Vakulin Andrew, D'Rozario Angela, Kim Jong-Won, Watson Brooke, Cross Nathan, Wang David, Coeytaux Alessandra, Bartlett Delwyn, Wong Keith, Grunstein Ronald
NHMRC Centre of Research Excellence CIRUS and NEUROSLEEP, Woolcock Institute of Medical Research, The University of Sydney, Sydney, Australia; Adelaide Institute for Sleep Health: A Flinders Centre of Research Excellence, School of Medicine, Faculty of Medicine, Nursing and Health Sciences, Flinders University, Bedford Park, South Australia, Australia.
NHMRC Centre of Research Excellence CIRUS and NEUROSLEEP, Woolcock Institute of Medical Research, The University of Sydney, Sydney, Australia; Sydney Local Health District, Sydney, New South Wales, Australia; Sydney Medical School, University of Sydney, Australia.
Clin Neurophysiol. 2016 Feb;127(2):1428-1435. doi: 10.1016/j.clinph.2015.09.004. Epub 2015 Sep 25.
To improve identification of obstructive sleep apnea (OSA) patients at risk of driving impairment, this study explored predictors of driving performance impairment in untreated OSA patients using clinical PSG metrics, sleepiness questionnaires and quantitative EEG markers from routine sleep studies.
Seventy-six OSA patients completed sleepiness questionnaires and driving simulator tests in the evening of their diagnostic sleep study. All sleep EEGs were subjected to quantitative power spectral analysis. Correlation and multivariate linear regression were used to identify the strongest predictors of driving simulator performance.
Absolute EEG spectral power across all frequencies (0.5-32 Hz) throughout the entire sleep period and separately in REM and NREM sleep, (r range 0.239-0.473, all p<0.05), as well as sleep onset latency (r=0.273, p<0.017) positively correlated with driving simulator steering deviation. Regression models revealed that amongst clinical and qEEG variables, the significant predictors of worse steering deviation were greater total EEG power during NREM and REM sleep, greater beta EEG power in NREM and greater delta EEG power in REM (range of variance explained 5-17%, t range 2.29-4.0, all p<0.05) and sleep onset latency (range of variance explained 4-9%, t range 2.15-2.5, all p<0.05).
In OSA patients, increased EEG power, especially in the faster frequency (beta) range during NREM sleep and slower frequency (delta) range in REM sleep were associated with worse driving performance, while no relationships were observed with clinical metrics e.g. apnea, arousal or oxygen indices.
Quantitative EEG analysis in OSA may provide useful markers of driving impairment risk. Future studies are necessary to confirm these findings and assess the clinical significance of quantitative EEG as predictors of driving impairment in OSA.
为了改善对有驾驶功能受损风险的阻塞性睡眠呼吸暂停（OSA）患者的识别，本研究使用临床多导睡眠图（PSG）指标、嗜睡问卷以及常规睡眠研究中的定量脑电图标记，探究未经治疗的OSA患者驾驶性能受损的预测因素。
76名OSA患者在其诊断性睡眠研究当晚完成了嗜睡问卷和驾驶模拟器测试。所有睡眠脑电图均进行了定量功率谱分析。采用相关性分析和多元线性回归来确定驾驶模拟器性能的最强预测因素。
整个睡眠期以及快速眼动（REM）睡眠期和非快速眼动（NREM）睡眠期各频率（0.5 - 32赫兹）的绝对脑电图频谱功率（r范围为0.239 - 0.473，所有p<0.05），以及入睡潜伏期（r = 0.273，p<0.017）与驾驶模拟器转向偏差呈正相关。回归模型显示，在临床和定量脑电图变量中，转向偏差更严重的显著预测因素是NREM和REM睡眠期间更高的总脑电图功率、NREM睡眠中更高 的β脑电图功率、REM睡眠中更高的δ脑电图功率（解释方差范围为5 - 17%，t范围为2.29 - 4.0，所有p<0.05）以及入睡潜伏期（解释方差范围为4 - 9%，t范围为2.15 - 2.5，所有p<0.05）。
在OSA患者中，脑电图功率增加，尤其是NREM睡眠期间较快频率（β）范围和REM睡眠期间较慢频率（δ）范围的脑电图功率增加，与较差的驾驶性能相关，而未观察到与临床指标如呼吸暂停、觉醒或氧合指数之间的关系。
OSA中的定量脑电图分析可能提供驾驶功能受损风险的有用标记。未来有必要开展研究以证实这些发现，并评估定量脑电图作为OSA患者驾驶功能受损预测指标的临床意义。
