Champ-Rigot Laure, Cornille Anne-Laure, Ferchaud Virginie, Morello Rémy, Pellissier Arnaud, Ollitrault Pierre, Saloux Eric, Moirot Pierre, Milliez Paul
Normandie Univ, UNICAEN, CHU de Caen Normandie, Service de Cardiologie, 14000 Caen, France.
Normandie Univ, UNICAEN, CHU de Caen Normandie, Service de Cardiologie, 14000 Caen, France.
Respir Med Res. 2023 Nov;84:101025. doi: 10.1016/j.resmer.2023.101025. Epub 2023 May 13.
Automated detection of sleep apnea (SA) by pacemaker (PM) has been proposed and exhibited good agreement with polysomnography to detect severe SA. We aimed to evaluate the usefulness of SA monitoring algorithm in elderly patients with diastolic dysfunction.
Consecutive patients referred to the Caen University Hospital for PM implantation between May 2016 and December 2018 presenting isolated diastolic dysfunction were eligible for the study. The respiratory disturbance index (RDI) measured by the PM, and the mean monthly RDI (RDIm), were compared to the apnea hypopnea index (AHI) assessed with portable monitor for severe SA diagnosis.
During the study period, 68 patients were recruited, aged of 80.4 ± 8.2 years. 63 patients underwent polygraphy with a portable monitor: 57 presented SA (83.8%), including 16 with severe SA (23.5%). Eight were treated with continuous positive airway pressure (CPAP). We found the RDI cutoff value of 22 events/h to predict severe SA, with 71.4% sensitivity and 65.2%, specificity. The RDIm cutoff value to detect severe SA was 19 events/h, with a sensitivity of 60% and a specificity of 66%. There was a significant reduction in RDI (p = 0.041), RDIm (p = 0.039) and AHI (p = 0.002) after CPAP. Supraventricular arrhythmias were frequent in all patients, regardless of SA severity, considering either episodes occurrence or total burden.
In a population of elderly patients with PM and diastolic dysfunction, the SA monitoring algorithm was able to detect severe SA, with good diagnostic performance values, but also to provide follow-up data for the patients treated with CPAP.
有人提出通过起搏器（PM）自动检测睡眠呼吸暂停（SA），并且在检测重度SA方面与多导睡眠图表现出良好的一致性。我们旨在评估SA监测算法在老年舒张功能障碍患者中的实用性。
2016年5月至2018年12月期间连续转诊至卡昂大学医院进行PM植入且存在单纯舒张功能障碍的患者符合本研究条件。将PM测量的呼吸紊乱指数（RDI）和平均每月RDI（RDIm）与便携式监测仪评估的用于重度SA诊断的呼吸暂停低通气指数（AHI）进行比较。
在研究期间，招募了68例患者，年龄为80.4±8.2岁。63例患者使用便携式监测仪进行了多导睡眠监测：57例存在SA（83.8%），其中16例为重度SA（23.5%）。8例接受了持续气道正压通气（CPAP）治疗。我们发现预测重度SA的RDI临界值为22次/小时，敏感性为71.4%，特异性为65.2%。检测重度SA的RDIm临界值为19次/小时，敏感性为60%，特异性为66%。CPAP治疗后，RDI（p = 0.041）、RDIm（p = 0.039）和AHI（p = 0.002）均有显著降低。无论SA严重程度如何，从发作次数或总负荷来看，所有患者室上性心律失常均很常见。
在患有PM和舒张功能障碍的老年患者群体中，SA监测算法能够检测出重度SA，具有良好的诊断性能值，还能为接受CPAP治疗的患者提供随访数据。
