Elliott Rosalind, Rai Tapan, McKinley Sharon
Faculty of Health, University of Technology Sydney, Broadway 2007, New South Wales, Australia.
School of Mathematical Sciences, Faculty of Science, University of Technology Sydney, Broadway 2007, New South Wales, Australia.
J Crit Care. 2014 Oct;29(5):859-63. doi: 10.1016/j.jcrc.2014.05.015. Epub 2014 May 29.
The aims of the current study were to describe the extrinsic and intrinsic factors affecting sleep in critically ill patients and to examine potential relationships with sleep quality.
Sleep was recorded using polysomnography (PSG) and self-reports collected in adult patients in intensive care. Sound and illuminance levels were recorded during sleep recording. Objective sleep quality was quantified using total sleep time divided by the number of sleep periods (PSG sleep period time ratio). A regression model was specified using the "PSG sleep period time ratio" as a dependent variable.
Sleep was highly fragmented. Patients rated noise and light as the most sleep disruptive. Continuous equivalent sound levels were 56 dB (A). Median daytime illuminance level was 74 lux, and nighttime levels were 1 lux. The regression model explained 25% of the variance in sleep quality (P = .027); the presence of an artificial airway was the only statistically significant predictor in the model (P = .007).
The presence of an artificial airway during sleep monitoring was the only significant predictor in the regression model and may suggest that although potentially uncomfortable, an artificial airway may actually promote sleep. This requires further investigation.
本研究的目的是描述影响重症患者睡眠的外在和内在因素，并探讨与睡眠质量的潜在关系。
使用多导睡眠图（PSG）记录睡眠情况，并收集成年重症监护患者的自我报告。在睡眠记录期间记录声音和光照水平。客观睡眠质量通过总睡眠时间除以睡眠周期数（PSG睡眠周期时间比）来量化。以“PSG睡眠周期时间比”作为因变量建立回归模型。
睡眠高度碎片化。患者将噪音和光线评为最干扰睡眠的因素。连续等效声级为56 dB（A）。白天光照水平中位数为74勒克斯，夜间为1勒克斯。回归模型解释了睡眠质量变异的25%（P = .027）；人工气道的存在是模型中唯一具有统计学意义的预测因素（P = .007）。
睡眠监测期间人工气道的存在是回归模型中唯一显著的预测因素，这可能表明尽管人工气道可能会带来不适，但实际上可能会促进睡眠。这需要进一步研究。
