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Background <p>The stress hyperglycemia ratio (SHR) is used to measure acute glycemic response to physiological stress and has been found to relate to unfavorable clinical outcomes. Nevertheless, the relationship between SHR and mortality risk in patients with heart failure (HF) combined with acute kidney injury (AKI) remains unclear. Therefore, this study aimed to investigate the association between SHR and all-cause mortality in critically ill patients with HF and AKI, and to evaluate its potential for improving existing predictive models.</p> Methods <p>The study enrolled qualifying individuals from the Medical Information Mart for Intensive Care (MIMIC-IV) database and assigned them to four distinct groups based on SHR quartiles. The main endpoints included all-cause mortality at 30&#xa0;days and 90&#xa0;days, with in-hospital mortality as the secondary endpoint. Kaplan–Meier survival analysis, Cox proportional hazards regression, and restricted cubic spline (RCS) analysis were performed to assess the association between SHR and patient outcomes. Furthermore, receiver operating characteristic (ROC) curve analysis, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were applied to evaluate the predictive ability of SHR. Subgroup analysis was also conducted to assess the robustness of the findings.</p> Results <p>The study involved 1312 patients, of whom 791 (60.29%) were male. The patients in the highest SHR quartile had higher in-hospital (11.28% vs. 12.80% vs. 12.20% vs. 25.30%,&#xa0;<i>P</i> &lt; 0.001), 30-day (15.24% vs. 17.99% vs. 18.90% vs. 28.66%,&#xa0;<i>P</i> &lt; 0.001), and 90-day (22.87% vs. 23.48% vs. 25.91% vs. 33.23%,&#xa0;<i>P</i> = 0.009) all-cause mortality. Cox regression analysis revealed that SHR was independently associated with in-hospital mortality (per standard deviation (SD) increase: hazard ratio (HR) 1.26, 95% confidence interval (CI) 1.12–1.41), 30-day mortality (per SD increase: HR 1.23, 95% CI 1.11–1.36), and 90-day mortality (per SD increase: HR 1.17, 95% CI 1.06–1.28). RCS analysis revealed a linear association between SHR and mortality risk&#xa0;(<i>P</i> for nonlinearity &gt; 0.05). Kaplan–Meier survival analysis demonstrated that patients in the highest SHR quartile had significantly poorer survival outcomes. ROC analysis revealed that SHR had superior predictive value for mortality risk compared with admission glucose and HbA1c.&#xa0;Furthermore, incorporating SHR into established prediction models significantly improved reclassification performance and discriminative capacity. Subgroup analysis showed potential interactions between SHR and diabetes and renal disease (all&#xa0;<i>P</i>&#xa0;for interaction &lt; 0.05).</p> Conclusions <p>Increased SHR values are independently associated with all-cause mortality in individuals with HF and AKI. Routine SHR assessment not only enables the rapid identification of individuals at high risk but also facilitates the implementation of timely and targeted interventions.</p>

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Association between stress hyperglycemia ratio and mortality in patients with heart failure and acute kidney injury

  • 期刊论文

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  • 发表日期：

    2026年2月27日

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  • Susu He,
  • Yongkang Wang,
  • Hui Li,
  • Wei Zheng,
  • Shifeng Yang

摘要

Background

The stress hyperglycemia ratio (SHR) is used to measure acute glycemic response to physiological stress and has been found to relate to unfavorable clinical outcomes. Nevertheless, the relationship between SHR and mortality risk in patients with heart failure (HF) combined with acute kidney injury (AKI) remains unclear. Therefore, this study aimed to investigate the association between SHR and all-cause mortality in critically ill patients with HF and AKI, and to evaluate its potential for improving existing predictive models.

Methods

The study enrolled qualifying individuals from the Medical Information Mart for Intensive Care (MIMIC-IV) database and assigned them to four distinct groups based on SHR quartiles. The main endpoints included all-cause mortality at 30 days and 90 days, with in-hospital mortality as the secondary endpoint. Kaplan–Meier survival analysis, Cox proportional hazards regression, and restricted cubic spline (RCS) analysis were performed to assess the association between SHR and patient outcomes. Furthermore, receiver operating characteristic (ROC) curve analysis, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were applied to evaluate the predictive ability of SHR. Subgroup analysis was also conducted to assess the robustness of the findings.

Results

The study involved 1312 patients, of whom 791 (60.29%) were male. The patients in the highest SHR quartile had higher in-hospital (11.28% vs. 12.80% vs. 12.20% vs. 25.30%, P < 0.001), 30-day (15.24% vs. 17.99% vs. 18.90% vs. 28.66%, P < 0.001), and 90-day (22.87% vs. 23.48% vs. 25.91% vs. 33.23%, P = 0.009) all-cause mortality. Cox regression analysis revealed that SHR was independently associated with in-hospital mortality (per standard deviation (SD) increase: hazard ratio (HR) 1.26, 95% confidence interval (CI) 1.12–1.41), 30-day mortality (per SD increase: HR 1.23, 95% CI 1.11–1.36), and 90-day mortality (per SD increase: HR 1.17, 95% CI 1.06–1.28). RCS analysis revealed a linear association between SHR and mortality risk (P for nonlinearity > 0.05). Kaplan–Meier survival analysis demonstrated that patients in the highest SHR quartile had significantly poorer survival outcomes. ROC analysis revealed that SHR had superior predictive value for mortality risk compared with admission glucose and HbA1c. Furthermore, incorporating SHR into established prediction models significantly improved reclassification performance and discriminative capacity. Subgroup analysis showed potential interactions between SHR and diabetes and renal disease (all P for interaction < 0.05).

Conclusions

Increased SHR values are independently associated with all-cause mortality in individuals with HF and AKI. Routine SHR assessment not only enables the rapid identification of individuals at high risk but also facilitates the implementation of timely and targeted interventions.