首页> 外文期刊>Sleep & breathing =: Schlaf & Atmung >Accuracy of autotitrating CPAP to estimate the residual Apnea-Hypopnea Index in patients with obstructive sleep apnea on treatment with autotitrating CPAP.
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Accuracy of autotitrating CPAP to estimate the residual Apnea-Hypopnea Index in patients with obstructive sleep apnea on treatment with autotitrating CPAP.

机译:自动滴定CPAP估计使用自动滴定CPAP治疗阻塞性睡眠呼吸暂停患者的残余呼吸暂停低通气指数的准确性。

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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.
机译:目的:自动滴定连续气道正压(auto-CPAP)设备现在具有智能卡(具有嵌入式集成电路的袖珍卡,可记录来自CPAP机器的数据,例如CPAP使用情况,CPAP压力,大泄漏等)。可以估计治疗时的呼吸暂停低通气指数(AHI)。这项研究的目的是确定使用自动CPAP进行CPAP滴定研究的阻塞性睡眠呼吸暂停(OSA)患者中自动CPAP估计残留AHI的准确性。患者与方法:我们研究了2005年4月至2007年5月间99例OSA患者,他们使用自动CPAP进行了重复睡眠研究。使用Bland-Altman图和似然比分析,将来自自动CPAP的估计AHI与来自自动CPAP的夜间多导睡眠图(PSG)的AHI进行比较。每小时五个事件的PSG AHI临界值用于区分接受自动CPAP最佳治疗的患者与治疗中残留OSA的患者。结果:Bland和Altman分析表明,自动CPAP AHI和PSG AHI之间具有良好的一致性。将在家中AHI的智能卡估计值与在睡眠实验室获得的智能卡估计值进行比较时,没有显着偏差。每小时自动发生6个事件的AHI的自动CPAP阈值被证明是区分具有和没有残留OSA的患者的最佳选择,其敏感性为0.92(95%置信区间(CI)0.76至0.98),特异性为0.90(95% CI为0.82至0.95),正似然比(LR)为9.6(95%CI为5.1至21.5),负似然比为0.085(95%CI为0.02至0.25)。每小时进行8次事件的Auto-CPAP AHI产生最佳灵敏度(0.94,95%CI 0.73至0.99)和特异性(0.90,95%CI 0.82至0.95),LR值为9.6(95%CI 5.23至20.31)和阴性LR为0.065(95%CI为0.004至0.279),以识别PSG AHI每小时>或= 10事件的患者。结论:自动CPAP估计的AHI可用于估计使用自动CPAP治疗的严重程度不同的OSA患者的残余AHI。

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