AimTo investigate whether diagnostic data from implanted cardiac resynchronization therapy defibrillators (CRT-Ds) retrieved automatically at 24 h intervals via a Home Monitoring function can enable dynamic prediction of cardiovascular hospitalization and death.Methods and resultsThree hundred and seventy-seven heart failure patients received CRT-Ds with Home Monitoring option. Data on all deaths and hospitalizations due to cardiovascular reasons and Home Monitoring data were collected prospectively during 1-year follow-up to develop a predictive algorithm with a predefined specificity of 99.5%. Seven parameters were included in the algorithm: mean heart rate over 24 h, heart rate at rest, patient activity, frequency of ventricular extrasystoles, atrial–atrial intervals (heart rate variability), right ventricular pacing impedance, and painless shock impedance. The algorithm was developed using a 25-day monitoring window ending 3 days before hospitalization or death. While the retrospective sensitivities of the individual parameters ranged from 23.6 to 50.0%, the combination of all parameters was 65.4% sensitive in detecting cardiovascular hospitalizations and deaths with 99.5% specificity (corresponding to 1.83 false-positive detections per patient-year of follow-up). The estimated relative risk of an event was 7.15-fold higher after a positive predictor finding than after a negative predictor finding.ConclusionWe developed an automated algorithm for dynamic prediction of cardiovascular events in patients treated with CRT-D devices capable of daily transmission of their diagnostic data via Home Monitoring. This tool may increase patients’ quality of life and reduce morbidity, mortality, and health economic burden, it now warrants prospective studies.ClinicalTrials.gov  NCT00376116.

The formalism of wave propagation in passive media is applied to the spread of the electrical excitation in the human atrial myocardium. From an analog of the classical dispersion dependence that is obtained by wavelet decomposition a precursor parameter is calculated that serves to predict fibrillation already before its onset.

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We use the formalism of wave-packet propagation in passive media to characterize the spread of the electrical excitation in excitable media, namely the cardiac myocardium. We introduce equivalent concepts of group and phase velocities, attenuation coefficient and refraction index to describe the myocardial excitation wave and apply the wavelet approach to construct an analogue of the classical dispersion dependence for active media — the "equivalent dispersion dependence". Using the wavelet decomposition we develop a method for the reconstruction of the equivalent dispersion dependence for the myocardium on the basis of electrical intracardiac signals that are measured in two spatially separated points. The novel method is applied to two different sets of experimental data and to data obtained from a numerical simulation of the atrial myocardium. We show that the introduced equivalent dispersion dependence under physiological conditions is similar to the one that is obtained for resonant wave-medium interaction. The analysis of both experimental data sets clearly shows that the number of cardiac cycles with a resonant form of the equivalent dispersion dependence predominates in the normal state of the myocardium while it decreases early before the onset of atrial fibrillation. We set up the hypothesis that an increasing number of non-resonant cardiac cycles is a precursor of atrial fibrillation and thus can serve to predict fibrillation already at an early stage before its onset. The proposed conception can be applied to investigate the properties of the atrial as well as the ventricular myocardium.

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