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通过生存曲线分析测量睡眠连续性。

Sleep continuity measured by survival curve analysis.

作者信息

Norman Robert G, Scott Marc A, Ayappa Indu, Walsleben Joyce A, Rapoport David M

机构信息

Division of Pulmonary & Critical Care Medicine, Department of Medicine, New York University School of Medicine, New York, NY 10016, USA.

出版信息

Sleep. 2006 Dec;29(12):1625-31. doi: 10.1093/sleep/29.12.1625.

Abstract

STUDY OBJECTIVES

To develop and demonstrate the utility of measures of sleep continuity based on survival analysis techniques.

DESIGN

Retrospective.

SETTING

University sleep laboratory.

PATIENTS

Anonymous nocturnal polysomnograms from 10 normal subjects, 10 subjects with mild sleep disordered breathing (SDB) (apnea-hypopnea index [AHI], 15-30/hr), and 10 subjects with moderate/severe SDB (AHI > 30/hr).

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

Hypnograms were analyzed to measure the lengths of episodes of contiguous sleep and processed using several common survival analysis techniques. Using separate survival curves for each group to describe the durations of continuous epochs of sleep (sleep run lengths), statistically significant differences were found between all groups (p < .001) as well as between the normal and mild SDB groups (p < .001), suggesting differences in the stability of sleep. Using survival regression techniques applied separately to each subject, statistically significant differences were found among all three groups (p < .001) and, more importantly, between the normal and mild SDB groups (p < .005). Similarly, estimation of sleep continuity based on the pooled sleep run data for each group also showed statistically significant differences (normal vs mild, p < .001; Normal vs moderate/severe, p < .001). In addition, the latter technique showed that changes in the "stability" of sleep could be demonstrated as runs progressed.

CONCLUSION

Survival curve analysis of the lengths of runs of contiguous sleep provides a potentially useful method of quantifying sleep continuity. The results suggest that sleep becomes more stable as sleep progresses in normal subjects and those with mild SDB and less stable in subjects with moderate/severe SDB.

摘要

研究目的

基于生存分析技术开发并证明睡眠连续性测量方法的实用性。

设计

回顾性研究。

地点

大学睡眠实验室。

患者

来自10名正常受试者、10名轻度睡眠呼吸障碍(SDB)受试者(呼吸暂停低通气指数[AHI],15 - 30次/小时)和10名中度/重度SDB受试者(AHI > 30次/小时)的匿名夜间多导睡眠图。

干预措施

无。

测量与结果

分析睡眠图以测量连续睡眠时段的长度,并使用几种常见的生存分析技术进行处理。使用每组单独的生存曲线来描述连续睡眠时段的持续时间(睡眠持续长度),发现所有组之间(p < .001)以及正常组和轻度SDB组之间(p < .001)存在统计学显著差异,表明睡眠稳定性存在差异。对每个受试者分别应用生存回归技术,发现三组之间存在统计学显著差异(p < .001),更重要的是,正常组和轻度SDB组之间存在差异(p < .005)。同样,基于每组汇总的睡眠持续数据估计睡眠连续性也显示出统计学显著差异(正常组与轻度组,p < .001;正常组与中度/重度组,p < .001)。此外,后一种技术表明,随着睡眠时段的推进,可以证明睡眠“稳定性”的变化。

结论

对连续睡眠时段长度进行生存曲线分析提供了一种量化睡眠连续性的潜在有用方法。结果表明,在正常受试者和轻度SDB受试者中,随着睡眠的推进,睡眠变得更加稳定,而在中度/重度SDB受试者中则不太稳定。

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