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重症监护病房睡眠障碍呼吸的高发率:一项横断面研究。

High prevalence of sleep-disordered breathing in the intensive care unit - a cross-sectional study.

机构信息

Department of Neurology, Massachusetts General Hospital (MGH), 55 Fruit Street, Boston, MA, 02114, USA.

Clinical Data Animation Center (CDAC), MGH, Boston, MA, USA.

出版信息

Sleep Breath. 2023 Jun;27(3):1013-1026. doi: 10.1007/s11325-022-02698-9. Epub 2022 Aug 16.

Abstract

PURPOSE

Sleep-disordered breathing may be induced by, exacerbate, or complicate recovery from critical illness. Disordered breathing during sleep, which itself is often fragmented, can go unrecognized in the intensive care unit (ICU). The objective of this study was to investigate the prevalence, severity, and risk factors of sleep-disordered breathing in ICU patients using a single respiratory belt and oxygen saturation signals.

METHODS

Patients in three ICUs at Massachusetts General Hospital wore a thoracic respiratory effort belt as part of a clinical trial for up to 7 days and nights. Using a previously developed machine learning algorithm, we processed respiratory and oximetry signals to measure the 3% apnea-hypopnea index (AHI) and estimate AH-specific hypoxic burden and periodic breathing. We trained models to predict AHI categories for 12-h segments from risk factors, including admission variables and bio-signals data, available at the start of these segments.

RESULTS

Of 129 patients, 68% had an AHI ≥ 5; 40% an AHI > 15, and 19% had an AHI > 30 while critically ill. Median [interquartile range] hypoxic burden was 2.8 [0.5, 9.8] at night and 4.2 [1.0, 13.7] %min/h during the day. Of patients with AHI ≥ 5, 26% had periodic breathing. Performance of predicting AHI-categories from risk factors was poor.

CONCLUSIONS

Sleep-disordered breathing and sleep apnea events while in the ICU are common and are associated with substantial burden of hypoxia and periodic breathing. Detection is feasible using limited bio-signals, such as respiratory effort and SpO signals, while risk factors were insufficient to predict AHI severity.

摘要

目的

睡眠呼吸障碍可能由危重病引起、加重或使危重病恢复复杂化。睡眠期间呼吸紊乱通常是碎片化的,在重症监护病房(ICU)中可能无法识别。本研究的目的是使用单个呼吸带和血氧饱和度信号来调查 ICU 患者睡眠呼吸障碍的患病率、严重程度和危险因素。

方法

马萨诸塞州综合医院的三个 ICU 的患者佩戴了一个胸带式呼吸努力带,作为一项临床试验的一部分,最长可达 7 天 7 夜。使用之前开发的机器学习算法,我们处理呼吸和血氧信号以测量 3%的呼吸暂停低通气指数(AHI),并估计 AHI 特定的低氧负担和周期性呼吸。我们训练模型从风险因素(包括入院变量和生物信号数据)预测 12 小时段的 AHI 类别,这些因素可在这些段开始时获得。

结果

在 129 名患者中,68%的患者 AHI≥5;40%的患者 AHI>15,19%的患者 AHI>30。中位[四分位数范围]夜间低氧负担为 2.8[0.5, 9.8],白天为 4.2[1.0, 13.7]%min/h。AHI≥5 的患者中,26%有周期性呼吸。从危险因素预测 AHI 类别的表现不佳。

结论

在 ICU 期间,睡眠呼吸障碍和睡眠呼吸暂停事件很常见,与大量缺氧和周期性呼吸负担有关。使用有限的生物信号(如呼吸努力和 SpO 信号)可以实现检测,而危险因素不足以预测 AHI 严重程度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c9f6/9931933/3162c2f31cd6/nihms-1861736-f0001.jpg

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