High prevalence of sleep-disordered breathing in the intensive care unit — a cross-sectional study,Sleep and Breathing

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High prevalence of sleep-disordered breathing in the intensive care unit — a cross-sectional study
Sleep and Breathing ( IF 2.1 ) Pub Date : 2022-08-16 , DOI: 10.1007/s11325-022-02698-9
Abigail A Bucklin _{1,

2} , Wolfgang Ganglberger _{1,

2,

3,

4} , Syed A Quadri ₂ , Ryan A Tesh _{1,

2} , Noor Adra _{1,

2} , Madalena Da Silva Cardoso _{1,

2} , Michael J Leone _{1,

2} , Parimala Velpula Krishnamurthy _{1,

2} , Aashritha Hemmige _{1,

2} , Subapriya Rajan _{1,

2} , Ezhil Panneerselvam _{1,

2} , Luis Paixao _{1,

2} , Jasmine Higgins _{1,

2} , Muhammad Abubakar Ayub _{1,

2} , Yu-Ping Shao _{1,

2} , Elissa M Ye _{1,

2} , Brian Coughlin ₁ , Haoqi Sun _{1,

2,

4} , Sydney S Cash _{1,

2} , B Taylor Thompson ₅ , Oluwaseun Akeju _{4,

6} , David Kuller ₇ , Robert J Thomas _{2,

8} , M Brandon Westover _{1,

2,

4}

Affiliation

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 SpO₂ signals, while risk factors were insufficient to predict AHI severity.

中文翻译：

重症监护病房睡眠呼吸障碍的患病率很高——一项横断面研究

目的

睡眠呼吸障碍可能由危重疾病的恢复引起、加剧或复杂化。睡眠期间的呼吸紊乱本身往往是碎片化的，在重症监护病房 (ICU) 中可能无法被识别出来。本研究的目的是使用单一呼吸带和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

作为长达 7 天 7 夜的临床试验的一部分，马萨诸塞州总医院三个 ICU 的患者佩戴胸式呼吸努力带。使用先前开发的机器学习算法，我们处理呼吸和血氧饱和度信号，以测量 3% 呼吸暂停低通气指数 (AHI)，并估计 AH 特定的缺氧负担和周期性呼吸。我们训练模型根据风险因素预测 12 小时段的 AHI 类别，包括这些段开始时可用的入院变量和生物信号数据。

结果

在 129 名患者中，68% 的 AHI ≥ 5； 40% 的患者在病情危重时 AHI > 15，19% 的 AHI > 30。夜间缺氧负荷中位数[四分位距]为 2.8 [0.5, 9.8] %min/h，白天为 4.2 [1.0, 13.7] %min/h。在 AHI ≥ 5 的患者中，26% 有周期性呼吸。根据风险因素预测 AHI 类别的性能很差。