Intensive care unit depth of sleep: proof of concept of a simple electroencephalography index in the non-sedated

Reinke, Laurens; Hoeven, Johannes H. van der; Putten, Michel Johannes Antonius Maria van; Dieperink, Willem; Tulleken, Jaap

doi:10.1186/cc13823

Cited by 19 publications

(10 citation statements)

References 27 publications

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“…No significant differences were detected for AUCs for any other spectral power frequency analyzed between the two groups (see Table 4 ). Delta and gamma power oscillated reciprocally within each SWS cycle with delta power increasing and gamma power decreasing from the start to end of each SWS cycle in both groups similar to previously reported findings [ 36 , 37 ]. In addition, AUCs for gamma power also showed non-significant differences with similar trends along the AP axis in RTT group that were not significantly different from the control group during SWS sleep ( Fig 2C , Table 4 ).…”

Section: Resultssupporting

confidence: 91%

Heightened Delta Power during Slow-Wave-Sleep in Patients with Rett Syndrome Associated with Poor Sleep Efficiency

et al. 2015

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Sleep problems are commonly reported in Rett syndrome (RTT); however the electroencephalographic (EEG) biomarkers underlying sleep dysfunction are poorly understood. The aim of this study was to analyze the temporal evolution of quantitative EEG (qEEG) biomarkers in overnight EEGs recorded from girls (2–9 yrs. old) diagnosed with RTT using a non-traditional automated protocol. In this study, EEG spectral analysis identified high delta power cycles representing slow wave sleep (SWS) in 8–9h overnight sleep EEGs from the frontal, central and occipital leads (AP axis), comparing age-matched girls with and without RTT. Automated algorithms quantitated the area under the curve (AUC) within identified SWS cycles for each spectral frequency wave form. Both age-matched RTT and control EEGs showed similar increasing trends for recorded delta wave power in the EEG leads along the antero-posterior (AP). RTT EEGs had significantly fewer numbers of SWS sleep cycles; therefore, the overall time spent in SWS was also significantly lower in RTT. In contrast, the AUC for delta power within each SWS cycle was significantly heightened in RTT and remained heightened over consecutive cycles unlike control EEGs that showed an overnight decrement of delta power in consecutive cycles. Gamma wave power associated with these SWS cycles was similar to controls. However, the negative correlation of gamma power with age (r = -.59; p<0.01) detected in controls (2–5 yrs. vs. 6–9 yrs.) was lost in RTT. Poor % SWS (i.e., time spent in SWS overnight) in RTT was also driven by the younger age-group. Incidence of seizures in RTT was associated with significantly lower number of SWS cycles. Therefore, qEEG biomarkers of SWS in RTT evolved temporally and correlated significantly with clinical severity.

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Section: Resultssupporting

confidence: 91%

Heightened Delta Power during Slow-Wave-Sleep in Patients with Rett Syndrome Associated with Poor Sleep Efficiency

et al. 2015

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“…Given the high interscorer reproducibility of this method, these measurements could facilitate the sharing of sleep quantification across ICUs. Sleep continuity might be a useful measure of sleep quality in all patients in whom the distribution of sleep episode duration could be reliably determined; sleep continuity could be calculated using the automated EEG analysis recently reported in ICU patients [ 40 ]. However, before being considered as an effective alternative metric in the study of sleep in ICUs, sleep continuity measurements need to be replicated and tested in critically ill patients with atypical sleep, in patients with a high severity of illness and in patients receiving sedatives.…”

Section: Discussionmentioning

confidence: 99%

Sleep continuity: a new metric to quantify disrupted hypnograms in non-sedated intensive care unit patients

et al. 2014

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IntroductionSleep in intensive care unit (ICU) patients is severely altered. In a large proportion of critically ill patients, conventional sleep electroencephalogram (EEG) patterns are replaced by atypical sleep. On the other hand, some non-sedated patients can display usual sleep EEG patterns. In the latter, sleep is highly fragmented and disrupted and conventional rules may not be optimal. We sought to determine whether sleep continuity could be a useful metric to quantify the amount of sleep with recuperative function in critically ill patients with usual sleep EEG features.MethodsWe retrospectively reanalyzed polysomnographies recorded in non-sedated critically ill patients requiring non-invasive ventilation (NIV) for acute hypercapnic respiratory failure. Using conventional rules, we built two-state hypnograms (sleep and wake) and identified all sleep episodes. The percentage of time spent in sleep bouts (<10 minutes), short naps (>10 and <30 minutes) and long naps (>30 minutes) was used to describe sleep continuity. In a first study, we compared these measures regarding good (NIV success) or poor outcome (NIV failure). In a second study performed on a different patient group, we compared these measurements during NIV and during spontaneous breathing.ResultsWhile fragmentation indices were similar in the two groups, the percentage of total sleep time spent in short naps was higher and the percentage of sleep time spent in sleep bouts was lower in patients with successful NIV. The percentage of total sleep time spent in long naps was higher and the percentage of sleep time spent in sleep bouts was lower during NIV than during spontaneous breathing; the level of reproducibility of sleep continuity measures between scorers was high.ConclusionsSleep continuity measurements could constitute a clinically relevant and reproducible assessment of sleep disruption in non-sedated ICU patients with usual sleep EEG.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-014-0628-4) contains supplementary material, which is available to authorized users.

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“…5 As part of a pilot study, we too found severely fragmented sleep and EEG activity that suggest heightened arousal and signs of sleep deprivation. 6 Patients admitted to an ICU are exposed to several intrinsic and extrinsic sleep-disrupting factors, which were described previously in more detail by Le Guen and colleagues. 7 A multitude of these factors, most of them interdependent, likely causes the disrupted sleep observed in the ICU.…”

Section: Key Pointsmentioning

confidence: 99%

Systematic review of the effects of intensive-care-unit noise on sleep of healthy subjects and the critically ill

Horsten

Reinke

Absalom

et al. 2018

British Journal of Anaesthesia

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Intensive-care-unit (ICU) patients exhibit disturbed sleeping patterns, often attributed to environmental noise, although the relative contribution of noise compared to other potentially disrupting factors is often debated. We therefore systematically reviewed studies of the effects of ICU noise on the quality of sleep to determine to what extent noise explains the observed sleep disruption, using the Cochrane Collaboration method for non-randomized studies. Searches in Scopus, PubMed, Embase, CINAHL, Web of Science, and the Cochrane Library were conducted until May 2017. Twenty papers from 18 studies assessing sleep of adult patients and healthy volunteers in the ICU environment, whilst recording sound levels, were included and independently reviewed by two reviewers. We found that the numbers of arousals between the baseline and the ICU noise condition in healthy subjects differed significantly (mean difference 9.59; 95% confidence interval 2.48-16.70). However, there was considerable heterogeneity between studies (I 94%, P < 0.00001), and all studies suffered from a considerable risk of bias. The meta-analysis of results was hampered by widely varying definitions of sound parameters between studies and a general lack of detailed description of methods used. It is, therefore, currently impossible to quantify the extent to which noise contributes to sleep disruption among ICU patients, and thus, the potential benefit from noise reduction remains unclear. Regardless, the majority of the observed sleep disturbances remain unexplained. Future studies should, therefore, also focus on more intrinsic sleep-disrupting factors in the ICU environment.

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Intensive care unit depth of sleep: proof of concept of a simple electroencephalography index in the non-sedated

Cited by 19 publications

References 27 publications

Heightened Delta Power during Slow-Wave-Sleep in Patients with Rett Syndrome Associated with Poor Sleep Efficiency

Heightened Delta Power during Slow-Wave-Sleep in Patients with Rett Syndrome Associated with Poor Sleep Efficiency

Sleep continuity: a new metric to quantify disrupted hypnograms in non-sedated intensive care unit patients

Systematic review of the effects of intensive-care-unit noise on sleep of healthy subjects and the critically ill

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