The estimation of excessive daytime sleepiness in post-stroke patients - a polysomnographic study

Martynowicz, Helena; Jodkowska, Anna; Skomro, Robert; Gać, Paweł; Bryłka, Agnieszka; Bladowski, Maciej; Wojakowska, Anna; Mazur, Grzegorz; Poręba, Rafał

doi:10.1016/j.resp.2019.05.013

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…So far, the bed sensor has appeared to be suitable for measurement of HR and RR in laboratory studies with healthy subjects [47][48][49]. Previous studies found significantly higher HR in low paraplegia SCI patients compared to healthy controls [37] and similar HR in stroke patients and healthy controls [38]. The higher HR in iSCI compared to stroke patients in this study supports higher nocturnal HR in iSCI patients.…”

Section: Discussionsupporting

confidence: 72%

“…We hypothesized that the bed sensor is feasible in inpatient rehabilitation and is considered feasible if at least 95% of the nights were captured and the total coverage of nocturnal HR and RR was above 80% [26,27,36]. Based on the literature, a higher nocturnal HR for iSCI compared to stroke patients was hypothesized [37,38], which serves as a first indication of group validity of the bed sensor.…”

Section: Introductionmentioning

confidence: 99%

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Bed Sensor Technology for Objective Sleep Monitoring Within the Clinical Rehabilitation Setting: Observational Feasibility Study (Preprint)

Hendriks¹,

Lotringen²,

Hulst³

et al. 2020

Preprint

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BACKGROUND Since adequate sleep is essential for optimal inpatient rehabilitation, there is an increased interest in sleep assessment. Unobtrusive, contactless, portable bed sensors show great potential for objective sleep analysis. OBJECTIVE The aim of this study was to investigate the feasibility of a bed sensor for continuous sleep monitoring overnight in a clinical rehabilitation center. METHODS Patients with incomplete spinal cord injury (iSCI) or stroke were monitored overnight for a 1-week period during their in-hospital rehabilitation using the Emfit QS bed sensor. Feasibility was examined based on missing measurement nights, coverage percentages, and missing periods of heart rate (HR) and respiratory rate (RR). Furthermore, descriptive data of sleep-related parameters (nocturnal HR, RR, movement activity, and bed exits) were reported. RESULTS In total, 24 participants (12 iSCI, 12 stroke) were measured. Of the 132 nights, 5 (3.8%) missed sensor data due to Wi-Fi (2), slipping away (1), or unknown (2) errors. Coverage percentages of HR and RR were 97% and 93% for iSCI and 99% and 97% for stroke participants. Two-thirds of the missing HR and RR periods had a short duration of ≤120 seconds. Patients with an iSCI had an average nocturnal HR of 72 (SD 13) beats per minute (bpm), RR of 16 (SD 3) cycles per minute (cpm), and movement activity of 239 (SD 116) activity points, and had 86 reported and 84 recorded bed exits. Patients with a stroke had an average nocturnal HR of 61 (SD 8) bpm, RR of 15 (SD 1) cpm, and movement activity of 136 (SD 49) activity points, and 42 reported and 57 recorded bed exits. Patients with an iSCI had significantly higher nocturnal HR (t18=−2.1, P=.04) and movement activity (t18=−1.2, P=.02) compared to stroke patients. Furthermore, there was a difference between self-reported and recorded bed exits per night in 26% and 38% of the nights for iSCI and stroke patients, respectively. CONCLUSIONS It is feasible to implement the bed sensor for continuous sleep monitoring in the clinical rehabilitation setting. This study provides a good foundation for further bed sensor development addressing sleep types and sleep disorders to optimize care for rehabilitants.

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Section: Discussionsupporting

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Bed Sensor Technology for Objective Sleep Monitoring Within the Clinical Rehabilitation Setting: Observational Feasibility Study (Preprint)

Hendriks¹,

Lotringen²,

Hulst³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…80 A 2019 study recorded overnight PSG in 33 patients more than 3 months after stroke and compared them with an equal number of healthy nonstroke controls. 81 Excessive daytime sleepiness (EDS) in the stroke group correlated with mean heart rate during sleep and REM duration. In the nonstroke group (but not the stroke group), EDS correlated with sleep apnea severity (AHI, ODI, nadir SpO2).…”

Section: Polysomnographic Changes In Sleep Architecture In Acute Strokementioning

confidence: 99%

Stroke: What's Sleep Got to Do With It?

Zafar

Dhruv

Anwar

et al. 2022

Journal of Clinical Neurophysiology

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Ischemic strokes most often occur between 6 AM and 12 AM after awakening from sleep but up to 30% occur during sleep. Wake-up strokes (WUS) are new focal neurological deficit(s) persisting for $ 24 hours attributable to an ischemic event present on patient awakening. Obstructive sleep apnea (OSA) is a major risk factor for WUS because it compounds the instability of the morning environment and increases the likelihood of cardiovascular events, including hypertension, atrial fibrillation, right-to-left shunts, and stroke. Circadian-driven alterations in structural, homeostatic, and serological factors also predispose to WUS. Also, WUS patients are often not considered candidates for time-dependent intravenous thrombolysis therapy because of an uncertain onset time. However, using the tissue clock (positive diffusion weighted imaging-negative fluidattenuated inversion recovery mismatch) dates the WUS as 3 to 4.5 hours old and permits consideration for intravenous thrombolysis and if needed mechanical thrombectomy. Given the high prevalence of moderate/severe OSA in stroke patients and its impact on stroke outcomes, screening with overnight pulse oximetry and home sleep apnea test is needed. Treating OSA poststroke remains challenging. Polysomnographic changes in sleep architecture following acute/subacute stroke may also impact upon stroke outcome.

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“…8 However, the relationship between the non-sleepy SDB subtype and CVD remains unclear; because SDB has been identified as a heterogeneous disorder, the severity determined using AHI does not reflect the myriad of symptoms (eg daytime sleepiness owing to various reasons 9 ), clinical outcomes, and response to therapy. [10][11][12][13] In addition, several large clinical trials have shown that there is no positive effect of CPAP therapy in secondary prevention of cardiovascular events [14][15][16] in patients with non-sleepy SDB. On the one hand, this suggests that primary prevention of cardiovascular events in SDB patients may be critical, as reversing an impaired vascular flow may be more difficult than preventing it.…”

Section: Introductionmentioning

confidence: 99%

Independent Association Between Oxygen Desaturation Index and Cardiovascular Disease in Non-Sleepy Sleep-Disordered Breathing Subtype: A Chinese Community-Based Study

Wang¹,

Ou²,

Shan³

et al. 2022

NSS

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Purpose Non-sleepy sleep-disordered breathing (SDB) is increasingly recognized as an important clinical subtype. The association between non-sleepy SDB and cardiovascular disease (CVD) is not well understood. Our objectives were to investigate the relationship between non-sleepy SDB and CVD and determine which nocturnal hypoxia parameter most strongly reflects this association in a large community population. Patients and Methods Cross-sectional data from 3626 randomly-selected Chinese community-dwelling participants who underwent overnight type IV sleep monitoring were analyzed. Parameters of nocturnal hypoxemia were extracted from sleep monitoring devices, including mean nocturnal oxygen saturation, lowest oxygen saturation, oxygen desaturation index (ODI), and time with oxygen saturation <90%. An ODI ≥7.0 events/h was considered to signify SDB. An Epworth Sleepiness Scale score of 10 or less indicated no sleepiness. Results The SDB rate was 30.7% (1114/3626), of which 96.5% (1075/1114) were considered the non-sleepy SDB subtype. ODI, typical nocturnal intermittent hypoxia indicator for SDB, was independently related to CVD, regardless of whether excessive daytime sleepiness was present. After adjusting for confounders, ODI most strongly reflected the association between non-sleepy SDB and CVD (OR:1.023; 95% CI:1.003–1.043). We observed a nonlinear association between ODI and the prevalence of CVD, where the likelihood of CVD increased with ODI≥10 events/h and a markedly increasing trend was observed with ODI ≥20 events/h (reference ODI = 7.0 events/h). Metabolic parameters, Pittsburgh Sleep Quality Index, and inflammatory marker did not mediate the association between ODI and CVD in the non-sleepy SDB subtype. Conclusion In the Chinese community-dwelling population, non-sleepy SDB was highly prevalent. ODI, an easily extracted indicator from a type IV sleep monitor, most strongly reflected the association between non-sleepy SDB and CVD.

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The estimation of excessive daytime sleepiness in post-stroke patients - a polysomnographic study

Cited by 6 publications

References 36 publications

Bed Sensor Technology for Objective Sleep Monitoring Within the Clinical Rehabilitation Setting: Observational Feasibility Study (Preprint)

Bed Sensor Technology for Objective Sleep Monitoring Within the Clinical Rehabilitation Setting: Observational Feasibility Study (Preprint)

Stroke: What's Sleep Got to Do With It?

Independent Association Between Oxygen Desaturation Index and Cardiovascular Disease in Non-Sleepy Sleep-Disordered Breathing Subtype: A Chinese Community-Based Study

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