Potential value of automated daily screening of cardiac resynchronization therapy defibrillator diagnostics for prediction of major cardiovascular events: results from Home-CARE (Home Monitoring in Cardiac Resynchronization Therapy) study

Abstract

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.

Figures

Figure 1

Sensitivity values for the basic five-parameter predictor (A) and for the enhanced seven-parameter predictor (B), to detect major cardiovascular events from Table 3. In (A), combinations of two, three, and four parameters were made by adding the next best individual parameter. (B) shows the sensitivities for two new parameters and for the combination of all seven parameters. The combination of the two new parameters from (B) without ‘help’ of parameters from (A) had still suboptimal sensitivity of 50% (not shown). All sensitivity values were calculated retrospectively for the target specificity of 99.5%.

Figure 2

Modified receiver operating characteristic curve for the enhanced seven-parameter predictor. Plots show the trade-off between sensitivity to detect impending cardiovascular hospitalization or death and the number of false-positive detections per patient-year of monitoring, as a function of varying thresholds for weighted trends. The optimal point is indicated by the circle.

Figure 3

Example of parameter trends and the resulting combined predictor line (upper panel) in a patient hospitalized for heart failure worsening on 5 April 2008. The seven-parameter predictor reached threshold (beginning of the grey area) 16 days before hospitalization. The threshold was set at 200 arbitrary units as a result of the algorithm optimization process described in the section Methods, and corresponds to a hazard ratio of 7.15. ActP, patient activity; a.u., arbitrary units; bpm, beats per minute; MHR24, mean heart rate during 24 h; MHRR, heart rate at rest; PPVar P–P interval variability; PSHImp, painless shock impedance; RVImp, right ventricular impedance; VES, ventricular extrasystoles; WHF, worsening heart failure.