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. 2025 Jul 31;15(1):27972.
doi: 10.1038/s41598-025-12853-3.

Elevated stress hyperglycemia ratio associated with higher hospital mortality in patients with respiratory failure

Affiliations

Elevated stress hyperglycemia ratio associated with higher hospital mortality in patients with respiratory failure

Huihui Bai et al. Sci Rep. .

Abstract

The stress hyperglycemia ratio (SHR) represents an emerging biomarker linked to poor clinical outcomes. However, its association with fatal outcomes in patients experiencing respiratory failure (RF) remains poorly understood. This research was designed to evaluate the utility of SHR in predicting both in-hospital mortality and intensive care unit (ICU) mortality among RF patients. This retrospective cohort analysis utilized data from the MIMIC-IV version 3.0 database. Patients diagnosed with RF in the ICU were divided into four groups according to the SHR index quartiles (group1, group2, group3, and group4), and the outcomes were in-hospital and ICU mortality. Survival outcomes among different groups were analyzed through Kaplan-Meier curves. Based on the results of the schoenfeld residual test, choose the Cox model or the model with the time interaction term to report the association between SHR and the outcome. Furthermore, restricted cubic splines analyses were conducted to explore potential non-linear relationships of SHR with both in-hospital and ICU mortality. This study enrolled 2,250 participants, demonstrating in-hospital mortality and ICU mortality rates of 23.91% and 14.31%, respectively. Kaplan-Meier analysis revealed that the group4 exhibited the lowest survival rates (P < 0.001). Through multivariate Cox regression, when comparing the group1 to the group4, three analytical models consistently showed increased in-hospital and ICU mortality in group4. The time interaction model revealed significant increases in hospital mortality risk across SHR quartiles compared to group1. Specifically, in model1, group2 showed a 62%-69% higher risk (HR = 1.69, 95% CI: 1.17-2.44), group3 exhibited a 358%-370% higher risk (HR = 4.58, 95% CI: 1.83-11.5) ,while group4 demonstrated exponential risk escalation. All quartile groups exhibited a daily risk attenuation of approximately 57% (time interaction term HR = 0.43, all p < 0.001). The associations remained consistent after adjusting the variables in models 2 and 3. In contrast, no significant risk association was observed between SHR and ICU mortality in the time interaction model. Besides, a U-shaped relationship was observed between SHR and both in-hospital mortality and ICU mortality. The study revealed that elevated SHR levels in ICU-admitted RF patients were significantly associated with increased risks of in-hospital mortality. Clinicians should closely monitor patients with high admission SHR values, especially patients in the highest SHR quantile (Q4 group) during the early admission period, underscoring the need for prioritized clinical intervention in this high-risk population.

Keywords: ICU mortality; In-hospital mortality; Intensive care unit; MIMIC-IV; Respiratory failure; Stress hyperglycemia ratio.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethics approval and consent to participate: This study was conducted according to the guidelines of the Declaration of Helsinki. The review boards of the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center approved the use of the MIMIC-IV database. Because the data were publicly available, the study was exempt from the requirements of an ethics approval statement and informed consent.

Figures

Fig. 1
Fig. 1
Flow of included patients through the trial.
Fig. 2
Fig. 2
(a) Kaplan-Meier survival analysis curves for in-hospital mortality stratified by SHR index. (b) Kaplan-Meier survival analysis curves for ICU mortality stratified by SHR index.
Fig. 3
Fig. 3
(a) RCS of SHR index with in-hospital mortality. (b) RCS of SHR index with ICU mortality.
Fig. 4
Fig. 4
(a) Kaplan-Meier survival analysis curves for in-hospital mortality stratified by cut-off value of SHR. (b) Kaplan-Meier survival analysis curves for ICU mortality stratified by cut-off value of SHR.
Fig. 5
Fig. 5
Subgroup analyses of the association between the SHR and mortality.

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References

    1. Vincent, J. L. et al. The epidemiology of acute respiratory failure in critically ill patients(*). Chest121, 1602–1609 (2002). - PubMed
    1. Chen, L. & Rackley, C. R. Diagnosis and epidemiology of acute respiratory failure. Crit. Care Clin.40, 221–233 (2024). - PubMed
    1. Kempker, J. A. et al. The epidemiology of respiratory failure in the united States 2002–2017: a serial cross-sectional study. Crit. Care Explor.2, e128 (2020). - PMC - PubMed
    1. Ippolito, M., Galvano, A. N. & Cortegiani, A. Long-term outcomes in critically ill patients with acute respiratory failure. Curr. Opin. Crit. Care. 30, 510–522 (2024). - PubMed
    1. Bellani, G. et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. Jama-J Am. Med. Assoc.315, 788–800 (2016). - PubMed

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