Cardiology Department, Universitätsklinikum Essen, Essen, Germany.
Eur J Heart Fail. 2011 Sep;13(9):1019-27. doi: 10.1093/eurjhf/hfr089.
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.
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.
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.
研究通过家庭监测功能以 24 小时间隔自动获取的植入式心脏再同步治疗除颤器（CRT-D）的诊断数据是否能够实现心血管住院和死亡的动态预测。
377 例心力衰竭患者接受了具有家庭监测功能的 CRT-D。在 1 年的随访期间，前瞻性地收集了所有因心血管原因导致的死亡和住院的数据以及家庭监测数据，以开发一种具有预设特异性为 99.5%的预测算法。该算法包括 7 个参数：24 小时平均心率、静息心率、患者活动、室性早搏频率、心房-心房间期（心率变异性）、右心室起搏阻抗和无痛性电击阻抗。该算法是使用 25 天的监测窗口开发的，该窗口在住院或死亡前 3 天结束。虽然个别参数的回顾性灵敏度范围为 23.6%至 50.0%，但所有参数的组合在检测心血管住院和死亡方面的灵敏度为 65.4%，特异性为 99.5%（相当于每位患者每年随访中出现 1.83 次假阳性检测）。与阴性预测因子发现相比，阳性预测因子发现后的事件相对风险估计值高 7.15 倍。
我们开发了一种用于预测接受 CRT-D 治疗的患者心血管事件的自动化算法，该算法能够通过家庭监测功能每天传输其诊断数据。该工具可能会提高患者的生活质量，降低发病率、死亡率和医疗经济负担，现在需要前瞻性研究。ClinicalTrials.gov NCT00376116。
