Wireless wearable sensors can facilitate rapid detection of sleep apnea in hospitalized stroke patients.

Autor:	Sindorf J; Max Nader Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, USA., Szabo AL; Feinberg School of Medicine, Northwestern University, Chicago, IL, USA., O'Brien MK; Max Nader Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, USA.; Feinberg School of Medicine, Northwestern University, Chicago, IL, USA., Sunderrajan A; Department of Medicine, University of Chicago Medicine, Chicago, IL, USA., Knutson KL; Feinberg School of Medicine, Northwestern University, Chicago, IL, USA., Zee PC; Feinberg School of Medicine, Northwestern University, Chicago, IL, USA., Wolfe L; Feinberg School of Medicine, Northwestern University, Chicago, IL, USA., Arora VM; Department of Medicine, University of Chicago Medicine, Chicago, IL, USA., Jayaraman A; Max Nader Center for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, USA.; Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Jazyk:	angličtina
Zdroj:	Sleep [Sleep] 2024 Nov 08; Vol. 47 (11).
DOI:	10.1093/sleep/zsae123
Abstrakt:	Study Objectives: To evaluate wearable devices and machine learning for detecting sleep apnea in patients with stroke at an acute inpatient rehabilitation facility (IRF). Methods: A total of 76 individuals with stroke wore a standard home sleep apnea test (ApneaLink Air), a multimodal, wireless wearable sensor system (ANNE), and a research-grade actigraphy device (ActiWatch) for at least 1 night during their first week after IRF admission as part of a larger clinical trial. Logistic regression algorithms were trained to detect sleep apnea using biometric features obtained from the ANNE sensors and ground truth apnea rating from the ApneaLink Air. Multiple algorithms were evaluated using different sensor combinations and different apnea detection criteria based on the apnea-hypopnea index (AHI ≥ 5, AHI ≥ 15). Results: Seventy-one (96%) participants wore the ANNE sensors for multiple nights. In contrast, only 48 participants (63%) could be successfully assessed for obstructive sleep apnea by ApneaLink; 28 (37%) refused testing. The best-performing model utilized photoplethysmography (PPG) and finger-temperature features to detect moderate-severe sleep apnea (AHI ≥ 15), with 88% sensitivity and a positive likelihood ratio (LR+) of 44.00. This model was tested on additional nights of ANNE data achieving 71% sensitivity (10.14 LR+) when considering each night independently and 86% accuracy when averaging multi-night predictions. Conclusions: This research demonstrates the feasibility of accurately detecting moderate-severe sleep apnea early in the stroke recovery process using wearable sensors and machine learning techniques. These findings can inform future efforts to improve early detection for post-stroke sleep disorders, thereby enhancing patient recovery and long-term outcomes. Clinical Trial: SIESTA (Sleep of Inpatients: Empower Staff to Act) for Acute Stroke Rehabilitation, https://clinicaltrials.gov/study/NCT04254484?term=SIESTA&checkSpell=false&rank=1, NCT04254484. (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)
Databáze:	MEDLINE
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