with Fd over short periods and LF/HF over long periods. Spectral HRV indices demonstrate, therefore, a diagnostic value in discriminating between hypotension-resistant and hypotension-prone patients.
Severi
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Bioengineering Unit at DEIS, University of Bologna, Italy.
Cavalcanti
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Avanzolini
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D001499:Bayes Theorem; D001794:Blood Pressure / Q000502:physiology; D006339:Heart Rate / Q000502:physiology*; D006439:Hemodynamics / Q000502:physiology*; D006801:Humans; D007676:Kidney Failure, Chronic / Q000503:physiopathology / Q000628:therapy; D012044:Regression Analysis; D006435:Renal Dialysis; D018570:Risk Assessment; D014462:Ultrafiltration