Daily variation in blood glucose levels during continuous enteral nutrition in patients on the intensive care unit: a retrospective observational study.

BACKGROUND: The circadian timing system coordinates daily cycles in physiological functions, including glucose metabolism and insulin sensitivity. Here, the aim was to characterise the 24-h variation in glucose levels in critically ill patients during continuous enteral nutrition after controlling for potential sources of bias. METHODS: Time-stamped clinical data from adult patients who stayed in the Intensive Care Unit (ICU) for at least 4 days and received enteral nutrition were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Linear mixed-effects and XGBoost modelling were used to determine the effect of time of day on blood glucose values. FINDINGS: In total, 207,647 glucose measurements collected during enteral nutrition were available from 6,929 ICU patients (3,948 males and 2,981 females). Using linear mixed-effects modelling, time of day had a significant effect on blood glucose levels (p < 0.001), with a peak of 9.6 [9.5-9.6; estimated marginal means, 95% CI] mmol/L at 10:00 in the morning and a trough of 8.6 [8.5-8.6] mmol/L at 02:00 at night. A similar impact of time of day on glucose levels was found with the XGBoost regression model. INTERPRETATION: These results revealed marked 24-h variation in glucose levels in ICU patients even during continuous enteral nutrition. This 24-h pattern persists after adjustment for potential sources of bias, suggesting it may be the result of endogenous biological rhythmicity. FUNDING: This work was supported by a VENI grant from the Netherlands Organisation for Health Research and Development (ZonMw), an institutional project grant, and by the Dutch Research Council (NWO).

An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children.

STUDY OBJECTIVES: Although sleep is frequently disrupted in the pediatric intensive care unit, it is currently not possible to perform real-time sleep monitoring at the bedside. In this study, spectral band powers of electroencephalography data are used to derive a simple index for sleep classification. METHODS: Retrospective study at Erasmus MC Sophia Children's Hospital, using hospital-based polysomnography recordings obtained in non-critically ill children between 2017 and 2021. Six age categories were defined: 6-12 months, 1-3 years, 3-5 years, 5-9 years, 9-13 years, and 13-18 years. Candidate index measures were derived by calculating spectral band powers in different frequent frequency bands of smoothed electroencephalography. With the best performing index, sleep classification models were developed for two, three, and four states via decision tree and five-fold nested cross-validation. Model performance was assessed across age categories and electroencephalography channels. RESULTS: In total 90 patients with polysomnography were included, with a mean (standard deviation) recording length of 10.3 (1.1) hours. The best performance was obtained with the gamma to delta spectral power ratio of the F4-A1 and F3-A1 channels with smoothing. Balanced accuracy was 0.88, 0.74, and 0.57 for two-, three-, and four-state classification. Across age categories, balanced accuracy ranged between 0.83 and 0.92 and 0.72 and 0.77 for two- and three-state classification, respectively. CONCLUSIONS: We propose an interpretable and generalizable sleep index derived from single-channel electroencephalography for automated sleep monitoring at the bedside in non-critically ill children ages 6 months to 18 years, with good performance for two- and three-state classification. CITATION: van Twist E, Hiemstra FW, Cramer ABG, et al. An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children. J Clin Sleep Med. 2024;20(3):389-397.

Sleep in the intensive and intermediate care units: Exploring related factors of delirium, benzodiazepine use and mortality.

AIM OF THE STUDY: The primary purpose was to examine sleep difficulties and delirium in the Intensive and Intermediate Care Unit. Secondarily, factors impacting night-time sleep duration and quality, mortality, and the impact of benzodiazepine use on sleep outcomes were investigated. MATERIALS AND METHODS: This retrospective study encompassed data from 323 intensive and intermediate care unit admissions collected in the Netherlands, spanning from November 2018 to May 2020. Sleep quality was measured using the Richards-Campbell Sleep Questionnaire. Night-time sleep duration was nurse-reported. We investigated associations of these sleep outcomes with age, sex, length-of-stay, natural daylight, disease severity, mechanical ventilation, benzodiazepine use, and delirium using Generalized Estimating Equations models. Associations with one-year post-discharge mortality were analyzed using Cox regression. RESULTS: Night-time sleep duration was short (median 4.5 hours) and sleep quality poor (mean score 4.9/10). Benzodiazepine use was common (24 % of included nights) and was negatively associated with night-time sleep duration and quality (B = -0.558 and -0.533, p <.001). Delirium and overnight transfers were negatively associated with sleep quality (B = -0.716 and -1.831, p <.05). The day-to-night sleep ratio was higher in the three days before delirium onset than in non-delirious individuals (p <.05). Age, disease severity and female sex were associated with increased one-year mortality. Sleep quality was negatively, but not-significantly, associated with mortality (p =.070). CONCLUSIONS: Night-time sleep in the critical care environment has a short duration and poor quality. Benzodiazepine use was not associated with improved sleep. Sleep patterns change ahead of delirium onset. IMPLICATIONS FOR CLINICAL PRACTICE: Consistent sleep monitoring should be part of routine nursing practice, using a validated instrument like the Richards-Campbell Sleep Questionnaire. Given the lack of proven efficacy of benzodiazepines in promoting sleep in critical care settings, it is vital to develop more effective sleep treatments that include non-benzodiazepine medication and sleep hygiene strategies.