BACKGROUND: We tested whether or not heart rate variability (HRV) changes can serve as early signs of ventricular tachycardia (VT) and predict slow and fast VT in patients with an implantable cardioverter defibrillator (ICD). METHODS AND RESULTS: We studied the ICD stored 1000 beat-to-beat intervals before the onset of VT (131 episodes) and during a control time without VT (74 series) in 63 chronic heart failure ICD patients. Standard HRV parameters as well as two nonlinear parameters, namely 'Polvar10' from symbolic dynamics and the finite time growth rates 'Fitgra9' were calculated. Comparing the control and the VT series, no linear HRV parameter showed a significant difference. The nonlinear parameters detected a significant increase in short phases with low variability before the onset of VT (for time series with less than 10% ectopy, P<0.05). Subdividing VT into fast (cycle length <or=270 ms) and slow (>270 ms) events, we found that the onset of slow VT was characterized by a significant increase in heart rate, whereas fast VT was triggered during decreased heart rates, compared to the control series. CONCLUSIONS: Our data may permit the development of automatic ICD algorithms based on nonlinear dynamic HRV parameters to predict VT before it starts. Furthermore, they may facilitate improved prevention strategies.
BACKGROUND: We tested whether or not heart rate variability (HRV) changes can serve as early signs of ventricular tachycardia (VT) and predict slow and fast VT in patients with an implantable cardioverter defibrillator (ICD). METHODS AND RESULTS: We studied the ICD stored 1000 beat-to-beat intervals before the onset of VT (131 episodes) and during a control time without VT (74 series) in 63 chronic heart failureICDpatients. Standard HRV parameters as well as two nonlinear parameters, namely 'Polvar10' from symbolic dynamics and the finite time growth rates 'Fitgra9' were calculated. Comparing the control and the VT series, no linear HRV parameter showed a significant difference. The nonlinear parameters detected a significant increase in short phases with low variability before the onset of VT (for time series with less than 10% ectopy, P<0.05). Subdividing VT into fast (cycle length <or=270 ms) and slow (>270 ms) events, we found that the onset of slow VT was characterized by a significant increase in heart rate, whereas fast VT was triggered during decreased heart rates, compared to the control series. CONCLUSIONS: Our data may permit the development of automatic ICD algorithms based on nonlinear dynamic HRV parameters to predict VT before it starts. Furthermore, they may facilitate improved prevention strategies.
Authors: Claudia Lerma; Niels Wessel; Alexander Schirdewan; Jürgen Kurths; Leon Glass Journal: Med Biol Eng Comput Date: 2008-03-15 Impact factor: 2.602