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1.
Crit Care Explor ; 5(12): e1016, 2023 Dec.
Article in English | MEDLINE | ID: mdl-38053748

ABSTRACT

OBJECTIVES: It is well-established that sleep quality of ICU patients is poor, with sleep being highly fragmented by multiple awakenings. These sleep disruptions are associated with poor outcomes such as prolonged weaning duration from mechanical ventilation. Polysomnography can measure sleep continuity, a parameter associated positively with outcomes in patients treated with noninvasive ventilation, but polysomnography is not routinely available in all ICUs, and simple means to assess sleep quality are needed. The Richards-Campbell sleep questionnaire (RCSQ) assesses sleep quality in ICU patients but is difficult to administrate in patients who are not fully awake, and a simpler sleep numeric rating scale (sleep-NRS) has been proposed as an alternative. We here investigated the relationships between sleep continuity and patients-reported sleep quality. DESIGN: Single-center retrospective study. SETTING: Medical ICU of Poitiers University Hospital. PATIENTS: Seventy-two patients were extubated from mechanical ventilation and at high risk of reintubation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We analyzed 52 previously recorded polysomnographies in nonsedated and conscious ICU patients. Sleep was recorded the night after extubation. Sleep continuity was measured using an automated scoring algorithm from one electroencephalogram (EEG) channel of the polysomnography. Patient-reported sleep quality was assessed using RCSQ and sleep-NRS. Sleep continuity could be calculated on 45 polysomnographies (age: 68 [58-77], median [25th-75th]) RCSQ (62 [48-72]) and sleep-NRS (6.0 [5.0-7.0]) were obtained in 21 patients and 34 patients, respectively. Our results show a significant correlation between sleep continuity and sleep-NRS (p = 0.0037; ρ = 0.4844; n = 34) but not with RCSQ score (p = 0.6732; ρ = 0.1005; n = 20). CONCLUSION: Sleep continuity correlates with patient-reported sleep quality assessed using sleep-NRS and may capture the refreshing part of sleep. Sleep-NRS can be easily administered in ICU patients. Sleep continuity and sleep-NRS are simple tools that may prove useful to evaluate sleep quality in ICU patients.

2.
Neurophysiol Clin ; 53(1): 102856, 2023 Feb.
Article in English | MEDLINE | ID: mdl-36966728

ABSTRACT

OBJECTIVES: Due to the noisy environment, a very large number of patients admitted to intensive care units (ICUs) suffer from sleep severe disruption. These sleep alterations have been associated with a prolonged need for assisted ventilation or even with death. Sleep scoring in the critically ill is very challenging and requires sleep experts, limiting relevant studies to a few experienced teams. In this context, an automated scoring system would be of interest for researchers. In addition, real-time scoring could be used by nurses to protect patients' sleep. We devised a sleep scoring algorithm working in real time and compared this automated scoring against visual scoring. METHODS: We analyzed retrospectively 45 polysomnographies previously recorded in non-sedated and conscious ICU patients during their weaning phase. For each patient, one EEG channel was processed, providing automated sleep scoring. We compared total sleep time obtained with visual scoring versus automated scoring. The proportion of sleep episodes correctly identified was calculated. RESULTS: Automated total sleep time and visual sleep time were correlated; the automatic system overestimated total sleep time. The median [25th-75th] percentage of sleep episodes lasting more than 10 min detected by algorithm was 100% [73.2 - 100.0]. Median sensitivity was 97.9% [92.5 - 99.9]. CONCLUSION: An automated sleep scoring system can identify nearly all long sleep episodes. Since these episodes are restorative, this real-time automated system opens the way for EEG-guided sleep protection strategies. Nurses could cluster their non-urgent care procedures, and reduce ambient noise so as to minimize patients' sleep disruptions.


Subject(s)
Critical Illness , Respiration, Artificial , Humans , Retrospective Studies , Respiration, Artificial/methods , Sleep , Intensive Care Units , Algorithms
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