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

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

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  9   10   11
Affiliations

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

Abigail A Bucklin et al. Sleep Breath. 2023 Jun.

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

Keywords: Apnea; Critical care; Intensive care unit; Machine learning; Sleep-disordered breathing.

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Conflict of interest statement

Dr. Westover is a co-founder of Beacon Biosignals and reports grants from NIH, during the conduct of the study; Dr. Cash reports other COI from Neuralink, Paradromics, and Synchron, and Beacon Biosignals, outside the submitted work. Dr. Thomas reports personal fees from GLG Councils, Guidepoint Global, and Jazz Pharmaceutics, outside the submitted work. In addition, Dr. Thomas has a patent ECG-spectrogram with royalties paid by MyCardio, LLC, a patent Auto-CPAP with royalties paid by DeVilbiss-Drive, and an unlicensed patent CO2 device for central/complex sleep apnea issued. Dr. Thompson reports consulting for Bayer, Novartis, and Thetis, outside the submitted work. In addition, Dr. Kuller has a patent: Patent US10123724B2 “Breath volume monitoring system and method” issued. Dr. Kuller reports non-financial support from Myair Inc., during the conduct of the study, and non-financial support from Myair Inc, outside the submitted work.

Figures

Fig. 1
Fig. 1
Swimmer plot showing the apnea detections for each patient in the study. Each row represents one patient, and each bin represents 1 h colored as the amount of apnea events detected. Apneic events were only detected when patients were asleep. Each patient is aligned to the same day time so that the ticks represent the 20:00 timestamp. Patients have an indication on the plot if they had a previous documented diagnosis of obstructive sleep apnea, and whether or not they were receiving oxygen therapy. The patients are split depending on whether they were admitted to a medical ICU or a surgical ICU
Fig. 2
Fig. 2
AHI and hypoxic burden distribution for 12-h segments. Distributions of apnea–hypopnea index (Panel A) and the apnea-specific hypoxic burden (Panel B) among all 12-h segments included in the analysis, i.e., at least 90 min of data available and at least 1 h of sleep. 8am–8 pm and 8 pm–8am segments are defined to be “day” and “night” respectively. Left panels show ordinary histograms, and right panels show cumulative histograms. We have found similar amount of AHI levels for day and night periods. Fifty-four percent of all nights show an AHI > 5 and 27% an AHI > 15. Similarly, the hypoxic burden is similar for day and night and 17% of nights show a hypoxic burden larger than 15%desaturation-min/h
Fig. 3
Fig. 3
Example detection with signal. Twenty-minute example respiratory and SpO2 signals with detected apnea events for each apnea–hypopnea index category. A 70-year-old female with previous OSA diagnosis admitted to medical ICU due to respiratory failure, AHI = 2. B 56-year-old male without previous OSA diagnosis admitted to surgical ICU due to myxoid chondrosarcoma, AHI = 12. C 75-year-old male without previous OSA diagnosis admitted to surgical ICU after a fall, AHI = 22. D 57-year-old male without previous OSA diagnosis admitted to medical ICU due to gastrointestinal bleeding, AHI = 54
Fig. 4
Fig. 4
Self-similar breathing. Example respiratory and SpO2 signal with apnea detections and detected high self-similarity (periodic breathing). Patient was female, 88 years old, and admitted to the surgical ICU due to a hip fracture
See this image and copyright information in PMC

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