Pitkänen Minna, Nath Rajdeep Kumar, Korkalainen Henri, Nikkonen Sami, Mahamid Alaa, Oksenberg Arie, Duce Brett, Töyräs Juha, Kainulainen Samu, Leppänen Timo
Department of Technical Physics, University of Eastern Finland, Kuopio, Finland.
VTT Technical Research Centre of Finland Ltd, Kuopio, Finland.
Sleep Adv. 2023 Dec 22;5(1):zpad054. doi: 10.1093/sleepadvances/zpad054. eCollection 2024.
Polygraphy (PG) is often used to diagnose obstructive sleep apnea (OSA). However, it does not use electroencephalography, and therefore cannot estimate sleep time or score arousals and related hypopneas. Consequently, the PG-derived respiratory event index (REI) differs from the polysomnography (PSG)-derived apnea-hypopnea index (AHI). In this study, we comprehensively analyzed the differences between AHI and REI. Conventional AHI and REI were calculated based on total sleep time (TST) and total analyzed time (TAT), respectively, from two different PSG datasets ( = 1561). Moreover, TAT-based AHI (AHI) and TST-based REI (REI) were calculated. These indices were compared keeping AHI as the gold standard. The REI, AHI, and REI were significantly lower than AHI ( < 0.0001, ≤ 0.002, and ≤ 0.01, respectively). The total classification accuracy of OSA severity based on REI was 42.1% and 72.8% for two datasets. Based on AHI, the accuracies were 68.4% and 85.9%, and based on REI, they were 65.9% and 88.5% compared to AHI. AHI was most correlated with REI ( = 0.98 and = 0.99 for the datasets) and least with REI ( = 0.92 and = 0.97). Compared to AHI, REI had the largest mean absolute errors (13.9 and 6.7) and REI the lowest (5.9 and 1.9). REI had the lowest sensitivities (42.1% and 72.8%) and specificities (80.7% and 90.9%) in both datasets. Based on these present results, REI underestimates AHI. Furthermore, these results indicate that arousal-related hypopneas are an important measure for accurately classifying OSA severity.
多导睡眠监测（PG）常用于诊断阻塞性睡眠呼吸暂停（OSA）。然而，它不使用脑电图，因此无法估计睡眠时间或对觉醒及相关呼吸浅慢进行评分。因此，PG得出的呼吸事件指数（REI）与多导睡眠图（PSG）得出的呼吸暂停低通气指数（AHI）不同。在本研究中，我们全面分析了AHI和REI之间的差异。传统的AHI和REI分别基于两个不同的PSG数据集（n = 1561）的总睡眠时间（TST）和总分析时间（TAT）计算得出。此外，还计算了基于TAT的AHI（AHI）和基于TST的REI（REI）。以AHI作为金标准对这些指数进行比较。REI、AHI和REI均显著低于AHI（分别为P < 0.0001、P ≤ 0.002和P ≤ 0.01）。基于REI的OSA严重程度的总分类准确率在两个数据集中分别为42.1%和72.8%。基于AHI的准确率分别为68.4%和85.9%，与AHI相比，基于REI的准确率分别为65.9%和88.5%。AHI与REI的相关性最高（数据集中r = 0.98和r = 0.99），与REI的相关性最低（r = 0.92和r = 0.97）。与AHI相比，REI的平均绝对误差最大（分别为13.9和6.7），而REI的平均绝对误差最低（分别为5.9和1.9）。在两个数据集中，REI的敏感性（分别为42.1%和72.8%）和特异性（分别为80.7%和90.9%）最低。基于目前这些结果，REI低估了AHI。此外，这些结果表明，与觉醒相关的呼吸浅慢是准确分类OSA严重程度的一项重要指标。
