Scharf Christoph, Cho Yong K, Bloch Konrad E, Brunckhorst Corinna, Duru Firat, Balaban Kryzstof, Foldvary Nancy, Liu Lynn, Burgess Richard C, Candinas Reto, Wilkoff Bruce L
Cardiovascular Center, University Hospital Zurich, Department of Internal Medicine, Division of Cardiology and Pneumology, Rämistrasse 100, CH-8091 Zürich, Switzerland.
Circulation. 2004 Oct 26;110(17):2562-7. doi: 10.1161/01.CIR.0000145540.36097.EB. Epub 2004 Oct 18.
Minute ventilation sensors of cardiac pacemakers measure ventilation by means of transthoracic impedance changes between the pacemaker case and the electrode tip. We investigated whether this technique might detect sleep-related breathing disorders.
In 22 patients, analog waveforms of the transthoracic impedance signal measured by the pacemaker minute ventilation sensor over the course of a night were visualized, scored for apnea/hypopnea events, and compared with simultaneous polysomnography. Analysis of transthoracic impedance signals correctly identified the presence or absence of moderate to severe sleep apnea (apnea/hypopnea index, AHI >20 h(-1)) in all patients (receiver operating characteristics, ROC=1.0). The ROC for AHI scores of > or =5 h(-1) and > or =10 h(-1) showed an area under the curve of 0.95, P<0.005, and 0.97, P<0.0001, respectively. Accuracy over time assessed by comparing events per 5-minute epochs was high (Cronbach alpha reliability coefficient, 0.85; intraclass correlation, 0.73). Event-by-event comparison within +/-15 seconds revealed agreement in 81% (kappa, 0.77; P<0.001).
Detection of apnea/hypopnea events by pacemaker minute ventilation sensors is feasible and accurate compared with laboratory polysomnography. This technique might be useful to screen and monitor sleep-related breathing disorders in pacemaker patients.
心脏起搏器的分钟通气传感器通过起搏器外壳与电极尖端之间的经胸阻抗变化来测量通气。我们研究了该技术是否能检测出与睡眠相关的呼吸障碍。
对22例患者夜间起搏器分钟通气传感器测量的经胸阻抗信号模拟波形进行可视化处理,对呼吸暂停/低通气事件进行评分,并与同步多导睡眠图进行比较。经胸阻抗信号分析能正确识别所有患者中是否存在中度至重度睡眠呼吸暂停(呼吸暂停/低通气指数,AHI>20 h⁻¹)(受试者工作特征曲线,ROC = 1.0)。AHI评分≥5 h⁻¹和≥10 h⁻¹的ROC曲线下面积分别为0.95,P<0.005和0.97,P<0.0001。通过比较每5分钟时段的事件评估的随时间的准确性较高(克朗巴赫α可靠性系数,0.85;组内相关系数,0.73)。在±15秒内逐事件比较显示一致性为81%(kappa值,0.77;P<0.001)。
与实验室多导睡眠图相比,起搏器分钟通气传感器检测呼吸暂停/低通气事件是可行且准确的。该技术可能有助于筛查和监测起搏器患者与睡眠相关的呼吸障碍。