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夜间睡眠动态特征可识别1型发作性睡病。

Nocturnal Sleep Dynamics Identify Narcolepsy Type 1.

作者信息

Pizza Fabio, Vandi Stefano, Iloti Martina, Franceschini Christian, Liguori Rocco, Mignot Emmanuel, Plazzi Giuseppe

机构信息

Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy.

IRCCS, Istituto delle Scienze Neurologiche, ASL di Bologna, Bologna, Italy.

出版信息

Sleep. 2015 Aug 1;38(8):1277-84. doi: 10.5665/sleep.4908.

Abstract

STUDY OBJECTIVES

To evaluate the reliability of nocturnal sleep dynamics in the differential diagnosis of central disorders of hypersomnolence.

DESIGN

Cross-sectional.

SETTING

Sleep laboratory.

PATIENTS

One hundred seventy-five patients with hypocretin-deficient narcolepsy type 1 (NT1, n = 79), narcolepsy type 2 (NT2, n = 22), idiopathic hypersomnia (IH, n = 22), and "subjective" hypersomnolence (sHS, n = 52).

INTERVENTIONS

None.

METHODS

Polysomnographic (PSG) work-up included 48 h of continuous PSG recording. From nocturnal PSG conventional sleep macrostructure, occurrence of sleep onset rapid eye movement period (SOREMP), sleep stages distribution, and sleep stage transitions were calculated. Patient groups were compared, and receiver operating characteristic (ROC) curve analysis was used to test the diagnostic utility of nocturnal PSG data to identify NT1.

RESULTS

Sleep macrostructure was substantially stable in the 2 nights of each diagnostic group. NT1 and NT2 patients had lower latency to rapid eye movement (REM) sleep, and NT1 patients showed the highest number of awakenings, sleep stage transitions, and more time spent in N1 sleep, as well as most SOREMPs at daytime PSG and at multiple sleep latency test (MSLT) than all other groups. ROC curve analysis showed that nocturnal SOREMP (area under the curve of 0.724 ± 0.041, P < 0.0001), percent of total sleep time spent in N1 (0.896 ± 0.023, P < 0.0001), and the wakefulness-sleep transition index (0.796 ± 0.034, P < 0.0001) had a good sensitivity and specificity profile to identify NT1 sleep, especially when used in combination (0.903 ± 0.023, P < 0.0001), similarly to SOREMP number at continuous daytime PSG (0.899 ± 0.026, P < 0.0001) and at MSLT (0.956 ± 0.015, P < 0.0001).

CONCLUSIONS

Sleep macrostructure (i.e. SOREMP, N1 timing) including stage transitions reliably identifies hypocretin-deficient narcolepsy type 1 among central disorders of hypersomnolence.

摘要

研究目的

评估夜间睡眠动态变化在中枢性过度嗜睡症鉴别诊断中的可靠性。

设计

横断面研究。

地点

睡眠实验室。

患者

175例患者,其中1型发作性睡病伴下丘脑分泌素缺乏(NT1,n = 79)、2型发作性睡病(NT2,n = 22)、特发性嗜睡症(IH,n = 22)和“主观性”过度嗜睡症(sHS,n = 52)。

干预措施

无。

方法

多导睡眠图(PSG)检查包括连续48小时的PSG记录。根据夜间PSG的传统睡眠宏观结构,计算睡眠起始快速眼动期(SOREMP)的发生率、睡眠阶段分布及睡眠阶段转换情况。对患者组进行比较,并采用受试者工作特征(ROC)曲线分析来检验夜间PSG数据对识别NT1的诊断效用。

结果

每个诊断组的两个夜间睡眠宏观结构基本稳定。NT1和NT2患者快速眼动(REM)睡眠潜伏期较短,NT1患者觉醒次数、睡眠阶段转换次数最多,N1睡眠期时间更长,且白天PSG和多次睡眠潜伏期试验(MSLT)中的SOREMP数量多于所有其他组。ROC曲线分析显示,夜间SOREMP(曲线下面积为0.724±0.041,P<0.0001)、N1睡眠占总睡眠时间的百分比(0.896±0.023,P<0.0001)以及觉醒-睡眠转换指数(0.796±0.034,P<0.0001)对识别NT1睡眠具有良好的敏感性和特异性,尤其是联合使用时(0.903±0.023,P<0.0001),类似于连续白天PSG(0.899±0.026,P<0.0001)和MSLT(0.956±0.015,P<0.0001)时的SOREMP数量。

结论

包括阶段转换在内的睡眠宏观结构(即SOREMP、N1时间)能够可靠地在中枢性过度嗜睡症中识别出1型发作性睡病伴下丘脑分泌素缺乏。

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