RESUMEN
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.
Asunto(s)
Fibrilación Atrial/diagnóstico , Fibrilación Atrial/fisiopatología , Atrios Cardíacos/fisiopatología , Sistema de Conducción Cardíaco/fisiopatología , Modelos Cardiovasculares , Procesamiento de Señales Asistido por Computador , Potenciales de Acción , Fibrilación Atrial/etiología , Fibrilación Atrial/cirugía , Simulación por Computador , Atrios Cardíacos/cirugía , Prótesis Valvulares Cardíacas/efectos adversos , Humanos , Válvula Mitral , Valores de Referencia , Sensibilidad y EspecificidadRESUMEN
AIM: To 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 RESULTS: Three 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. CONCLUSION: We 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.