Abstract
Background
The stress hyperglycemia ratio (SHR) was created to reduce the impact of long-term chronic glycemic factors on stress hyperglycemia, which is considered a reliable biomarker for adverse outcomes in intensive care patients. However, the relationship between SHR and the outcomes for critically ill patients with heart failure (HF) remains unclear.
Methods
This retrospective cohort study analyzed 1,438 critically ill patients with HF from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v3.1) database (2008–2022). Patients were followed from ICU admission to death or 365days. SHR was calculated as admission glucose (mmol/L) / [1.59 × HbA1c (%) − 2.59]. Multivariable Cox regression with four adjustment levels evaluated the association between SHR and mortality. Restricted cubic splines assessed nonlinear relationships, and Kaplan-Meier methodology with log-rank tests analyzed survival patterns.
Results
In the cohort, each 1-unit increase in SHR was associated with a 40% higher 30-day mortality risk (HR 1.40, 95% CI 1.20–1.63, P < 0.001) and a 31% higher 90-day mortality risk (HR 1.31, 95% CI 1.14–1.51, P < 0.001). Patient survival rates decreased significantly with higher SHR tertiles (P < 0.001), and restricted cubic spline analysis confirmed a linear relationship. Subgroup analyses confirmed the robustness of these links.
Conclusions
Elevated SHR is significantly associated with increased mortality in critically ill patients with HF, supporting its role as a valuable biomarker for risk stratification in this high-risk population.
Graphical abstract
The Association Between SHR and Mortality in Critically Ill Patients with HF.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-025-05257-8.
Keywords: Stress hyperglycemia ratio, Heart failure, Mortality, Biomarker, MIMIC-IV database
Introduction
Heart failure (HF), a clinical syndrome characterized by structural and/or functional cardiac abnormalities, represents a global health concern affecting over 64 million individuals [1, 2]. Its prevalence continues to climb, fuelled by aging populations and improved survival rates due to breakthroughs in early detection and disease management [3, 4]. In the United States, 10%–51% of hospitalized HF patients require intensive care unit (ICU) admission for life-threatening complications, with in-hospital mortality (10.6%) significantly exceeding that of non-ICU admissions (4.0%) [5–7]. Annual healthcare costs are projected to reach $70 billion by 2030, reflecting their escalating socioeconomic burden [8, 9]. These challenges highlight the urgent need for reliable biomarkers to enhance risk stratification and guide individualized therapies in this high-risk population.
The stress hyperglycemia ratio (SHR), calculated by comparing acute blood glucose levels to hemoglobin A1c (HbA1c), integrates acute metabolic disruptions with chronic glycemic status, confirming its relevance as a biomarker for stress-induced hyperglycemia [10–12]. Traditional metrics such as isolated glucose measurements or HbA1c alone cannot distinguish between the temporary impacts of critical illness from underlying glucose dysregulation, whereas SHR provides a dynamic indicator that reflects both components [13]. There is mounting evidence that SHR plays a prognostic function in critically ill populations [14]. Elevated SHR levels highly correlate with higher mortality in critically ill patients with sepsis, acute myocardial infarction, atrial fibrillation, and cerebrovascular disease [15–19]. Studies in acute HF reveal that a high SHR independently predicts adverse outcomes both short- and long-term [20, 21].
This study uses data from the MIMIC-IV database to investigate the association between SHR and mortality in critically ill patients with heart failure admitted to the ICU.
Materials and methods
Data source
This study retrospectively analyzed data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, v3.1) database, which contained anonymised clinical records from over 50,000 ICU admissions at Beth Israel Deaconess Medical Center between 2008 and 2022. Two authors (G.J. and R.D.) completed the required training and obtained certified database access (Certification IDs: 66796235, 64760223). The study protocol was approved by the institutional review boards at Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center. Informed consent was waived for this deidentified data analysis. The study adhered to STROBE guidelines and complied with the Declaration of Helsinki.
Study subjects
This study focused on 19,924 individuals admitted to the ICU with a HF diagnosis (defined by ICD-9/ICD-10 codes detailed in Supplementary Table 1). Exclusion criteria included: (1) Age < 18 years old; (2) Multiple ICU admissions (only the first admission was analyzed); (3) Admission involving an ICU stay shorter than 24 h; and (4) Missing glycated hemoglobin or initial glucose measurement. After applying these exclusion criteria, the final study cohort consisted of 1,438 patients. The patient selection flowchart is provided in Fig. 1.
Fig. 1.
Flowchart of the study cohort. ICU, Intensive Care Unit; MIMIC-IV, Medical Information Mart for Intensive Care IV
Demographical and laboratory variables
Baseline clinical data were extracted with PostgreSQL (version 13.3) and Navicat Premium (version 16.0). The extracted variables included demographic characteristics (age, sex, race, height, weight); vital signs (mean arterial pressure, oxygen saturation) and laboratory parameters (hemoglobin, platelets, white blood cell count, anion gap, creatinine, and activated partial thromboplastin time), recorded as the worst values observed within the initial 24 h following ICU admission; comorbidities (myocardial infarction, cerebrovascular disease, chronic kidney disease, severe liver disease, chronic pulmonary disease, diabetes, and sepsis), critical care interventions (presence of continuous renal replacement therapy and mechanical ventilation in the first 24 h); initial severity scores (OASIS, SAPS II, SOFA), and survival time. Body Mass Index (BMI) was determined by dividing weight in kilograms by the square of height in meters. Missing data (< 30%) were addressed using single imputation with Bayesian Ridge regression.
SHR assessment and outcomes
The SHR was calculated using the formula: SHR = [admission glucose (mmol/L)]/[1.59 × HbA1c (%) − 2.59]. The initial glucose measurement obtained within 24 h of ICU admission was used. The first available HbA1c measurement during hospitalization was utilized for SHR calculation. The primary outcomes were 30- and 90-day mortality, with 365-day mortality and ICU mortality serving as secondary endpoints for sensitivity analyses. Death events were recorded within these predefined timeframes following ICU admission.
Statistical analysis
Continuous variables with normal distribution were expressed as mean ± standard deviation and compared using ANOVA. Non-normally distributed variables were reported as median (interquartile range) and analyzed via the Kruskal-Wallis test. Categorical variables were summarized as frequencies (percentages), with between-group comparisons performed using χ² tests or Fisher’s exact tests, as appropriate. Kaplan-Meier curves were generated to visualize mortality trends stratified by SHR tertiles, and intergroup survival differences were evaluated with log-rank tests.
Multivariate Cox proportional hazards models were used to examine the association between SHR and mortality, with results expressed as hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). Four models were constructed: Model 1 was unadjusted; Model 2 was adjusted for age, sex, and race; Model 3 was further adjusted for body mass index, mean arterial pressure, peripheral oxygen saturation, hemoglobin, platelets, white blood cell count, anion gap, creatinine, activated partial thromboplastin time, myocardial infarction, chronic kidney disease, severe liver disease, chronic pulmonary disease, and diabetes; Model 4 additionally included adjustments for the Oxford Acute Severity of Illness Score (OASIS), continuous renal replacement therapy (CRRT), and mechanical ventilation. Covariate selection incorporated variables that were significant in univariate analysis (P < 0.1, Supplementary Table 2), published evidence, and clinical relevance considerations. Multicollinearity was measured using generalized variance inflation factors (GVIF; Supplementary Table 3), and all values were below 2, indicating no substantial collinearity.
The relationship between SHR and 30- and 90-day mortality was analyzed using restricted cubic spline (RCS) regression with three knots at the 10th, 50th, and 90th percentiles, and nonlinearity was evaluated with a likelihood ratio test. Forest plots were generated to visualize subgroup analyses, assessing the robustness of the findings and evaluating potential interaction effects across predefined clinical subgroups.
Analysis was performed using R 4.2.2 (http://www.Rproject.org; The R Foundation, Vienna, Austria) and the Free Statistics software (version 2.2; Beijing FreeClinical Medical Technology Co., Ltd, Beijing, China). Statistical significance was indicated by P < 0.05.
Results
Baseline characteristics of study subjects
In our study of 1,438 critically ill patients with HF, the average age was 69.8 years, and the median duration of hospitalization was 11.8 days (Table 1). Patients were divided into three groups based on SHR levels: T1 (0.23–1.00.23.00), T2 (1.00–1.37.00.37), and T3 (1.37–6.37). As SHR levels climbed, white blood cell counts, anion gap, activated partial thromboplastin time, and lactate levels all increased significantly, whereas bicarbonate and calcium levels decreased simultaneously. The highest SHR tertile was associated with higher disease severity scores, including SAPSII, OASIS, and SOFA. Comorbidities such as diabetes, myocardial infarction, and sepsis were more common in the highest SHR group. Furthermore, the need for mechanical ventilation increased with higher SHR levels. The highest SHR tertile also had the highest mortality rates in the ICU and at different follow-up times.
Table 1.
The clinical characteristics of critically ill patients with HF according to SHR levels
| Variables | Total (n = 1438) | SHR | p-Value | ||
|---|---|---|---|---|---|
| T1 (0.23–1.00) | T2 (1.00–1.37) | T3 (1.37–6.37) | |||
| Participants (n) | 1438 | 479 | 478 | 481 | |
| Age (years) | 69.8 ± 13.3 | 70.0 ± 14.0 | 69.9 ± 13.3 | 69.5 ± 12.5 | 0.760 |
| Sex (n, %) | 0.475 | ||||
| Female | 525 (36.5) | 176 (36.7) | 183 (38.3) | 166 (34.5) | |
| Male | 913 (63.5) | 303 (63.3) | 295 (61.7) | 315 (65.5) | |
| Race (n, %) | 0.140 | ||||
| White | 839 (58.3) | 281 (58.7) | 282 (59) | 276 (57.4) | |
| Black or African American | 124 (8.6) | 50 (10.4) | 43 (9.0) | 31 (6.4) | |
| Others | 475 (33.0) | 148 (30.9) | 153 (32.0) | 174 (36.2) | |
| BMI (kg/m2) | 30.4 ± 8.1 | 31.2 ± 9.0 | 29.5 ± 7.7 | 30.4 ± 7.5 | 0.008 |
| MAP (mmHg) | 80.7 ± 11.4 | 81.6 ± 12.5 | 80.6 ± 11.1 | 80.0 ± 10.5 | 0.096 |
| SpO2 (%) | 96.6 ± 2.2 | 96.6 ± 2.0 | 96.5 ± 2.0 | 96.6 ± 2.5 | 0.603 |
| Hemoglobin (g/dL) | 10.8 ± 2.4 | 10.9 ± 2.5 | 10.8 ± 2.4 | 10.7 ± 2.5 | 0.423 |
| Platelets (109/L) | 199.1 ± 86.8 | 192.4 ± 91.2 | 205.7 ± 84.4 | 199.2 ± 84.4 | 0.059 |
| White blood cell (109/L) | 15.0 ± 7.3 | 13.4 ± 6.2 | 14.4 ± 6.6 | 17.3 ± 8.2 | < 0.001 |
| Anion Gap (mmol/L) | 17.5 ± 5.1 | 16.8 ± 5.0 | 17.1 ± 4.6 | 18.6 ± 5.4 | < 0.001 |
| Bicarbonate (mmol/L) | 21.2 ± 5.2 | 21.9 ± 5.1 | 21.3 ± 5.0 | 20.3 ± 5.4 | < 0.001 |
| Calcium (mg/dL) | 8.2 ± 0.8 | 8.3 ± 0.8 | 8.3 ± 0.7 | 8.1 ± 0.8 | < 0.001 |
| Potassium (mmol/L) | 4.8 ± 1.0 | 4.8 ± 1.1 | 4.7 ± 0.9 | 4.8 ± 0.8 | 0.041 |
| Sodium (mmol/L) | 136.2 ± 5.0 | 136.6 ± 5.7 | 136.8 ± 4.5 | 135.2 ± 4.7 | < 0.001 |
| Creatinine (mg/dL) | 1.4 (1.0, 2.2) | 1.4 (1.0, 2.1) | 1.3 (1.0, 1.9) | 1.6 (1.1, 2.4) | < 0.001 |
| APTT (s) | 43.2 (30.1, 81.7) | 36.7 (29.3, 63.9) | 42.1 (30.2, 71.2) | 57.6 (31.5, 115.0) | < 0.001 |
| Lactate (mmol/L) | 1.9 (1.4, 3.3) | 1.8 (1.2, 2.7) | 1.9 (1.4, 2.8) | 2.5 (1.5, 4.4) | < 0.001 |
| Diabetes (n, %) | 654 (45.5) | 207 (43.2) | 200 (41.8) | 247 (51.4) | 0.006 |
| Myocardial infarction (n, %) | 709 (49.3) | 187 (39.0) | 238 (49.8) | 284 (59.0) | < 0.001 |
| Chronic pulmonary disease (n, %) | 471 (32.8) | 166 (34.7) | 132 (27.6) | 173 (36.0) | 0.012 |
| Cerebrovascular disease (n, %) | 435 (30.3) | 179 (37.4) | 141 (29.5) | 115 (23.9) | < 0.001 |
| Chronic kidney disease (n, %) | 504 (35.0) | 168 (35.1) | 172 (36.0) | 164 (34.1) | 0.829 |
| Severe liver disease (n, %) | 28 (1.9) | 12 (2.5) | 3 (0.6) | 13 (2.7) | 0.037 |
| Sepsis (n, %) | 1013 (70.4) | 314 (65.6) | 327 (68.4) | 372 (77.3) | < 0.001 |
| SAPS Ⅱ (score) | 40.3 ± 13.6 | 39.0 ± 13.9 | 39.1 ± 11.7 | 42.8 ± 14.8 | < 0.001 |
| OASIS (score) | 36.8 ± 9.8 | 36.3 ± 10.1 | 36.0 ± 9.2 | 38.2 ± 10.0 | < 0.001 |
| SOFA (score) | 5.5 ± 3.4 | 5.2 ± 3.5 | 5.2 ± 3.2 | 6.2 ± 3.4 | < 0.001 |
| CRRT (n, %) | 115 (8.0) | 33 (6.9) | 36 (7.5) | 46 (9.6) | 0.280 |
| Mechanical ventilation (n, %) | 467 (32.5) | 140 (29.2) | 158 (33.1) | 155 (32.2) | 0.930 |
| 30-day mortality (n, %) | 283 (19.7) | 59 (12.3) | 145 (30.3) | 182 (37.8) | 0.008 |
| 90-day mortality (n, %) | 380 (26.4) | 97 (20.3) | 121 (25.3) | 162 (33.7) | < 0.001 |
| 365-day mortality (n, %) | 482 (33.5) | 142 (29.6) | 147 (30.8) | 193 (40.1) | < 0.001 |
| ICU mortality (n, %) | 173 (12.0) | 26 (5.4) | 45 (9.4) | 102 (21.2) | < 0.001 |
| Hospital stays (day) | 11.8 (6.8, 19.6) | 12.7 (7.9, 20.8) | 10.8 (6.1, 19.4) | 11.3 (6.7, 19.2) | 0.015 |
HF Heart failure, SHR Stress hyperglycemia ratio, BMI Body mass index, MAP Mean arterial pressure, SpO2 peripheral oxygen saturation, APTT Activated partial thromboplastin time, SAPS Ⅱ Simplified acute physiology score II, OASIS Oxford Acute Severity of Illness Score, SOFA Sequential Organ Failure Assessment, CRRT Continuous renal replacement therapy
Survival analysis
The Kaplan-Meier survival analysis showed a notable link between higher SHR levels and a lower chance of survival. As illustrated in Fig. 2, patients with increased SHR levels had markedly inferior 30-day and 90-day survival outcomes compared to those with decreased SHR levels (p < 0.0001).
Fig. 2.
Kaplan-Meier survival analysis curves for 30-day (A) and 90-day (B) mortality in the overall study population
SHR and mortality
Multivariable Cox regression analysis demonstrated a significant association between SHR levels and mortality in critically ill patients with HF (Table 2). As a continuous variable, each 1-unit increase in SHR was associated with a 52% higher risk of 30-day mortality (HR 1.52, 95% CI 1.33–1.72) in the unadjusted model and a 40% higher risk (HR 1.40, 95% CI 1.20–1.63) after full adjustment (Model 4); corresponding increases for 90-day mortality were 36% (HR 1.36, 95% CI 1.20–1.54)and 31% (HR 1.31, 95% CI 1.14–1.51), respectively (all P < 0.001). When analyzed by tertiles, the highest SHR tertile (T3) exhibited a 156% increased risk of 30-day mortality (HR 2.56, 95% CI 1.88–3.47) in the unadjusted model and a 131% increased risk (HR 2.31, 95% CI 1.66–3.22) in the fully adjusted model, compared to the lowest tertile (T1). Similarly, for 90-day mortality, the increased risk was 89% (HR 1.89, 95% CI 1.47–2.43) in Model 1 and 93% (HR 1.93, 95% CI 1.47–2.54) in Model 4 (all P < 0.001). RCS analysis revealed a linear dose-response relationship of SHR with both 30- and 90-day mortality (P for non-linearity: 0.308 and 0.203, respectively), as shown in Fig. 3.
Table 2.
Multivariate Cox regression analyses for 30- and 90-day mortality
| Variable | Model 1 | Model 2 | Model 3 | Model 4 | ||||
|---|---|---|---|---|---|---|---|---|
| HR (95%CI) | P-value | HR (95%CI) | P-value | HR (95%CI) | P-value | HR (95%CI) | P-value | |
| 30-day mortality | ||||||||
| SHR continuous | 1.52 (1.33 ~ 1.72) | < 0.001 | 1.59 (1.39 ~ 1.82) | < 0.001 | 1.46 (1.25 ~ 1.70) | < 0.001 | 1.40 (1.20 ~ 1.63) | < 0.001 |
| SHR tertiles | ||||||||
| T1(0.23–1.00) | 1(Ref) | 1(Ref) | 1(Ref) | 1(Ref) | ||||
| T2(1.00–1.37) | 1.55 (1.12 ~ 2.16) | 0.009 | 1.55 (1.12 ~ 2.16) | 0.009 | 1.70 (1.21 ~ 2.40) | 0.002 | 1.75 (1.23 ~ 2.48) | 0.002 |
| T3(1.37–6.37) | 2.56 (1.88 ~ 3.47) | < 0.001 | 2.59 (1.91 ~ 3.53) | < 0.001 | 2.43 (1.75 ~ 3.37) | < 0.001 | 2.31 (1.66 ~ 3.22) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | < 0.001 | ||||
| 90-day mortality | ||||||||
| SHR continuous | 1.36 (1.20 ~ 1.54) | < 0.001 | 1.43 (1.25 ~ 1.63) | < 0.001 | 1.34 (1.17 ~ 1.54) | < 0.001 | 1.31 (1.14 ~ 1.51) | < 0.001 |
| SHR tertiles | ||||||||
| T1(0.23–1.00) | 1(Ref) | 1(Ref) | 1(Ref) | 1(Ref) | ||||
| T2(1.00–1.37) | 1.32 (1.01 ~ 1.72) | 0.043 | 1.32 (1.01 ~ 1.72) | 0.042 | 1.42 (1.08 ~ 1.88) | 0.013 | 1.45 (1.09 ~ 1.92) | 0.010 |
| T3(1.37–6.37) | 1.89 (1.47 ~ 2.43) | < 0.001 | 1.97 (1.53 ~ 2.54) | < 0.001 | 1.96 (1.49 ~ 2.56) | < 0.001 | 1.93 (1.47 ~ 2.54) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | < 0.001 | ||||
Model 1: No adjusted
Model 2: Adjusted for age, sex, and race
Model 3: Adjusted for age, sex, race, body mass index, mean arterial pressure, peripheral oxygen saturation, hemoglobin, platelets, white blood cell, anion gap, creatinine, activated partial thromboplastin time, myocardial infarction, chronic kidney disease, severe liver disease, chronic pulmonary disease, and diabetes
Model 4: Adjusted for age, sex, race, body mass index, mean arterial pressure, peripheral oxygen saturation, hemoglobin, platelets, white blood cell, anion gap, creatinine, activated partial thromboplastin time, myocardial infarction, chronic kidney disease, severe liver disease, chronic pulmonary disease, diabetes, OASIS, continuous renal replacement therapy, and mechanical ventilation
SHR Stress hyperglycemia ratio, OASIS Oxford Acute Severity of Illness Score
Fig. 3.
Association of SHR with 30-day (A) and 90-day (B) mortality in critically ill patients with HF. Solid and dashed lines represent the predicted values and 95% confidence intervals, respectively. Adjusted for age, sex, race, body mass index, mean arterial pressure, peripheral oxygen saturation, hemoglobin, platelets, white blood cell, anion gap, creatinine, activated partial thromboplastin time, myocardial infarction, chronic kidney disease, severe liver disease, chronic pulmonary disease, diabetes, OASIS, continuous renal replacement therapy, and mechanical ventilation. Abbreviations: SHR, stress hyperglycemia ratio; OASIS, Oxford Acute Severity of Illness Score
Subgroup analysis
Subgroup analyses were conducted to evaluate potential effect modifications in the association between SHR and mortality among critically ill heart failure patients, stratified by age, sex, myocardial infarction, diabetes, chronic pulmonary disease, chronic kidney disease, and mechanical ventilation status (Fig. 4). The association between elevated SHR and increased risk of mortality was consistent across all predefined subgroups for both 30-day and 90-day outcomes. No significant interaction effects were observed in any of the subgroups (all P for interaction > 0.05).
Fig. 4.
Subgroup analyses of the association between SHR with 30- and 90-day mortality in critically ill patients with HF. Adjusted for age, sex, race, body mass index, mean arterial pressure, peripheral oxygen saturation, hemoglobin, platelets, white blood cell, anion gap, creatinine, activated partial thromboplastin time, myocardial infarction, chronic kidney disease, severe liver disease, chronic pulmonary disease, diabetes, OASIS, continuous renal replacement therapy, and mechanical ventilation. Abbreviations: SHR, stress hyperglycemia ratio; OASIS, Oxford Acute Severity of Illness Score
Sensitivity analysis
There was a significant relationship between higher SHR levels and increased 365-day and ICU mortality in all models. In Model 4, which was fully adjusted, a 1-unit rise in SHR correlated with a 85% increase in the risk of ICU mortality (HR 1.85, 95% CI 1.53–2.24, p < 0.001) and a 21% increase in the risk of mortality within 365 days (HR 1.21, 95% CI 1.06–1.38, p = 0.004). As a categorical variable, SHR showed consistent patterns across tertiles (Supplementary Table 4).
Discussion
Our study utilized the MIMIC-IV database to examine the association between SHR and mortality in critically ill patients with heart failure. Following extensive multivariable adjustment, elevated SHR was independently associated with increased risks of mortality at 30 days, 90 days, 365 days, and throughout ICU stay, demonstrating a consistent positive linear dose-response relationship. These findings corroborate and extend previous evidence, which was primarily derived from cohorts with acute heart failure [22], by demonstrating that this association remains robust in a broader, real-world ICU population encompassing the entire spectrum of HF. Furthermore, subgroup analyses indicated the consistency of this association across multiple clinical strata, with no significant interaction effects detected, thereby strengthening the generalizability of the existing knowledge.
Stress-induced hyperglycemia, a metabolic hallmark of critical illness, independently predicts mortality in critically ill populations [23, 24]. Admission hyperglycemia captures both acute stress-driven glucose surges and persistent dysregulation caused by preexisting glycemic instability [16, 25]. A national representative cohort study of 50,532 elderly patients hospitalized for HF discovered no significant association between admission hyperglycemia and mortality [26]. Thus, examining the admission glucose-disease state link alone is insufficient [27]. Using HbA1c to calculate SHR is more rational as it accounts for patients’ past glucose levels when examining the relationship between SHR and disease [28, 29]. Our study confirms that higher SHR is significantly associated with adverse outcomes in critically ill patients with HF, both in patients with and without diabetes. This is consistent with existing research showing SHR’s association with outcomes in critical populations [30–34].
Among non-surgical patients with HF and type 2 diabetes, SHR exhibits a U-shaped association with adverse results [35]. In instances of acute HF among patients, high SHR is an independent predictor of high mortality [20]. Our study extends to all HF patients in the intensive care unit, including those admitted for other critical conditions. In chronic HF, prolonged activation of neurohormonal systems, such as the renin-angiotensin-aldosterone system and the sympathetic nervous system, alters glucose balance, leading to a tendency for metabolic instability [36]. During critical illness, acute stress reactions worsen chronic changes. Activation of the hypothalamic-pituitary-adrenal axis causes overproduction of proinflammatory cytokines such as TNF-α and IL-6, further increasing hepatic glucose production and reducing insulin sensitivity in peripheral tissues [37, 38].
The increased mortality risk associated with elevated SHR in critically ill patients with HF may involve the following mechanisms: (1) Hyperglycemia-induced mitochondrial dysfunction triggers excessive formation of reactive oxygen species (ROS), which harms cardiomyocytes, while simultaneously activating the AGE-RAGE pathway, further exacerbating metabolic disturbances through sustained oxidative stress [39, 40]. Acute glucose fluctuations may further amplify systemic inflammation and exacerbate organ damage [41, 42]. (2) Acute hyperglycemia promotes syndecan-1 shedding in HF patients, degrading the endothelial glycocalyx and increasing microvascular permeability, which impairs tissue perfusion and activates coagulation pathways [43, 44]. (3) Elevated SHR, indicating a stressful state, is accompanied by increased catecholamine release, raising heart rate, boosting myocardial uptake, and causing peripheral vasoconstriction, worsening myocardial damage [45]. (4) Hyperglycemia inhibits myocardial fatty acid oxidation, resulting in inadequate energy supply, weakened myocardial contraction, and worsened heart failure symptoms [46]. (5) It can also diminish the efficiency of certain anti-HF drugs like β-blockers, thereby increasing mortality risk [47, 48].
Our study emphasizes the necessity of taking into account both acute and chronic glycemic status in clinical decision-making for HF patients with hyperglycemia in the ICU, rather than focusing solely on admission glucose. This indicates that intensive glycemic control may not benefit all patients in the same way and must be tailored to individual SHR levels [49]. The observed association between SHR and short-term mortality in critically ill patients with heart failure may be predominantly due to the systemic impact of acute stress. Whereas long-term prognosis appears to be more strongly influenced by progressive cardiac structural remodeling and comorbid conditions, with more complex underlying mechanisms diminishing the prognostic significance of transient metabolic disturbances. The independent association of SHR with mortality highlights its therapeutic relevance for early risk stratification and encourages more research into SHR-based glycemic management strategies.
There are several limitations to our study. First, the single-center nature of the database may limit the generalizability of our findings to other populations. Second, the retrospective design is inherently prone to selection bias, especially given the requirement for HbA1c availability, which led to the exclusion of a substantial proportion of the original cohort. Thus, our findings may be most applicable to HF patients with available HbA1c data. In addition, although we adjusted for numerous confounding variables, residual confounding may still exist due to unmeasured factors. Third, variations in glucose management strategies before and after ICU admission could introduce further bias. Moreover, glycemic variability and dynamic glucose trajectories have been identified as complementary prognostic markers beyond SHR in critical cardiovascular conditions [50]. Therefore, future prospective multicenter studies are essential to validate our results and further investigate these issues.
Conclusions
Our findings demonstrate a significant and independent association between elevated SHR and increased mortality in critically ill patients with heart failure. These results support the utility of SHR as a valuable biomarker for risk stratification in this clinical population.
Supplementary Information
Acknowledgements
The authors thank the Laboratory for Computational Physiology at MIT (LCP-MIT) for providing access to the MIMIC-IV database. We acknowledge Jie Liu (People’s Liberation Army General Hospital), Qilin Yang (The Second Affiliated Hospital of Guangzhou Medical University), and Haibo Li (Fujian Maternity and Child Health Hospital) for their contributions to study design and statistical analysis.
Abbreviations
- SHR
Stress hyperglycemia ratio
- HF
Heart failure
- MIMIC-IV
Medical Information Mart for Intensive Care IV
- HR
Hazard ratio
- CI
Confidence interval
- ICU
Intensive care unit
- GVIF
Generalized variance inflation factor
- STROBE
Strengthening the Reporting of Observational Studies in Epidemiology
- HbA1c
Hemoglobin A1c
- SpO2
Peripheral oxygen saturation
- APTT
Activated partial thromboplastin time
- SAPS II
Simplified Acute Physiology Score II
- OASIS
Oxford Acute Severity of Illness Score
- SOFA
Sequential Organ Failure Assessment score
- MAP
Mean arterial pressure
- BMI
Body mass index
- CRRT
Continuous renal replacement therapy
- ROS
Reactive oxygen species
Authors’ contributions
The study was designed by GJ and RD. Data collection and analysis were conducted by GJ, while RD analyzed the data and wrote the initial draft of the manuscript. EC and JL also contributed to data analysis. All authors reviewed and approved the manuscript’s final version.
Funding
This study was not funded by any external source.
Data availability
Data were sourced from the MIMIC-IV database (https://mimic.physionet.org/). The corresponding author will provide the datasets used and analyzed during the current work upon reasonable request.
Declarations
Ethics approval and consent to participate
Approval for using the MIMIC-IV database was obtained from the IRBs of both MIT and BIDMC. The MIMIC database’s existing ethical approval applies to the data in this study, eliminating the requirement for additional ethical approval or informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Savarese G, Becher PM, Lund LH, Seferovic P, Rosano GMC, Coats AJS. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res. 2023;118:3272–87. 10.1093/cvr/cvac013. [DOI] [PubMed] [Google Scholar]
- 2.Shahim B, Kapelios CJ, Savarese G, Lund LH. Global public health burden of heart failure: an updated review. Card Fail Rev. 2023;9:e11. 10.15420/cfr.2023.05. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Groenewegen A, Rutten FH, Mosterd A, Hoes AW. Epidemiology of heart failure. Eur J Heart Fail. 2020;22:1342–56. 10.1002/ejhf.1858. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Gotsman I, Zwas D, Planer D, Azaz-Livshits T, Admon D, Lotan C, et al. Clinical outcome of patients with heart failure and preserved left ventricular function. Am J Med. 2008;121:997–1001. 10.1016/j.amjmed.2008.06.031. [DOI] [PubMed] [Google Scholar]
- 5.Safavi KC, Dharmarajan K, Kim N, Strait KM, Li S-X, Chen SI, et al. Variation exists in rates of admission to intensive care units for heart failure patients across hospitals in the united States. Circulation. 2013;127:923–9. 10.1161/CIRCULATIONAHA.112.001088. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Van Diepen S, Bakal JA, Lin M, Kaul P, McAlister FA, Ezekowitz JA. Variation in critical care unit admission rates and outcomes for patients with acute coronary syndromes or heart failure among high- and low‐volume cardiac hospitals. J Am Heart Assoc. 2015;4:e001708. 10.1161/JAHA.114.001708. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Adams KF, Fonarow GC, Emerman CL, LeJemtel TH, Costanzo MR, Abraham WT, et al. Characteristics and outcomes of patients hospitalized for heart failure in the united states: rationale, design, and preliminary observations from the first 100,000 cases in the acute decompensated heart failure National registry (ADHERE). Am Heart J. 2005;149:209–16. 10.1016/j.ahj.2004.08.005. [DOI] [PubMed] [Google Scholar]
- 8.Heidenreich PA, Albert NM, Allen LA, Bluemke DA, Butler J, Fonarow GC, et al. Forecasting the impact of heart failure in the united states: a policy statement from the American heart association. Circ: Heart Fail. 2013;6:606–19. 10.1161/HHF.0b013e318291329a. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Conrad N, Judge A, Tran J, Mohseni H, Hedgecott D, Crespillo AP, et al. Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. Lancet. 2018;391:572–80. 10.1016/S0140-6736(17)32520-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Yang J, Zheng Y, Li C, Gao J, Meng X, Zhang K, et al. The impact of the stress hyperglycemia ratio on Short-term and Long-term poor prognosis in patients with acute coronary syndrome: insight from a large cohort study in Asia. Diabetes Care. 2022;45:947–56. 10.2337/dc21-1526. [DOI] [PubMed] [Google Scholar]
- 11.Wang M, Su W, Cao N, Chen H, Li H. Prognostic implication of stress hyperglycemia in patients with acute coronary syndrome undergoing percutaneous coronary intervention. Cardiovasc Diabetol. 2023;22:63. 10.1186/s12933-023-01790-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Roberts GW, Quinn SJ, Valentine N, Alhawassi T, O’Dea H, Stranks SN, et al. Relative hyperglycemia, a marker of critical illness: introducing the stress hyperglycemia ratio. J Clin Endocrinol Metab. 2015;100:4490–7. 10.1210/jc.2015-2660. [DOI] [PubMed] [Google Scholar]
- 13.Cao B, Guo Z, Li D-T, Zhao L-Y, Wang Z, Gao Y-B, et al. The association between stress-induced hyperglycemia ratio and cardiovascular events as well as all-cause mortality in patients with chronic kidney disease and diabetic nephropathy. Cardiovasc Diabetol. 2025;24:55. 10.1186/s12933-025-02610-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Zhang C, Shen H-C, Liang W-R, Ning M, Wang Z-X, Chen Y, et al. Relationship between stress hyperglycemia ratio and allcause mortality in critically ill patients: results from the MIMIC-IV database. Front Endocrinol. 2023;14:1111026. 10.3389/fendo.2023.1111026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Chen Y, Xu J, He F, Huang A, Wang J, Liu B, et al. Assessment of stress hyperglycemia ratio to predict all-cause mortality in patients with critical cerebrovascular disease: a retrospective cohort study from the MIMIC-IV database. Cardiovasc Diabetol. 2025;24:58. 10.1186/s12933-025-02613-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Yan F, Chen X, Quan X, Wang L, Wei X, Zhu J. Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning. Cardiovasc Diabetol. 2024;23:163. 10.1186/s12933-024-02265-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Liu J, Zhou Y, Huang H, Liu R, Kang Y, Zhu T, et al. Impact of stress hyperglycemia ratio on mortality in patients with critical acute myocardial infarction: insight from American MIMIC-IV and the Chinese CIN-II study. Cardiovasc Diabetol. 2023;22:281. 10.1186/s12933-023-02012-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Cheng S, Shen H, Han Y, Han S, Lu Y. Association between stress hyperglycemia ratio index and all-cause mortality in critically ill patients with atrial fibrillation: a retrospective study using the MIMIC-IV database. Cardiovasc Diabetol. 2024;23:363. 10.1186/s12933-024-02462-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Zhang Y, Yin X, Liu T, Ji W, Wang G. Association between the stress hyperglycemia ratio and mortality in patients with acute ischemic stroke. Sci Rep. 2024;14:20962. 10.1038/s41598-024-71778-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Ge T, Hu J, Zhou Y. The association between stress hyperglycemia ratio with mortality in critically ill patients with acute heart failure. Front Cardiovasc Med. 2024;11:1463861. 10.3389/fcvm.2024.1463861. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Zhou Q, Yang J, Wang W, Shao C, Hua X, Tang Y-D. The impact of the stress hyperglycemia ratio on mortality and rehospitalization rate in patients with acute decompensated heart failure and diabetes. Cardiovasc Diabetol. 2023;22:189. 10.1186/s12933-023-01908-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.You X, Zhang H, Li T, Zhu Y, Zhang Z, Chen X, et al. Stress hyperglycemia ratio and 30-day mortality among critically ill patients with acute heart failure: analysis of the MIMIC-IV database. Acta Diabetol. 2025;62:1537–47. 10.1007/s00592-025-02486-3. [DOI] [PubMed] [Google Scholar]
- 23.Lee TF, Drake SM, Roberts GW, Bersten A, Stranks SN, Heilbronn LK, et al. Relative hyperglycemia is an independent determinant of In-hospital mortality in patients with critical illness. Crit Care Med. 2020;48:e115–22. 10.1097/CCM.0000000000004133. [DOI] [PubMed] [Google Scholar]
- 24.Kim EJ, Jeong MH, Kim JH, Ahn TH, Seung KB, Oh DJ, et al. Clinical impact of admission hyperglycemia on in-hospital mortality in acute myocardial infarction patients. Int J Cardiol. 2017;236:9–15. 10.1016/j.ijcard.2017.01.095. [DOI] [PubMed] [Google Scholar]
- 25.Gao S, Huang S, Lin X, Xu L, Yu M. Prognostic implications of stress hyperglycemia ratio in patients with myocardial infarction with nonobstructive coronary arteries. Ann Med. 2023;55:990–9. 10.1080/07853890.2023.2186479. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Kosiborod M, Inzucchi SE, Spertus JA, Wang Y, Masoudi FA, Havranek EP, et al. Elevated admission glucose and mortality in elderly patients hospitalized with heart failure. Circulation. 2009;119:1899–907. 10.1161/CIRCULATIONAHA.108.821843. [DOI] [PubMed] [Google Scholar]
- 27.Scheen M, Giraud R, Bendjelid K. Stress hyperglycemia, cardiac glucotoxicity, and critically ill patient outcomes current clinical and pathophysiological evidence. Physiol Rep [Internet]. 2021. 10.14814/phy2.14713. [cited 2025 May 5];9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Krinsley JS, Preiser J-C. Is it time to abandon glucose control in critically ill adult patients? Curr Opin Crit Care. 2019;25:299–306. 10.1097/MCC.0000000000000621. [DOI] [PubMed] [Google Scholar]
- 29.Alhatemi G, Aldiwani H, Alhatemi R, Hussein M, Mahdai S, Seyoum B. Glycemic control in the critically ill: less is more. Cleve Clin J Med. 2022;89:191–9. 10.3949/ccjm.89a.20171. [DOI] [PubMed] [Google Scholar]
- 30.Xia D, Luo X, Zhu Y, Zhu J, Xie Y. Assessment of stress hyperglycemia ratio to predict mortality in critically ill patients with sepsis: a retrospective cohort study from the MIMIC-IV database. Front Endocrinol. 2025;16:1496696. 10.3389/fendo.2025.1496696. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.He H, Zheng S, Xie Y, Wang Z, Jiao S, Yang F, et al. Simultaneous assessment of stress hyperglycemia ratio and glycemic variability to predict mortality in patients with coronary artery disease: a retrospective cohort study from the MIMIC-IV database. Cardiovasc Diabetol. 2024;23:61. 10.1186/s12933-024-02146-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Mondal S, DasGupta R, Lodh M, Garai R, Choudhury B, Hazra AK, et al. Stress hyperglycemia ratio, rather than admission blood glucose, predicts in-hospital mortality and adverse outcomes in moderate-to severe COVID-19 patients, irrespective of pre-existing glycemic status. Diabetes Res Clin Pract. 2022;190:109974. 10.1016/j.diabres.2022.109974. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Zhang Y, Yan Y, Sun L, Wang Y. Stress hyperglycemia ratio is a risk factor for mortality in trauma and surgical intensive care patients: a retrospective cohort study from the MIMIC-IV. Eur J Med Res. 2024;29:558. 10.1186/s40001-024-02160-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Li X, Guo L, Zhou Y, Yuan C, Yin Y. Stress hyperglycemia ratio as an important predictive indicator for severe disturbance of consciousness and all-cause mortality in critically ill patients with cerebral infarction: a retrospective study using the MIMIC-IV database. Eur J Med Res. 2025;30:53. 10.1186/s40001-025-02309-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Zhou Y, Liu L, Huang H, Li N, He J, Yao H, et al. Stress hyperglycemia ratio and in-hospital prognosis in non-surgical patients with heart failure and type 2 diabetes. Cardiovasc Diabetol. 2022;21:290. 10.1186/s12933-022-01728-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Hartupee J, Mann DL. Neurohormonal activation in heart failure with reduced ejection fraction. Nat Rev Cardiol. 2017;14:30–8. 10.1038/nrcardio.2016.163. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Song G, Liu X, Lu Z, Guan J, Chen X, Li Y, et al. Relationship between stress hyperglycaemic ratio (SHR) and critical illness: a systematic review. Cardiovasc Diabetol. 2025;24:188. 10.1186/s12933-025-02751-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Marino LO, Souza HP. Dysfunction of the hypothalamic-pituitary-adrenal axis in critical illness: a narrative review for emergency physicians. Eur J Emerg Med. 2020;27:406–13. 10.1097/MEJ.0000000000000693. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Liang Q, Kobayashi S. Mitochondrial quality control in the diabetic heart. J Mol Cell Cardiol. 2016;95:57–69. 10.1016/j.yjmcc.2015.12.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.González P, Lozano P, Ros G, Solano F. Hyperglycemia and oxidative stress: an integral, updated and critical overview of their metabolic interconnections. Int J Mol Sci. 2023;24:9352. 10.3390/ijms24119352. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Yang Z, Li Y, Guo T, Yang M, Chen Y, Gao Y. The effect of inflammatory markers on mortality in patients with acute myocardial infarction. Sci Rep. 2025;15:14514. 10.1038/s41598-025-98408-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Yang Z, Li Y, Liu Y, Zhong Z, Ditchfield C, Guo T, et al. Prognostic effects of glycaemic variability on diastolic heart failure and type 2 diabetes mellitus: insights and 1-year mortality machine learning prediction model. Diabetol Metab Syndr. 2024;16:280. 10.1186/s13098-024-01534-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Nieuwdorp M, Van Haeften TW, Gouverneur MCLG, Mooij HL, Van Lieshout MHP, Levi M, et al. Loss of endothelial glycocalyx during acute hyperglycemia coincides with endothelial dysfunction and coagulation activation in vivo. Diabetes. 2006;55:480–6. 10.2337/diabetes.55.02.06.db05-1103. [DOI] [PubMed] [Google Scholar]
- 44.Nieuwdorp M, Mooij HL, Kroon J, Atasever B, Spaan JAE, Ince C, et al. Endothelial glycocalyx damage coincides with microalbuminuria in type 1 diabetes. Diabetes. 2006;55:1127–32. 10.2337/diabetes.55.04.06.db05-1619. [DOI] [PubMed] [Google Scholar]
- 45.De La Perez RA, School of Medicine BA, University SP, School of Medicine, Buenos Aires University, Buenos Aires, Argentina, Cintora FM, School of Medicine, Buenos Aires University, Buenos, Aires et al. Argentina,. Neuroendocrine system regulatory mechanisms: acute coronary syndrome and stress hyperglycaemia. Eur Cardiol Rev. 2018;13:29. 10.15420/ecr.2017:19:3. [DOI] [PMC free article] [PubMed]
- 46.Fuentes-Antrás J, Picatoste B, Ramírez E, Egido J, Tuñón J, Lorenzo Ó. Targeting metabolic disturbance in the diabetic heart. Cardiovasc Diabetol. 2015;14:17. 10.1186/s12933-015-0173-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Shen J, Greenberg BH. Diabetes management in patients with heart failure. Diabetes Metab J. 2021;45:158–72. 10.4093/dmj.2020.0296. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.DiNicolantonio JJ, Fares H, Niazi AK, Chatterjee S, D’Ascenzo F, Cerrato E, et al. β-blockers in hypertension, diabetes, heart failure and acute myocardial infarction: a review of the literature. Open Heart. 2015;2:e000230. 10.1136/openhrt-2014-000230. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Marik PE, Bellomo R. Stress hyperglycemia: an essential survival response! Crit Care. 2013;17:305. 10.1186/cc12514. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Chen Y, Yang Z, Liu Y, Gue Y, Zhong Z, Chen T, et al. Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning. Cardiovasc Diabetol. 2024;23:426. 10.1186/s12933-024-02521-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data were sourced from the MIMIC-IV database (https://mimic.physionet.org/). The corresponding author will provide the datasets used and analyzed during the current work upon reasonable request.