联想(心理学)
唤醒
可能性
冲程(发动机)
优势比
医学
内科学
心理学
逻辑回归
神经科学
工程类
机械工程
心理治疗师
作者
Reeman Marzouqah,Sean Jairam,Ivan Ntale,Kathleen S. J. Preston,Sandra E. Black,Richard H. Swartz,Brian J. Murray,Magdy Younes,Mark I. Boulos
出处
期刊:Sleep Medicine
[Elsevier BV]
日期:2025-03-01
卷期号:129: 257-263
被引量:2
标识
DOI:10.1016/j.sleep.2025.02.048
摘要
Obstructive Sleep Apnea (OSA) affects up to 70 % of post-stroke patients, complicating recovery and rehabilitation. This study aimed to evaluate the utility of the Odds Ratio Product (ORP), a continuous EEG-derived metric of sleep depth, in predicting conventional respiratory and arousal measures in stroke patients. We hypothesized that ORP metrics will predict conventional measures in patients with a history of stroke or Transient ischemic attack (TIA). A retrospective analysis was conducted on 113 stroke/TIA individuals who underwent in-laboratory polysomnography (PSG). ORP metrics, including ORPnrem, ORPrem, ORP9, and Wake Intrusion Indices (WIIs), were analyzed using multivariate linear regression models. Models were stratified by OSA status. Standardized coefficients were used to assess associations with the apnea-hypopnea index (AHI), respiratory disturbance index (RDI), and arousal indices. ORP metrics demonstrated statistically significant associations with conventional respiratory and arousal measures, with varying predictive strength across models. Specifically, ORPnrem and WIIs exhibited strong predictive effects across all models. ORP9 significantly predicted respiratory and arousal measures in the overall sample and the OSA subgroup, but its predictive value diminished in the non-OSA subgroup. ORPrem was statistically significantly associated with respiratory and arousal measures; however, its associations with arousal measures were weaker in participants with OSA compared to those without OSA. ORP metrics have the potential to refine OSA diagnoses and improve therapeutic strategies in post-stroke/TIA populations. Their integration into sleep assessments could facilitate early intervention and potentially optimize stroke recovery outcomes, addressing gaps in current evaluation methods.
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