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Observational Study
. 2024 Jun:104:105169.
doi: 10.1016/j.ebiom.2024.105169. Epub 2024 May 30.

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

Affiliations
Observational Study

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

Floor W Hiemstra et al. EBioMedicine. 2024 Jun.

Abstract

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).

Keywords: Circadian rhythm; Critical illness; Daily variation; Electronic health records; Enteral nutrition; Glucose; Intensive care.

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Conflict of interest statement

Declaration of interests All authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Inclusion flowchart of ICU stays and glucose measurements.
Fig. 2
Fig. 2
Distributions of glucose measurements and enteral nutrition rates over the 24-h period and across the ICU stay. (a) Example of the timing of enteral nutrition, glucose measurements and administrations of dextrose, glucocorticoids and insulin in one representative patient. Black symbols represent glucose measurements taken while the patient received enteral nutritional support (included in the dataset), grey symbols represent glucose measurements taken while the patient did not receive enteral nutritional support (not included in the dataset). (b) Representative examples of enteral nutritional support across the ICU stay, taken from two patients included in the dataset. (c) Distribution of included glucose measurements, and (d) dosing rate of enteral nutrition (at the time of glucose measurements) by time of day.
Fig. 3
Fig. 3
24-h variation in glucose levels during the entire ICU stay during enteral nutrition. (a) Model-predicted glucose levels by time of day (in hourly bins). Data presented as estimated marginal means ± 95% CI derived from the final linear mixed effects model, adjusted for patient-level and sample-level covariates. (b) SHAP values for time of day (as a continuous variable) from the XGBoost model. Each black dot represents an individual glucose measurement. The SHAP value represents shows how much the time of day feature affected the glucose level. A positive SHAP value reflects a positive impact on the predicted glucose level, while negative values reflect negative impacts. Its absolute value represents the magnitude of this effect. A sample of 5000 randomly selected features of the glucose measurements was used in this SHAP analysis.
Fig. 4
Fig. 4
Subgroup analysis of 24-h variation in glucose levels. (a) Ventilation mode. (b) Survivor status. (c) Depth of sedation. (d) Days in ICU. (e) Sample type. (f) Time to next glucose sample. For each subgroup, a linear mixed-effects model was fitted. Data presented as estimated marginal means ± 95% CI, adjusted for covariates. The dashed line represents a normal range of glucose levels in the ICU. Number of patients and measurements per group are shown in Supplementary Table S5.
Fig. 5
Fig. 5
24-h variation in glucose levels in patient who required insulin during their stay compared to those who did not receive any insulin. Data presented as estimated marginal means ± 95% CI, adjusted for covariates. The dashed line represents a normal range of glucose levels in the ICU.

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