Introduction

Heart failure (HF), a prevalent but serious disease, has seen a 29% global prevalence increase from 2010 to 2019 [], with a 30%‐50% risk of mortality or rehospitalization within the acute phase [].

The critically ill patients with HF are particularly vulnerable to sepsis, which may be related to immune dysfunction [], cardiac remodeling [], or bacterial translocation []. It was reported that the mortality rate reach 90% in those combined cardiac dysfunction and sepsis [], and the recurrence rate of sepsis in patients with HF is 3-fold higher than those without HF [].

Thus, identifying modifiable risk factors for sepsis occurrence is crucial for global public health and prevention strategies. The pathogenesis of sepsis in critical illness is multifactorial, with emerging evidence suggesting that hyperglycemia, especially under stress, may contribute to the development and prognosis of sepsis involving immune dysregulation, inflammatory activation, and organ impairment [-]. Stress hyperglycemia ratio (SHR) has emerged as a novel and potentially valuable metric in the assessment of acute glycemic levels, which is calculated by comparing admission blood glucose levels to glycated hemoglobin (HbA1c), distinguishing an acute stress-induced hyperglycemia from preexisting poor glycemic control []. Multiple studies have demonstrated that higher SHR levels were associated with poorer prognosis in patients diagnosed with acute coronary syndrome [], acute HF [], and sepsis []. However, the relationship between SHR and the occurrence of sepsis among patients with HF remains unclear. Inflammatory markers such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, neutrophil-to-platelet ratio (NPR), neutrophil-monocyte-to-lymphocyte ratio (NMLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI) have been investigated in patients with sepsis and show promise as prognostic indicators [-]. Given the intricate interplay between hyperglycemia and inflammation [], and the well-documented role of inflammatory processes in the pathogenesis of sepsis, we hypothesize that inflammation may serve as a mediating factor in the association between SHR and sepsis occurrence. Therefore, this study aims to fill this critical gap by investigating the association of SHR and the risk of sepsis among critically ill patients with HF, based on data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Furthermore, we explored whether inflammation acts as a potential mediator in this relationship.

MethodsEthical Considerations

This study was a retrospective observational design with publicly available data from the MIMIC-IV database, which includes information on critically ill patients admitted to the intensive care units (ICUs) at Beth Israel Deaconess Medical Center from 2008 to 2022 []. The data acquisition process adhered to all relevant regulations. Author BY obtained a Collaborative Institutional Training Initiative license (Record ID 12861338) and the necessary permissions to extract data from the MIMIC-IV database. The project was approved by the institutional review boards of both the Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center and followed the Strengthening the Reporting of Cohort Studies in Surgery guidelines []. Our research was conducted in accordance with the Declaration of Helsinki. Since the MIMIC-IV database includes fully anonymous or deidentified patient records and all protected health information has been removed in accordance with Health Insurance Portability and Accountability Act (HIPAA) privacy standards, participants or the participants’ legal guardians or next of kin consent for publication is not applicable to this study. This study did not involve any ethical conflict issues.

Study Design and Participants

This is a retrospective observational study based on a large-scale critical care database, which included the ICU patients who were diagnosed with HF based on the International Classification of Diseases, Ninth Revision (ICD-9) and International Classification of Diseases, Tenth Revision (ICD-10) codes (Table S1 in ). We excluded (1) patients younger than 18 years of age; (2) patients with an ICU stay duration less than 24 hours; (3) for patients with multiple ICU admissions, only data from the first hospitalization were included; (4) patients lacked data of blood glucose or HbA1c; and (5) patients diagnosed with sepsis [] less than 6 hours after ICU admission. Finally, a total of 1205 patients were enrolled in the study cohort and divided into 4 groups based on SHR quartile (). Quartile 4 representing the highest SHR was consistent with previously published studies [].

‎Figure 1. Patient selection flowchart. Flowchart illustrating the inclusion and exclusion criteria for patient selection in a retrospective cohort study investigating the association between the stress hyperglycemia ratio and 7-day sepsis risk among critically ill patients with heart failure. Data were derived from the MIMIC-IV database, Beth Israel Deaconess Medical Center (Boston, MA, United States), encompassing admissions from 2008 to 2022. A total of 1205 patients were included in the final analysis. Inclusion and exclusion criteria are detailed. HbA1c: glycated hemoglobin; ICU: intensive care unit; MIMIC-IV: Medical Information Mart for Intensive Care-IV. Definition of Sepsis

Sepsis was defined according to Sepsis-3 criteria []. The time of sepsis onset (Tsepsis) was determined as the earlier of the first clinical suspicion of infection (Tsuspicion) and the first time the Sequential Organ Failure Assessment (SOFA) score increased by ≥2 points (TSOFA), provided that TSOFA occurred no more than 24 hours before or 12 hours after Tsuspicion according to published literature []. To specifically capture new-onset sepsis developing during the ICU stay and to minimize the inclusion of infections likely present at admission, we excluded all patients for whom Tsepsis occurred within the first 6 hours after ICU admission in our primary analysis.

Inflammation Biomarkers

Inflammatory biomarkers included NLR, platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, NPR, NMLR, SII, and SIRI. Detailed calculation methods are provided in Table S2 in .

Data Collection

The SHR was calculated using the formula: SHR=admission blood glucose (mg/dL)/ (28.7×HbA1c (%)−46.7) []. Blood glucose and HbA1c values were sourced from the initial records after ICU admission. The baseline variables were chosen on the bases of their possible influence on sepsis risk and cardiovascular risk of the individuals and followed six categories: (1) demographics (sex, age, race, and BMI), (2) vital signs (heart rate, systolic blood pressure, diastolic blood pressure, mean blood pressure, respiratory rate, temperature, and 6-hour fluid intake), (3) laboratory parameters (hemoglobin, white blood cell, platelet, blood urea nitrogen, creatinine, glucose, HbA1c, etc), (4) comorbidities (diabetes, hypertension, shock, etc), (5) disease severity scores (Charlson comorbidity index, Acute Physiology Score III, systemic inflammatory response syndrome score, SOFA score, etc), and (6) medication (angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, diuretics, antibiotics drugs, insulin, glucocorticoid, oral hypoglycemic drugs, etc). For all baseline variables, the first recorded value within the initial 6 hours after ICU admission was extracted using PostgreSQL (version 13.7.1; PostgreSQL Global Development Group) and Navicat Premium (version 17; PremiumSoft CyberTech Ltd) and used in the analysis. This study adhered to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for observational studies []. Variables with a missing rate ≤25% were imputed using the Multivariate Imputation by Chained Equations method, generating 20 imputed datasets, applying predictive mean matching for continuous variables and logistic regression for categorical variables. The imputation model included all baseline variables used in the analysis. Variables with a missing rate >25% were excluded to avoid bias. Further details are provided in Table S3 in .

Definition of Outcomes

The primary outcome was the occurrence of sepsis within 7 days after ICU admission, which was defined based on the acute disease distribution patterns observed in our dataset (Figure S1 in ) and was consistent with the early prediction window commonly adopted in prior studies focusing on acute sepsis in ICU settings []. Secondary outcomes included 7-day mortality, a composite outcome of sepsis occurrence or mortality within 7 days, 28-day mortality, and in-hospital mortality.

Statistical Analysis

Continuous variables were described using median (IQR) or mean (SD), and comparisons between groups were performed using the Kruskal-Wallis test or ANOVA, as appropriate. Whereas categorical variables were expressed as frequency (%) and were analyzed using the Fisher exact test or the Pearson chi-square test. The Kaplan-Meier survival analysis was used to estimate the occurrence of primary outcomes among groups according to the log-rank test. To assess the impact of SHR on sepsis occurrence, the multivariate proportional hazard regression models (Cox regression models) were used to assess the hazard ratio (HR) and 95% CI for event occurrence. Specifically, model 1 was unadjusted for covariates, and model 2 further adjusted sex, age, and BMI. Considering the impacts of patient general condition, vital signs, SOFA scores, and steroid use on outcomes, based on model 2, model 3 incorporated acute HF, diabetes, shock, SOFA score, antibiotic, insulin, and glucocorticoid. To explore the potential dose-response relationship between SHR and the risk of sepsis occurrence, restricted cubic splines (RCS) analysis was performed and adjusted by the same models in model 3. Furthermore, subgroup analyses were conducted to delve deeper into the data, stratifying outcomes based on sex, age, BMI, diabetes, acute HF, and insulin use. These subgroup analyses were performed using comprehensive regression models adjusted for potential confounding factors. To test whether diabetes status modifies the relationship between SHR and sepsis risk, we introduced an interaction term between SHR (as a continuous variable) and diabetes status (yes or no) within the Cox regression model, which was also adjusted for the same covariates as in the fully adjusted model (model 3). Moreover, to rigorously evaluate the robustness of our main findings, sensitivity analyses were performed by logistic regression, Fine-Gray, and competing risk Cox models. In addition, we extended the exclusion window for incident sepsis from the original 6 hours to 12, 24, 36, and 48 hours after ICU admission, respectively, and repeated the Cox proportional hazards regression models.

To determine whether inflammatory indices (such as NLR and SII) mediate the association between SHR and sepsis occurrence, we used the Cox model in the CMAverse package in R (R Foundation for Statistical Computing). We performed 1000 bootstrap simulations to estimate each mediator’s effect and calculate the mediation proportion. The average direct effect represents the impact of SHR on sepsis without mediation, while the average causal mediation effect indicates the effect of SHR on sepsis occurrence via mediators. The mediation proportion was calculated by dividing the average causal mediation effect by the total effect. All statistical analyses were executed using R software (version 4.4.1), with statistical significance set as a 2-sided P value of less than .05.

ResultsBaseline Characteristics

A total of 25,718 patients with HF from the ICU were consecutively recruited. Following certain exclusion criteria, 1205 patients were ultimately included, of whom 63.4% (n=764) were male, and had a median age of 71.51 (IQR 62.45‐79.47) years. presents the baseline characteristics. There were 297 (24.6%) patients with hypertension, 564 (46.8%) patients with diabetes, 855 (71%) patients with acute HF, and 595 (49.4%) patients with pneumonia.

Table 1. Baseline characteristics of study participants stratified by stress hyperglycemia ratio (SHR) quartiles.Overall (N=1205)Groups of SHRP valueQ1 (n=301)Q2 (n=302)Q3 (n=301)Q4 (n=301)Male, n (%)764 (63.4)206 (68.4)194 (64.2)188 (62.5)176 (58.5).08Age (years), median (IQR)71.51 (62.45‐79.47)71.64 (60.73‐79.40)71.69 (62.35‐79.97)71.61 (62.25‐79.25)71.15 (63.13‐79.61).92Race, n (%).38Black108 (9)26 (8.6)30 (9.9)21 (7)31 (10.3)White800 (66.4)202 (67.1)206 (68.2)207 (68.8)185 (61.5)Others297 (24.6)73 (24.3)66 (21.9)73 (24.3)85 (28.2)BMI (kg/m2), median (IQR)28.54 (24.42‐32.92)28.73 (25.10‐33.59)27.94 (24.42‐31.99)28.64 (24.54‐32.87)28.20 (23.93‐33.20).65HbA1c (%), median (IQR)6.00 (5.60‐7.10)6.50 (5.90‐8.00)5.80 (5.50‐6.57)5.90 (5.50‐6.70)6.00 (5.50‐7.40)<.001Glucose (mmol/L), median (IQR)133.00 (106.00‐182.00)99.00 (89.00‐115.00)115.00 (104.00‐134.75)140.00 (123.00‐171.00)219.00 (175.00‐275.00)<.001SHR, median (IQR)1.03 (0.85‐1.32)0.74 (0.62‐0.80)0.94 (0.90‐0.99)1.13 (1.08‐1.21)1.59 (1.42‐1.86)<.001Vital signs, median (IQR)Heart rate (bpm)81.28 (73.59‐91.48)81.04 (73.41‐89.58)80.85 (74.38‐89.13)81.54 (74.40‐93.11)82.31 (72.71‐92.97).43SBP (mm Hg)111.48 (104.13‐122.04)109.90 (104.39‐119.23)111.00 (105.26‐119.29)111.70 (103.58‐122.68)113.79 (104.18‐126.50).08DBP (mm Hg)60.85 (53.94‐69.63)58.71 (52.62‐65.57)59.06 (52.74‐67.47)62.10 (55.45‐70.69)64.44 (56.58‐72.86)<.001MBP (mm Hg)75.91 (70.45‐83.49)74.24 (69.61‐81.09)74.66 (70.02‐81.41)77.53 (70.95‐84.12)78.42 (71.95‐86.63)<.001Respiratory rate (breaths per minute)19.08 (17.22‐21.06)18.65 (16.91‐20.48)18.87 (17.01‐20.92)19.36 (17.29‐21.31)19.50 (17.67‐21.61)<.001Temperature (℃)37.06 (36.83‐37.44)37.06 (36.83‐37.40)37.10 (36.89‐37.50)37.11 (36.83‐37.44)37.06 (36.83‐37.50).54Intake (mL per 6 hours)499.86 (227.77‐1221.86)579.12 (240.00‐1275.58)718.86 (254.32‐1654.32)477.99 (244.53‐1111.45)382.35 (165.92‐800.00)<.001Laboratory test, median (IQR)Hemoglobin (g/dL)12.00 (10.40‐13.70)11.60 (9.80‐13.40)12.00 (10.70‐13.47)12.30 (10.50‐13.90)12.20 (10.40‐13.80).03White blood cell (109/L)12.10 (8.80‐16.60)11.20 (8.00‐16.50)12.60 (8.70‐17.78)11.90 (9.20‐15.50)12.30 (9.40‐16.60).05Platelets (109/L)217.00 (176.00‐274.00)207.00 (171.00‐262.00)214.50 (171.50‐267.75)210.00 (174.00‐265.00)234.00 (189.00‐287.00)<.001HCT (%)36.80 (31.90‐41.20)35.60 (30.90‐41.00)36.75 (32.40‐41.18)37.70 (32.10‐41.80)37.00 (32.00‐41.40).22BUN (mg/dL)24.00 (18.00‐36.00)25.00 (18.00‐38.00)22.00 (17.00‐33.00)23.00 (17.00‐33.00)25.00 (19.00‐43.00).001Creatinine (μmol/L)1.20 (0.90‐1.60)1.20 (0.90‐1.70)1.20 (0.90‐1.58)1.10 (0.90‐1.50)1.20 (1.00‐1.80).005Chloride (mmol/L)104.00 (100.00‐107.00)104.00 (101.00‐107.00)105.00 (101.00‐108.00)103.00 (99.00‐106.00)102.00 (98.00‐105.00)<.001Sodium (mmol/L)139.00 (136.00‐141.00)139.00 (137.00‐141.00)139.00 (137.00‐141.00)138.00 (136.00‐141.00)138.00 (135.00‐141.00)<.001Potassium (mmol/L)4.40 (4.00‐4.80)4.40 (4.00‐4.70)4.30 (4.00‐4.70)4.40 (4.00‐4.70)4.50 (4.10‐5.00).004Bicarbonate (mmol/L)25.00 (22.00‐27.00)25.00 (23.00‐28.00)25.00 (23.00‐27.00)24.00 (22.00‐27.00)23.00 (21.00‐26.00)<.001PT (seconds)14.70 (12.90‐17.40)15.30 (13.50‐17.90)15.70 (13.60‐18.00)14.30 (12.70‐17.40)13.70 (12.40‐16.40)<.001APTT (seconds)41.50 (30.90‐73.80)40.50 (31.60‐72.40)41.45 (31.63‐72.57)41.30 (30.20‐74.00)43.80 (29.10‐79.90).94Comorbidities, n (%)Acute HF855 (71)205 (68.1)210 (69.5)209 (69.4)231 (76.7).08Hypertension297 (24.6)73 (24.3)83 (27.5)73 (24.3)68 (22.6).56Coronary heart disease934 (77.5)229 (76.1)220 (72.8)234 (77.7)251 (83.4).02AMI551 (45.7)142 (47.2)144 (47.7)131 (43.5)134 (44.5).69Valve disorder540 (44.8)166 (55.1)146 (48.3)122 (40.5)106 (35.2)<.001Atrial fibrillation604 (50.1)163 (54.2)151 (50)157 (52.2)133 (44.2).08COPD329 (27.3)99 (32.9)84 (27.8)73 (24.3)73 (24.3).06Chronic kidney disease421 (34.9)118 (39.2)103 (34.1)95 (31.6)105 (34.9).26Liver disease74 (6.1)23 (7.6)22 (7.3)19 (6.3)10 (3.3).11Cancer40 (3.3)5 (1.7)8 (2.6)16 (5.3)11 (3.7).08Diabetes564 (46.8)166 (55.1)100 (33.1)138 (45.8)160 (53.2)<.001Lipoprotein metabolism disorders800 (66.4)216 (71.8)203 (67.2)186 (61.8)195 (64.8).07Shock284 (23.6)81 (26.9)58 (19.2)60 (19.9)85 (28.2).01Pneumonia104 (8.6)28 (9.3)25 (8.3)26 (8.6)25 (8.3).97Disease severity score, median (IQR)Charlson comorbidity index6.00 (4.00‐8.00)6.00 (5.00‐8.00)6.00 (4.00‐8.00)6.00 (4.00‐8.00)7.00 (5.00‐8.00).05APSIII40.00 (31.00‐50.00)41.00 (32.00‐53.00)39.00 (30.00‐48.75)38.00 (31.00‐49.00)41.00 (32.00‐52.00).15SAPSII35.00 (28.00‐43.00)35.00 (29.00‐43.00)37.00 (31.00‐44.00)34.00 (26.00‐43.00)35.00 (28.00‐42.00).05SIRS3.00 (2.00‐3.00)3.00 (2.00‐3.00)3.00 (2.00‐3.00)3.00 (2.00‐3.00)3.00 (2.00‐3.00).49SOFA3.00 (1.00‐5.00)4.00 (2.00‐6.00)3.00 (1.00‐6.00)2.00 (1.00‐4.00)2.00 (1.00‐4.00)<.001Medication at baseline, n (%)ACEI/ARB398 (33)108 (35.9)106 (35.1)95 (31.6)89 (29.6).31Amiodarone154 (12.8)37 (12.3)38 (12.6)33 (11)46 (15.3).45Anticoagulants139 (11.5)48 (15.9)29 (9.6)32 (10.6)30 (10).05NSAID770 (63.9)210 (69.8)202 (66.9)195 (64.8)163 (54.2)<.001β-Blocker737 (61.2)203 (67.4)209 (69.2)174 (57.8)151 (50.2)<.001Diuretic drug751 (62.3)197 (65.4)204 (67.5)177 (58.8)173 (57.5).03Hypolipidemic drug751 (62.3)206 (68.4)194 (64.2)187 (62.1)164 (54.5).004Antibiotics595 (49.4)184 (61.1)193 (63.9)130 (43.2)88 (29.2)<.001Insulin747 (62)221 (73.4)192 (63.6)163 (54.2)171 (56.8)<.001Glucocorticoid167 (13.9)49 (16.3)50 (16.6)34 (11.3)34 (11.3).09Oral hypoglycemic drugs19 (1.6)8 (2.7)5 (1.7)1 (0.3)5 (1.7).15

aBaseline demographic, clinical, laboratory, and treatment characteristics of 1205 critically ill patients with HF, stratified by quartiles of the stress hyperglycemia ratio. Data were derived from the Medical Information Mart for Intensive Care-IV database (Boston, MA, United States; 2008‐2022). P values were calculated using the Kruskal-Wallis test for continuous variables and the Pearson chi-square or Fisher exact test for categorical variables.

bSHR quartiles: Q1: <0.8490; Q2: 0.8490-1.0331; Q3: 1.0331-1.3177; Q4: >1.3177.

cHbA1c: glycated hemoglobin.

dSBP: systolic blood pressure.

eDBP: diastolic blood pressure.

fMBP: mean blood pressure.

gHCT: hematocrit.

hBUN: blood urea nitrogen.

iPT: prothrombin time.

jAPTT: activated partial thromboplastin time.

kHF: heart failure.

lAMI: acute myocardial infarction.

mCOPD: chronic obstructive pulmonary disease.

nAPSIII: Acute Physiology Score III.

oSAPSII: Simplified Acute Physiology Score II.

pSIRS: systemic inflammatory response syndrome.

qSOFA: Sequential Organ Failure Assessment.

rACEI/ARB: angiotensin-converting enzyme inhibitor/angiotensin receptor blocker.

sNSAID: nonsteroidal anti-inflammatory drug.

All participants were stratified into 4 groups (Q1-4) based on SHR quartiles: Q1: <0.8490 (n=301); Q2: 0.8490‐1.0331 (n=302); Q3: 1.0331‐1.3177 (n=301); and Q4: >1.3177 (n=301). The highest SHR group (Q4) exhibited increased vital signs, including respiratory rate and diastolic blood pressure, alongside elevated laboratory values such as platelet count and serum potassium levels. They had a higher burden of comorbidities (eg, coronary heart disease) and disease severity scores (Charlson index and Simplified Acute Physiology Score II), but lower antibiotic use. In contrast, the lowest SHR group (Q1) had higher HbA1c, diabetes prevalence, and insulin use. However, parameters such as age, BMI, and race levels showed no significant variation across SHR groups.

Furthermore, for clinical outcome, Table S4 in provides a comparison of baseline characteristics between patients with sepsis and those without sepsis within 7 days after admitted to the ICU. Compared to patients without sepsis, those with sepsis exhibited significantly elevated levels of SHR (median 1.13, IQR 0.92‐1.46 vs median 1.02, IQR 0.84‐1.29; P=.001) and higher inflammation infection.

Considering SHR be associated with diabetes status, compared to patients without diabetes, those with diabetes had higher BMI (28.96 vs 27.90 kg/m²) and no significant difference in SHR (P=.79; Table S5 in ).

Relationship Between SHR and Parameters Related to Sepsis Diagnosis

Figures S2 and S3 in illustrate the relationship between SHR and markers of sepsis. The association between SHR and antibiotic use (defined as administration during the entire ICU stay) was assessed using the point-biserial correlation coefficient, yielding a weak negative correlation (r=−0.196). The Mann-Whitney U test further confirmed a difference (P<.001). In contrast, the linear relationship between SHR and SOFA score was evaluated using Spearman correlation and was not statistically significant (r=0.056; P=.05).

Clinical Outcomes According to SHR Levels

In this study, sepsis occurred in 162 (13.4%) patients with HF, among whom, the Q4 (SHR >1.3177) exhibited the highest sepsis occurrence at 17.3%, thus being chosen as the reference group, and Q1 to Q4 showed progressively higher risks (9.6% vs 11.3% vs 15.6% vs 17.3%). Notably, the Q4 group exhibited a significantly higher mortality rate of 17.3% (52/301) and a composite outcome rate of 34.2% (103/301) compared with all other groups (both P<.001; S6 in ).

The Kaplan-Meier curves illustrated that patients with the highest SHR (Q4) exhibited the highest 7-day all-cause mortality across different SHR levels (P=.02; log-rank P<.001; Figure S4 in ). Specifically, although the association was nonsignificant in the diabetes subgroup (P=.17) and the nondiabetes subgroup (P=.07), the Q4 had relatively the highest 7-day all-cause mortality in the diabetes subgroup, while in the nondiabetes subgroup, patients in Q3 and Q4 groups had higher 7-day all-cause mortality than other groups (Figures S5 and S6 in ).

Association of SHR Index With Sepsis Occurrence

To explore the independent relationship of SHR index with the occurrence of sepsis, we constructed 3 Cox regression models (), and significant values were all found in models 1‐3. When SHR emerged as a continuous variable, in the fully adjusted model 3, per 1-unit higher SHR index was associated with an 18% higher risk of sepsis occurrence (HR 1.18, 95% CI 1.01‐1.38; P=.04). Additionally, similar trends were observed when patients were grouped according to quartiles of the SHR index. Taking the highest quartile of the SHR index (Q4) as the reference, the occurrence risk of sepsis in other groups showed a gradual upward trend of Q1 <Q2 <Q4 (HR 0.52, 95% CI 0.32‐0.82; P=.005 vs HR 0.62, 95% CI 0.40‐0.97; P=.04), while compared to Q4, Q3 showed no significant difference (HR 0.96, 95% CI 0.65‐1.43; P=.85), indicating a dose-response relationship between SHR levels and sepsis risk. However, the trend test indicated a significant upward trend in sepsis risk with increasing quartiles (P for trends=.001).

Table 2. Cox regression models for the association between stress hyperglycemia ratio (SHR) and 7-day sepsis occurrence.Events, nCases, nModel 1Model 2Model 3HR (95% CI)P valueHR (95% CI)P valueHR (95% CI)P valueSHR——1.31 (1.11‐1.54).0021.34 (1.13‐1.59).0011.18 (1.01‐1.38).04Q1293010.54 (0.34‐0.84).0070.54 (0.34‐0.85).0080.52 (0.32‐0.82).005Q2343020.62 (0.40‐0.96).030.62 (0.40‐0.96).030.62 (0.40‐0.97).04Q3473010.90 (0.61‐1.33).590.90 (0.60‐1.33).590.96 (0.65‐1.43).85Q452301Reference—Reference—Reference—P for trends——1.25 (1.09‐1.44).0021.25 (1.08‐1.44).0021.26 (1.09‐1.46).001

aMultivariable Cox proportional hazards regression model evaluating the association between the SHR (both as a continuous variable and by quartiles) and the risk of sepsis within 7 days of intensive care unit admission among 1205 critically ill patients with heart failure. Data were derived from the Medical Information Mart for Intensive Care-IV database (Boston, MA, United States; 2008‐2022).

bModel 1: unadjusted.

cModel 2: adjusted for sex, age, and BMI.

dModel 3: adjusted for sex, age, BMI, acute heart failure, diabetes, shock, Sequential Organ Failure Assessment score, antibiotic use, insulin use, and glucocorticoid use.

eHR: hazard ratio.

fNot available.

The interaction analysis between SHR and diabetes was statistically significant (HR 1.96, 95% CI 1.23‐3.14; P=.005), indicating that the association between SHR and sepsis was stronger in patients with diabetes. Similarly, a significant interaction was also observed with insulin use (HR 0.48, 95% CI 0.30‐0.77; P=.002), whereas interactions with BMI (both continuous and categorized) were not statistically significant (P=.17 and P=.07, respectively; Table S7 in ).

Additionally, multivariable adjusted RCS analysis was used to provide a comprehensive examination of the continuous relationship between SHR and the occurrence of sepsis. Relationship showed a nonlinear saturation effect as SHR increased, with the specific turning point of 1.29 (P for nonlinearity=.02; ). Specifically, sepsis risk increased steeply with SHR up to 1.29, after which the risk curve plateaued.

‎Figure 2. Restricted cubic spline analysis of the association between SHR and 7-day sepsis risk. Restricted cubic spline curve depicting the dose-response relationship between the SHR (as a continuous variable) and the adjusted HR for 7-day sepsis occurrence among 1205 critically ill patients with heart failure. Data were derived from the MIMIC-IV database (Boston, MA, United States; 2008‐2022). The model was adjusted for sex, age, BMI, acute heart failure, diabetes, shock, Sequential Organ Failure Assessment score, antibiotic use, insulin use, and glucocorticoid use. The curve demonstrates a nonlinear saturation effect, with an inflection point at SHR=1.29 (P for nonlinearity=.02). The horizontal dashed line represents an HR of 1.0. The shaded area represents the 95% CI. HR: hazard ratio; MIMIC-IV: Medical Information Mart for Intensive Care-IV; SHR: stress hyperglycemia ratio. Subgroup Analysis

Subsequently, the subgroup analyses were stratified by sex (male and female), age (<65 and ≥65 years), BMI (<30 and ≥30 kg/m2), diabetes (yes and no), acute HF (yes and no), and insulin use (yes and no). No significant interactions were observed for sex, age, or acute HF subgroups (P for interaction>.05). Notably, significant interactions were detected between SHR and BMI (P=.04), diabetes (P=.01), and insulin use (P=.005; ), thus prompting further RCS analyses within these subgroups. Results indicated that in the subgroups with diabetes, insulin use, and BMI ≥30 kg/m2, a linear association was observed between SHR and sepsis incidence (all P overall<.05; P nonlinear>.05). In contrast, a U-shaped relationship was identified in the subgroup with BMI <30 kg/m2, with the inflection point at 1.03 (P overall=.01; P nonlinear=.09). No significant associations were found in the remaining subgroups (P overall>.05; Figures S7-S9 in ).

‎Figure 3. Subgroup analysis forest plot. Forest plot summarizing subgroup analyses of the association between the stress hyperglycemia ratio (as a continuous variable) and 7-day sepsis risk among 1205 critically ill patients with HF. Data were derived from the MIMIC-IV database (Boston, MA, United States; 2008‐2022). Subgroups were stratified by sex, age (<65 vs ≥65 years), BMI (<30 vs ≥30 kg/m2), diabetes status, acute HF, and insulin use. HRs and 95% CIs were adjusted for sex, age, BMI, acute HF, diabetes, and insulin use. P values for interaction are displayed to assess effect modification. HF: heart failure; HR: hazard ratio; MIMIC-IV: Medical Information Mart for Intensive Care-IV. Sensitivity Analyses

We performed different sensitivity tests to validate the robustness of our results. First, considering 7-day all-cause mortality as a competing event, the Fine-Gray competing risk model was used. In the univariable-adjusted Fine-Gray model, the cumulative occurrence of sepsis increased from Q1 to Q4 (P=.01), maintaining significance even when accounting for the competing risk of all-cause mortality (P<.001; ).

‎Figure 4. Cumulative incidence curves from Fine-Gray competing risk models, illustrating the impact of SHR quartiles on (A) the cumulative incidence of sepsis within 7 days and (B) the cumulative incidence of 7-day all-cause mortality, with each considered a competing event for the other. The analysis is based on a retrospective cohort of 1205 critically ill patients with heart failure from the MIMIC-IV database (Boston, MA, United States; 2008-2022). SHR quartiles: Q1 (<0.849); Q2 (0.849‐1.033); Q3 (1.033‐1.318); Q4 (>1.318). MIMIC-IV: Medical Information Mart for Intensive Care-IV; SHR: stress hyperglycemia ratio.

In the multivariable-adjusted Fine-Gray model, SHR, as a categorical variable (quartiles), revealed a significant overall difference in sepsis incidence across groups (P=.01). When modeled as a continuous variable in the fully adjusted model, the association was positive but not statistically significant (HR 1.18, 95% CI 0.96‐1.44; P=.11). There was a significant positive trend in sepsis risk across increasing SHR quartiles, indicating a dose-response relationship (P for trend<.001 in the fully adjusted model; Table S8 in ).

Second, the logistic regression was also made to show a significant positive association between SHR and sepsis risk (model 3: odds ratio 1.03, 95% CI 1.01‐1.05; P<.001), with sepsis risk increasing significantly across SHR quartiles (P for trend <.001; Table S9 in ).

Third, when the sepsis exclusion window was extended to 12, 24, 36, and 48 hours, the overall association remained consistent. However, the association was not significant when the exposure was modeled continuously in the fully adjusted model (all P>.05; Table S10 in ).

Mediation Analysis

shows the mediation value of inflammatory indices between SHR and sepsis occurrence.

Table 3. Mediation effects of inflammatory biomarkers on the stress hyperglycemia ratio-sepsis association.Case, nACMEADEPETEEstimate (95% CI)P valueEstimate (95% CI)P valueEstimate (95% CI)P valueEstimate (95% CI)P valueSII8071.05 (1.01 to 1.13).01