自动滴定 CPAP 对接受自动滴定 CPAP 治疗的阻塞性睡眠呼吸暂停患者残余呼吸暂停低通气指数的估计准确性。
Accuracy of autotitrating CPAP to estimate the residual Apnea-Hypopnea Index in patients with obstructive sleep apnea on treatment with autotitrating CPAP.
机构信息
State University of New York at Buffalo, USA.
出版信息
Sleep Breath. 2009 Nov;13(4):383-90. doi: 10.1007/s11325-009-0258-2. Epub 2009 May 1.
OBJECTIVE
Autotitrating continuous positive airway pressure (auto-CPAP) devices now have a smart card (a pocket-sized card with embedded integrated circuits which records data from the CPAP machine such as CPAP usage, CPAP pressure, large leak, etc.) which can estimate the Apnea-Hypopnea Index (AHI) on therapy. The aim of this study was to determine the accuracy of auto-CPAP in estimating the residual AHI in patients with obstructive sleep apnea (OSA) who were treated with auto-CPAP without a CPAP titration study.
PATIENTS AND METHODS
We studied 99 patients with OSA from April 2005 to May 2007 who underwent a repeat sleep study using auto-CPAP. The estimated AHI from auto-CPAP was compared with the AHI from an overnight polysomnogram (PSG) on auto-CPAP using Bland-Altman plot and likelihood ratio analyses. A PSG AHI cutoff of five events per hour was used to differentiate patients optimally treated with auto-CPAP from those with residual OSA on therapy.
RESULTS
Bland and Altman analysis showed good agreement between auto-CPAP AHI and PSG AHI. There was no significant bias when smart card estimates of AHI at home were compared to smart card estimates obtained in the sleep laboratory. An auto-CPAP cutoff for the AHI of six events per hour was shown to be optimal for differentiating patients with and without residual OSA with a sensitivity of 0.92 (95% confidence interval (CI) 0.76 to 0.98) and specificity of 0.90 (95% CI 0.82 to 0.95) with a positive likelihood ratio (LR) of 9.6 (95% CI 5.1 to 21.5) and a negative likelihood ratio of 0.085 (95% CI 0.02 to 0.25). Auto-CPAP AHI of eight events per hour yielded the optimal sensitivity (0.94, 95% CI 0.73 to 0.99) and specificity (0.90, 95% CI 0.82 to 0.95) with a positive LR of 9.6 (95% CI 5.23 to 20.31) and a negative LR of 0.065 (95% CI 0.004 to 0.279) to identify patients with a PSG AHI of > or = 10 events per hour.
CONCLUSION
Auto-CPAP estimate of AHI may be used to estimate residual AHI in patients with OSA of varying severity treated with auto-CPAP.
目的
自动持续气道正压通气（auto-CPAP）设备现在有一个智能卡（一种带有嵌入式集成电路的袖珍卡，可记录 CPAP 机的数据，如 CPAP 使用情况、CPAP 压力、大泄漏等），可估算治疗过程中的睡眠呼吸暂停低通气指数（AHI）。本研究的目的是确定在未经 CPAP 滴定研究的情况下，使用 auto-CPAP 治疗阻塞性睡眠呼吸暂停（OSA）患者时，auto-CPAP 对残余 AHI 的估计的准确性。
方法
我们研究了 2005 年 4 月至 2007 年 5 月期间使用 auto-CPAP 进行重复睡眠研究的 99 例 OSA 患者。使用 Bland-Altman 图和似然比分析比较 auto-CPAP 智能卡估计的 AHI 与整夜多导睡眠图（PSG）上的 AHI。使用 PSG AHI 截定点五小时每小时来区分最佳治疗的患者与治疗后仍有 OSA 的患者。
结果
Bland 和 Altman 分析表明，auto-CPAP AHI 与 PSG AHI 之间具有良好的一致性。当比较在家中智能卡估计的 AHI 与在睡眠实验室中获得的智能卡估计时，没有发现明显的偏差。当使用 auto-CPAP 时，AHI 的截定点为六小时每小时，可最佳地区分有无残余 OSA 的患者，其灵敏度为 0.92（95%置信区间（CI）为 0.76 至 0.98），特异性为 0.90（95%CI 为 0.82 至 0.95），阳性似然比（LR）为 9.6（95%CI 为 5.1 至 21.5），阴性似然比为 0.085（95%CI 为 0.02 至 0.25）。当使用 auto-CPAP 时，AHI 的截定点为八小时每小时，其灵敏度（0.94，95%CI 为 0.73 至 0.99）和特异性（0.90，95%CI 为 0.82 至 0.95）最高，阳性 LR 为 9.6（95%CI 为 5.23 至 20.31），阴性 LR 为 0.065（95%CI 为 0.004 至 0.279），可用于识别 PSG AHI 大于或等于 10 小时每小时的患者。
结论
在使用 auto-CPAP 治疗的不同严重程度的 OSA 患者中，可使用 auto-CPAP 估计 AHI 来估算残余 AHI。
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