666 Sleep Architecture in the Intensive Care Unit As Revealed via Breathing and Heart Rate Variability. (3rd May 2021)

• Record Type: Journal Article
• Authors: Ganglberger, Wolfgang; Velpula Krishnamurthy, Parimala; Bucklin, Abigail; Tesh, Ryan; Da Silva Cardoso, Madalena; Sun, Haoqi; Adra, Noor; Leone, Michael; Qader Quadri, Syed Abdul; Ye, Elissa; Kuller, David; Thompson, Taylor; Akeju, Oluwaseun; Thomas, Robert; Westover, Michael
• Is Part Of: Sleep. Volume 44(2021)Supplement 2
• Page Start: A260
• Page End: A261
• Publication Date: 2021-05-03
• Subjects: Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsab072.664
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: Sleep in the intensive care unit (ICU) is difficult to measure by conventional polysomnography. We investigated the feasibility of assessing sleep state from readily available ICU signals: heart rate variability (HRV) from electrocardiography and breathing from a wearable respiratory band. We compared findings with an age and sex matched sleep laboratory group. Methods: As part of a clinical trial, 102 adult non-ventilated patients in three ICUs in the Massachusetts General Hospital wore a respiratory band. Both heart rate variability (RR-intervals) from ECG, and breathing (respiratory effort waveforms) data for up to seven days per patient were obtained. 220 age- and sex-matched subjects from a sleep lab cohort who wore the same respiratory effort band and ECG were selected for comparison. We staged sleep from the HRV and breathing data using previously published deep neural network models. We defined discordant sleep epochs as those where HRV- and breathing-based models disagreed. Agreement was computed for the following pairs: (R, R), (N1, N1), (N2, N2), (N3, N3), (N1, W), (N1, N2), (N2, N3). Results: Demographics: Mean(STD) age: ICU 68(9), sleeplab 68(9); BMI: ICU 27(6), sleeplab 31(6); ICU 40% female, sleeplab 44% female; race: ICU%:Sleeplab% 90:69 White, 5:4 Black, 2:7 Asian. 34% of ICU-subjects were in a medical ICU, 66% in a surgical ICU. Mean total sleep duration in the ICU was 8.9 hours (4.5h concordant, 4.4h discordant sleep). We observed increased amounts of discordant sleep in the ICU compared with the sleeplab cohort (4.4h vs. 1h, p<0.01). We found different REM sleep distributions (p<0.01) with reduced median (10% vs. 20%) but elevated 90% quantile (45% vs. 26%), elevated N1(%) (41% vs. 26%, p<0.05), reduced N2(%) (19 vs. 44, p<0.01), and reduced N2+N3(%) (34 vs. 59, p<0.05). We further observed higher mean respiratory rate (17.4 vs. 15.9 breaths per minute, p<0.01), lower inter-breath-intervals (3.9 vs. 4.7 seconds per breath, p<0.01), and more breathing variability than in sleeplab AHI<5 group but less than in AHI>15 group. Conclusion: HRV and respiratory-based measures can assess sleep in the ICU. The findings of increased discordant sleep in the ICU might stem from limitations of the models, fundamental changes in sleep biology during critical illness, pharmaceutical drugs, sleep fragmentation, and/or associated pathology in the ICU. Support (if any):