Abstract
Glucose dynamics is one of the unique mechanisms in patients with critically ill heart failure (HF). The aim of this study is to evaluate the impact of dynamic blood glucose trajectories on 28-day mortality in critically ill HF patients. Latent Category Growth Model (LCGM) was used to classify patients’ blood glucose trajectories during the first 4 days of intensive care unit (ICU) admission. Kaplan-Meier survival analysis and Cox regression assessed the association between admission blood glucose levels, glucose trajectories, and 28-day mortality in critically ill HF patients. Subgroup analyses evaluated the robustness of the findings. A total of 6062 patients with critically ill HF were included in this retrospective cohort study, with 28-day mortality occurring in 1306 (21.54%) patients. The Kaplan Meier survival curve shows that the survival probabilities of different blood glucose trajectories from high to low are: class 1 > class 3 > class 2 > class 4, and there are significant inter class differences. COX regression confirms that the predictive ability of blood glucose trajectory classification for mortality in patients with critically ill HF is superior to the blood glucose coefficient of variation. Subgroup analysis further evaluated the consistency of the association between blood glucose latent trajectory classification and 28-day mortality in different patient characteristics. Dynamic blood glucose trajectories and variability indicators provide complementary information for predicting 28-day mortality in critically ill HF patients.
Data availability
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- AIC:
-
Akaike information criterion
- AMI:
-
Acute myocardial infarction
- APS III:
-
Acute physiology score III
- BIC:
-
Bayesian information criteria
- BUN:
-
Blood urea nitrogen
- CVD:
-
Cardiovascular disease
- DM:
-
Diabetes mellitus
- GV:
-
Glucose variability
- HF:
-
Heart failure
- LASSO:
-
Least Absolute Shrinkage and Selection Operator
- LCGM:
-
Latent category growth model
- ICU:
-
Intensive care unit
- INR:
-
International normalized ratio
- LOS:
-
Length of stay
- MIMIC-IV:
-
Medical Information Mart for Intensive Care IV
- OASIS:
-
Oxford acute severity of illness score
- PT:
-
Prothrombin time
- PTT:
-
Partial thromboplastin time
- RBC:
-
Red blood cell
- SABIC:
-
Sample-adjusted information criteria
- SIRS:
-
Systemic inflammatory response syndrome
- WBC:
-
White blood cell
References
Ho, K. K., Anderson, K. M., Kannel, W. B., Grossman, W. & Levy, D. Survival after the onset of congestive heart failure in Framingham Heart Study subjects. Circulation 88 (1), 107–115 (1993).
Njoroge, J. N. & Teerlink, J. R. Pathophysiology and therapeutic approaches to acute decompensated heart failure. Circ. Res. 128 (10), 1468–1486 (2021).
Chan, M. C. et al. A minimum blood glucose value less than or equal to 120 mg/dL under glycemic control is associated with increased 14-day mortality in nondiabetic intensive care unit patients with sepsis and glucose dynamics. J. Crit. Care. 34, 69–73 (2016).
Chen, Y. et al. Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning. Cardiovasc. Diabetol. 23 (1), 426 (2024).
Monnier, L. et al. Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA 295 (14), 1681–1687 (2006).
Lheureux, O., Prevedello, D. & Preiser, J. C. Update on glucose in critical care. Nutrition 59, 14–20 (2019).
Rizas, K. D. et al. Sympathetic activity-associated periodic repolarization dynamics predict mortality following myocardial infarction. J. Clin. Invest. 124 (4), 1770–1780 (2014).
Li, L., Xing, X., Li, Q., Zhang, Q. & Meng, Z. Association between blood glucose level trajectories and 30-day mortality risk in patients with acute ischemic stroke: analysis of the MIMIC database 2001–2019. Diabetol. Metab. Syndr. 16 (1), 249 (2024).
Deng, B., Liu, Q., Qiao, L. & Lv, S. Longitudinal trajectories of blood glucose and 30-day mortality in patients with diabetes mellitus combined with acute myocardial infarction: a retrospective cohort analysis of the MIMIC database. PLoS One. 19 (9), e0307905 (2024).
Ning, Y. L. et al. Association between early blood glucose dynamic trajectory and mortality for critically ill patients with heart failure: insights from real-world data. Diabetes Res. Clin. Pract. 216, 111822 (2024).
Cao, B. F. et al. Impact of baseline and trajectory of the cardiometabolic indices on incident microvascular complications in patients with type 2 diabetes. Atherosclerosis 407, 120407 (2025).
Ma, H., Yu, G., Wang, Z., Zhou, P. & Lv, W. Association between dysglycemia and mortality by Diabetes status and risk factors of dysglycemia in critically ill patients: a retrospective study. Acta Diabetol. 59 (4), 461–470 (2022).
Gerbaud, E. et al. Glycaemic variability and hyperglycaemia as prognostic markers of major cardiovascular events in diabetic patients hospitalised in Cardiology Intensive Care Unit for Acute Heart Failure. J. Clin. Med. 11, 6 (2022).
Fiorentino, T. V., Prioletta, A., Zuo, P. & Folli, F. Hyperglycemia-induced oxidative stress and its role in diabetes mellitus related cardiovascular diseases. Curr. Pharm. Des. 19, 5695–5703 (2013).
Su, H. et al. Acute hyperglycaemia enhances oxidative stress and aggravates myocardial ischaemia/reperfusion injury: role of thioredoxin-interacting protein. J. Cell. Mol. Med. 17, 181–191 (2013).
Pouryousefi, E., Javadi, M., Hashemipour, S., Nooshabadi, M. R. & Haghighian, H. K. Improved glycemic status, insulin resistance and inflammation after receiving oral oleoylethanolamide supplement in people with prediabetes: a randomized controlled trial. Diabetol. Metab. Syndr. 14 (1), 77 (2022).
Funding
This study was supported by the Science and Technology Planning Project of Quanzhou (Grant Numbers 2023C013YR) and the Second Affiliated Hospital of Fujian Medical University PHD Project Foundation (2021GCC08; 2022BD0803; 2022BD0804; 2024BD1901).
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Ping-yu Cai, Wei-ze Lin and Shu-han Chen designed the study. Bao-ya Yang and Shi-hong Lin extracted, collected and analyzed data. Jun-han Chen, Ping-yu Cai, Wei-ze Lin and Hui-li Lin reviewed the results, interpreted data, and wrote the manuscript. All authors read and approved the final manuscript.
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This study was conducted in accordance with the principles of the Helsinki Declaration. The MIMIC-IV database ensures patient privacy by de-identifying all personal information and assigning random codes for patient identification. Given the retrospective design and the use of anonymized data, the Ethics Committee of the Second Affiliated Hospital of Fujian Medical University granted a waiver for informed consent.
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Cai, Py., Lin, Wz., Chen, Sh. et al. Synergistic predictive value of dynamic glycemic trajectories and variability metrics for 28-day mortality in critically ill heart failure. Sci Rep (2026). https://doi.org/10.1038/s41598-026-45217-6
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DOI: https://doi.org/10.1038/s41598-026-45217-6
