Abstract
This study aimed to characterize blood glucose trajectories during the early phase of enteral nutrition (EN) in critically ill patients and develop a predictive model for these trajectories to improve clinical management and patient outcomes. A retrospective analysis was conducted on critically ill patients who received continuous EN for ≥ 2 consecutive days, using data from the intensive care unit (ICU) of a tertiary hospital. Group-Based Trajectory Modeling (GBTM) was employed to identify distinct subgroups based on patterns of blood glucose changes. An early risk prediction model for trajectory classification was constructed using the eXtreme Gradient Boosting (XGBoost) algorithm. A total of 478 patients met the inclusion criteria. Three distinct blood glucose trajectory subgroups were identified: Mild Hyperglycemia Stable (41.84%), Moderate Hyperglycemia Peaking (36.40%), and Severe Hyperglycemia Peaking (21.76%). The XGBoost model exhibited robust discriminative ability and good calibration. Key predictors of trajectory classification included insulin use, history of diabetes, C-reactive protein, and interleukin-6. This study highlights the heterogeneity of glycemic trajectories during the early phase of EN in critically ill patients. The developed XGBoost model demonstrated satisfactory predictive performance and may serve as a valuable tool for maintaining glycemic stability and optimizing outcomes in critically ill patients receiving nutritional support in the ICU.
Data availability
Data sets are not publicly available because they contain information that could compromise the privacy of research participants, but the minimal data used and/or analyzed in this study may be provided to the corresponding authors at their reasonable request.
Code availability
The custom code and analysis scripts used in this study are publicly archived in a Zenodo repository (https://zenodo.org/records/18580968) and correspond to release v1.0.0. The repository includes documentation and instructions for reproducibility.
References
Plummer, M. P. et al. Dysglycaemia in the critically ill and the interaction of chronic and acute glycaemia with mortality. Intensive Care Med. 40, 973–980 (2014).
Vedantam, D. et al. Stress-induced hyperglycemia: consequences and management. Cureus 14, e26714 (2022).
Cattani, A., Eckert, I. C., Brito, J. E., Tartari, R. F. & Silva, F. M. Nutritional risk in critically ill patients: How it is assessed, its prevalence and prognostic value: a systematic review. Nutr. Rev. 78, 1052–1068 (2020).
Von Loeffelholz, C. & Birkenfeld, A. L. Tight versus liberal blood-glucose control in the intensive care unit: Special considerations for patients with diabetes. Lancet Diabetes Endocrinol. 12, 277–284 (2024).
Weimann, A. et al. ESPEN practical guideline: Clinical nutrition in surgery. Clin. Nutr. 40, 4745–4761 (2021).
Roberts, A. W. & Penfold, S. & the Joint British Diabetes Societies (JBDS) for Inpatient Care. Glycaemic management during the inpatient enteral feeding of people with stroke and diabetes. Diabet. Med. 35, 1027–1036 (2018).
Huajiao, X., Lingling, W. & Qi, Z. Summary of best evidence for the management of insulin intravenous infusion in ICU patients. Chin. J. Nurs. 58, 1489–1495 (2023).
Kwan, T. N. et al. Relative hypoglycemia in diabetic patients with critical illness. Crit. Care Med. 48, e233–e240 (2020).
Meng, J. et al. Intensive or liberal glucose control in intensive care units for septic patients? A meta-analysis of randomized controlled trials. Diabetes Metab. Syndr. 18, 103045 (2024).
Yao, R. et al. Is intensive glucose control bad for critically ill patients? A systematic review and meta-analysis. Int. J. Biol. Sci. 16, 1658–1675 (2020).
Hiemstra, F. W. et al. Daily variation in blood glucose levels during continuous enteral nutrition in patients on the intensive care unit: A retrospective observational study. eBioMedicine 104, 105169 (2024).
Beam, A. L. et al. Artificial intelligence in medicine. N Engl. J. Med. 388, 1220–1221 (2023).
Nagin, D. S. Group-Based Modeling of Development (Harvard University Press, 2005).
Celeux, G. & Soromenho, G. An entropy criterion for assessing the number of clusters in a mixture model. J. Classif. 13, 195–212 (1996).
Collins, G. S. et al. TRIPOD + AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. e078378 https://doi.org/10.1136/bmj-2023-078378 (2024).
Strutz, S. et al. Machine learning for predicting critical events among hospitalized children. JAMA Netw. Open. 8, e2513149 (2025).
Chen, T., Guestrin, C. & XGBoost A scalable tree boosting system. in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794ACM, San Francisco California USA, https://doi.org/10.1145/2939672.2939785 (2016).
Moorthy, V., Sim, M. A., Liu, W., Chew, S. T. H. & Ti, L. K. Risk factors and impact of postoperative hyperglycemia in nondiabetic patients after cardiac surgery: A prospective study. Medicine 98, e15911 (2019).
Krinsley, J. S. et al. Continuous glucose monitoring in the ICU: Clinical considerations and consensus. Crit. Care. 21, 197 (2017).
Honarmand, K. et al. Society of critical care medicine guidelines on glycemic control for critically ill children and adults 2024. Crit. Care Med. 52, e161–e181 (2024).
Wu, Z. et al. Expert consensus on the glycemic management of critically ill patients. J. Intensive Med. 2, 131–145 (2022).
Stenvers, D. J., Scheer, F. A. J. L., Schrauwen, P., La Fleur, S. E. & Kalsbeek, A. Circadian clocks and insulin resistance. Nat. Rev. Endocrinol. 15, 75–89 (2019).
Rau, C. S. et al. Mortality rate associated with admission hyperglycemia in traumatic femoral fracture patients is greater than non-diabetic normoglycemic patients but not diabetic normoglycemic patients. Int. J. Environ. Res. Public. Health. 15, 28 (2017).
Lee, T. F. et al. Relative hyperglycemia is an independent determinant of in-hospital mortality in patients with critical illness. Crit. Care Med. 48, e115–e122 (2020).
Gunst, J., De Bruyn, A. & Van Den Berghe, G. Glucose control in the ICU. Curr. Opin. Anaesthesiol. 32, 156–162 (2019).
Terzioglu, B., Ekinci, O. & Berkman, Z. Hyperglycemia is a predictor of prognosis in traumatic brain injury: Tertiary intensive care unit study. J. Res. Med. Sci. 20, 1166 (2015).
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Study design: W.C.X, S.J, C.L.X. Data collection: W.H.J, L.Q. Data analysis: W.C.X, S.J. Study supervision: C.L.X. Manuscript writing: W.C.X, S.J. Critical revisions for important intellectual content: C.L.X. All listed authors meet the authorship criteria and all authors agree with the content of the manuscript.
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This study was approved by the Ethics Committee of the Fourth Affiliated Hospital of Zhejiang University School of Medicine (Ethics Approval Number: K2025118) and registered with the Chinese Clinical Trial Registry (ChiCTR2500103452).
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Weng, C., Su, J., Wang, H. et al. Development and validation of glucose trajectory subphenotypes in critically ill patients on early enteral nutrition: a retrospective cohort study. Sci Rep (2026). https://doi.org/10.1038/s41598-026-47083-8
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DOI: https://doi.org/10.1038/s41598-026-47083-8