Abstract
Background
Heart failure (HF) and preserved ejection fraction (HFpEF) represents a growing health burden characterized by systemic inflammation. Yet, reliable and cost-effective inflammatory biomarkers for risk stratification in HFpEF remain limited.
Aim
We sought to examine the prognostic value of systemic immune-inflammation index (SII) on outcomes in HFpEF patients.
Methods
This study analyzed 458 (mean age:70.7 years; 59.2% women) participants with HFpEF admitted to the coronary care unit. HFpEF participants were grouped by SII values: Tertile 1 (SII ≤ 455.2), Tertile 2 (455.2–777.8), and Tertile 3 (SII > 777.8). Long-term associations between SII and composite outcomes, including all-cause, cardiovascular death, and HF rehospitalization, were evaluated. Decision curve analysis (DCA) assessed the clinical utility of SII-enhanced versus basic clinical models.
Results
Baseline demographics, comorbidities, and laboratory parameters varied significantly across SII tertiles. Of the total participants, 211 (46.1% ) experienced composite events over a mean follow-up of 41.8 months. When analyzed as a continuous variable, higher log-transformed SII was independently associated with increased risk of composite outcome of death and HF rehospitalization in the overall cohort (HR 1.50, 95% CI 1.18–1.92) and diabetic patients (HR 1.88, 95% CI 1.32–2.67), but not non-diabetic patients (HR 1.23, 95% CI 0.86–1.77) after multivariable adjustment. Similarly, in the fully adjusted tertile-based analysis, the highest SII tertile remained significantly associated with the composite outcome in the overall cohort (HR 1.70, 95% CI 1.15–2.52), with a particularly strong association observed in diabetic patients (HR 2.63, 95% CI 1.43–4.85), while no significant association was found in non-diabetic patients (HR 1.13, 95% CI 0.63–2.02). Restricted cubic splines demonstrated a linear relationship between SII and composite outcomes. SII showed superior prognostic accuracy compared to other inflammatory markers, and DCA confirmed improved clinical decision-making utility.
Conclusions
In patients with HFpEF, higher SII values were positively associated with risk of death and HF hospitalization, with particularly strong prognostic value in diabetic patients. SII represents a promising, accessible biomarker for enhanced risk stratification in HFpEF.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12967-025-06964-8.
Keywords: HFpEF, Systemic inflammation, SII, Risk prediction, Outcomes
Introduction
Heart failure (HF) and preserved ejection fraction (HFpEF) is a rapidly growing global epidemic, affecting approximately 10% of individuals aged 40 and older and significantly increasing the public health burden [1]. HFpEF patients are characterized by elevated left ventricular filling pressure and myocardial stiffness [2]. Over 50% of HFpEF patients face a poor prognosis due to high rates of HF readmissions and mortality, further compounded by the limited availability of targeted therapies, owing to its diverse etiology and complex pathophysiology [3, 4]. Therefore, it is essential to develop strategies for identifying those individuals most vulnerable within this patient group to prevent further health deterioration and improve overall quality of life.
HFpEF is associated with obesity, diabetes, atrial fibrillation, and vascular stiffness, with systemic inflammation contributing to adverse cardiac remodeling and playing a key role in its pathogenesis [5]. Although inflammation is involved in the progression of all HF subtypes, inflammatory markers show a particularly strong association in HFpEF [6–8]. Patients with HFpEF often exhibit elevated levels of circulating neutrophils, lymphocytes, platelets, and other inflammatory biomarkers, which are independent predictors of increased cardiovascular risk [8–11]. Diabetes represents a particularly high-risk phenotype in HFpEF, characterized by both a pronounced inflammatory burden and worse clinical outcomes [12]. Several novel systemic inflammatory biomarkers have been identified, offering valuable insights into adverse risks related to HFpEF, with growing interest in simple, cost-effective, and reliable indicators. Recently, the systemic immune-inflammation index (SII), a new and cost-effective marker based on neutrophils, lymphocytes, and platelets, has gained attention as a promising biomarker for assessing risk across various conditions, including cardiovascular diseases [13–16]. In addition, a higher SII is independently linked to asymptomatic individuals with metabolic syndrome and diabetes who are considered at risk for HFpEF [17–19].
Nevertheless, data on the SII and its specific influence on long-term outcomes in HFpEF are lacking, and its role in HFpEF remains unclear. To address this gap, we aimed to investigate the prognostic role of SII in HFpEF patients and explore its association with long-term clinical outcomes.
Materials and methods
Study population and data collection
The investigation was an observational-retrospective study that recorded data from participants who were referred to the Cardiology Department at Shanghai Tenth Peoples Hospital. We selected individuals diagnosed with HFpEF undergoing coronary angiography from 2015 to 2023, following the HFpEF diagnostic criteria outlined by European Society of Cardiology [2]. The clinical diagnosis of acute HF, in conjunction with the requirements: briefly, (1) a left ventricular ejection fraction (LVEF) measuring 50% or more, (2) at least one key objective indicator of HF, such as elevated N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels or the detection of cardiac dysfunction or structural irregularities through echocardiographic imaging. The exclusion criteria included individuals under 18 years of age, individuals with severe hepatic or kidney disorders, severe valve disease, a recent history of myocardial infarction, pulmonary conditions, or obstructive coronary artery disease (stenosis > 50% observed on angiography). Additionally, individuals lacking SII data and follow-up information were not included in the study. The study adhered to the principles outlined in the Declaration of Helsinki. Approval for the study is obtained from the Ethics Committee of Shanghai Tenth Peoples Hospital (No. 24k142) (ClinicalTrials.gov, NCT06228807).
Baseline patient characteristics were gathered from the hospital’s electronic medical record system for analysis, including data on age, sex, smoking habits, medical history, and echocardiographic and laboratory test results collected upon admission. Cardiologists conducted a complete echocardiographic examination, incorporating tissue Doppler imaging, in accordance with established guidelines [20]. Standard laboratory parameters, including NT-proBNP, creatinine, fasting glucose, complete blood count (including hemoglobin, neutrophil, lymphocyte, platelet counts), C-reactive protein, and additional biomarkers, were measured using samples of venous blood taken during the first 24 h of hospitalization, after 8 h of fasting. The complete blood count was processed at a central laboratory employing Cell-Dyn 3700 analyzer (Abbott Diagnostics, USA).
SII definition
SII was calculated as the platelet x neutrophil-lymphocyte ratio. The study population was then categorized into 3 sub-groups based on SII tertiles: Tertile 1 with SII ≤ 455.2, Tertile 2 with 455.2 > SII ≤ 777.8, and Tertile 3 with SII > 777.8.
Outcome
Participants were followed for a mean period of 41.8 months to assess a combined outcome that included all-cause mortality (deaths from any cause, including cardiovascular) and HF rehospitalization. Two cardiology specialists, who were unaware of the participants’ medical and diagnostic information, assessed survival status by documenting follow-up information, reviewing medical case files, and conducting interviews with patients or their family members.
Statistical analysis
The statistical analyses in this study incorporated mean ± standard deviation for continuous variables and percentages for categorical variables. Baseline comparisons of differences between the SII tertiles were assessed by analysis of variance (One-Way ANOVA) for continuous variables. Categorical variables were displayed according to SII tertiles as percentages and was assessed using χ2 test. Post-hoc power analysis was tested using G*Power 3.1.9.7. Kaplan-Meier analysis was used to evaluate the event-free survival rate across the SII tertiles, and the differences assessed using the log-rank test. Cox analysis was used to calculate estimated hazard risk ratio (HR) along with its corresponding 95% confidence interval (CI) for composite outcomes. Collinearity was assessed using the variance inflation factor (VIF), with results provided in Supplemental Table S1. The variables were selected across models based on their clinical relevance, pathophysiological rationale, and prior evidence of prognostic value in HFpEF. Model 1 was unadjusted. Model 2 was adjusted for age and sex, Model 3 was adjusted for Model 2 variables plus diabetes, chronic kidney disease (CKD), heart rate, diastolic blood pressure, NT-proBNP, creatinine, total cholesterol, high-density lipoprotein cholesterol (HDL), HbA1c, mean septal diastolic velocity (E/e’), left ventricular end-diastolic dimension (LVEDD), and LVEF. Restricted cubic spline (RCS) regression was performed to evaluate potential nonlinear relationships between SII and the risk of adverse events, with adjustments for age, sex, diabetes, CKD, heart rate, diastolic blood pressure, NT-proBNP, creatinine, total cholesterol, HDL, HbA1c, E/e’, LVEDD, and LVEF. The predictability of the SII, along with neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), in predicting composite clinical events was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). The proportional hazards assumption was assessed using the Schoenfeld residuals test. Significance is set at p < 0.05 with two-sided testing. The figures were created with GraphPad (v. 8.0.1), and R (version 4.0.3). Data analysis was performed using SPSS (v. 24.0).
Results
Between 2015 and 2023, a total of 584 participants with HFpEF were admitted to our site. Participants who met exclusion criteria, lost to follow-up, or had missing SII data were excluded from the final analysis (Supplemental Fig S1, Flowchart). Ultimately, 458 HFpEF participants, with a mean follow-up of 41.8 months, were included in the study data. The majority of participants were women (59.2%), with a mean age of 70.7 years and a mean BMI of 25.0. NYHA class III/IV was present in 230 participants (50.2%).
Baseline characteristics according to SII groups
The baseline clinical characteristics and laboratory measurements for the HFpEF population, categorized by SII tertiles, reveal that patients with the highest SII levels tend to be older, have higher BMI, a greater proportion of NYHA class III/IV, and an increased prevalence of comorbidities, including anemia, diabetes, dyslipidemia and CKD (all p < 0.05). Laboratory tests show significant differences across SII tertiles, with those in the highest SII tertile exhibiting elevated levels of fasting blood glucose (FBG), HbA1c, NT-proBNP, total cholesterol, and creatinine (all p < 0.05). In contrast, hemoglobin and eGFR levels were higher in the lower SII tertiles (all p < 0.05). Inflammatory markers, including C-reactive protein and neutrophil count, were significantly elevated in the highest tertile (all p < 0.05), while lymphocyte counts were decreased (p < 0.001). Platelet counts were also significantly higher in the top tertile (p < 0.001). Additionally, echocardiography results showed that patients in the highest SII tertile had significantly higher E/e’ ratio (15.2 ± 3.9 vs. 13.8 ± 3.9 and 14.7 ± 3.7; p = 0.005) and lower e’ velocity (6.5 ± 1.3 vs. 7.0 ± 1.2 and 6.8 ± 1.6 and; p = 0.003) and LVEF. While LAVI, LVEDD, and PASP did not differ significantly. No significant differences were observed in the medication data between the groups (Table 1).
Table 1.
Baseline clinical characteristics stratified by SII groups
| Overall | Tertile 1 | Tertile 2 | Tertile 3 | P-value | |
|---|---|---|---|---|---|
| (n = 458) | (n = 152) SII ≤ 455.2 |
(n = 154) 455.2 > SII ≤ 777.8 |
(n = 152) SII > 777.8 |
||
| Demographics | |||||
| Female, n (%) | 271 (59.2) | 99 (65.1) | 84 (54.5) | 88 (57.9) | 0.157 |
| NYHA III-IV | 230 (50.2) | 63 (41.4) | 80 (51.9) | 87 (57.2) | 0.020 |
| Age, years | 70.7 ± 7.5 | 69.8 ± 7.6 | 70.1 ± 7.5 | 72.2 ± 7.3 | 0.012 |
| BMI, kg/m2 | 25.0 ± 3.4 | 24.3 ± 3.2 | 25.2 ± 3.4 | 25.6 ± 3.5 | 0.004 |
| Heart rate | 80.3 ± 16.9 | 78.6 ± 16.4 | 81.5 ± 18.1 | 80.7 ± 16.1 | 0.295 |
| Systolic blood pressure, mmHg | 140.3 ± 23.0 | 137.5 ± 20.3 | 141.5 ± 24.2 | 141.8 ± 24.1 | 0.194 |
| Diastolic blood pressure, mmHg | 77.5 ± 13.3 | 77.3 ± 11.2 | 77.9 ± 15.0 | 77.1 ± 13.5 | 0.844 |
| Smoking status, n (%) | 95 (20.7) | 29 (19.1) | 33 (21.4) | 33 (21.7) | 0.824 |
| Drinking | 51 (11.1) | 16 (10.5) | 18 (11.7) | 17 (11.2) | 0.949 |
| Comorbidities | |||||
| Atrial fibrillation | 110 (24.0) | 37 (24.3) | 32 (20.8) | 41 (27.0) | 0.445 |
| Diabetes | 193 (42.1) | 47 (30.9) | 62 (40.3) | 84 (55.3) | < 0.001 |
| Hypertension | 339 (74.0) | 106 (69.7) | 113 (73.4) | 120 (78.9) | 0.182 |
| Dyslipidemia | 161 (35.2) | 45 (29.6) | 49 (31.8) | 67 (44.1) | 0.017 |
| CKD | 64 (14.0) | 12 (7.9) | 24 (15.6) | 28 (18.4) | 0.023 |
| Anemia | 179 (39.1) | 44 (28.9) | 63 (40.9) | 72 (47.4) | 0.004 |
| Laboratory data | |||||
| FBG, mmol/L | 6.7 ± 2.5 | 6.1 ± 1.8 | 6.7 ± 2.4 | 7.3 ± 2.9 | < 0.001 |
| Hb1AC, % | 6.6 ± 1.3 | 6.4 ± 1.0 | 6.6 ± 1.3 | 6.8 ± 1.4 | 0.018 |
| Hemoglobin, g/dL | 126.8 ± 19.7 | 128.7 ± 15.3 | 128.2 ± 21.4 | 123.6 ± 21.5 | 0.048 |
| Total Cholesterol, mmol/L | 3.9 ± 1.0 | 3.9 ± 1.0 | 3.7 ± 0.9 | 4.1 ± 1.1 | 0.028 |
| HDL, mmol/L | 1.42 ± 5.9 | 2.04 ± 10.2 | 1.11 ± 0.34 | 1.11 ± 0.39 | 0.324 |
| NTproBNP, pg/mL | 1359.4 ± 1435.5 | 1036.5 ± 1005.5 | 1179.0 ± 1168.4 | 1860.7 ± 1853.7 | < 0.001 |
| eGFR | 72.0 ± 21.9 | 76.0 ± 19.3 | 72.0 ± 22.5 | 67.9 ± 23.3 | 0.006 |
| C-reactive protein, mg/dL | 6.8 ± 5.4 | 5.8 ± 4.8 | 6.2 ± 5.3 | 8.5 ± 5.7 | < 0.001 |
| Creatinine, umol/L | 92.9 ± 68.5 | 80.1 ± 29.0 | 89.5 ± 41.2 | 108.2 ± 106.1 | 0.002 |
| Lymphocytes, *103cells/ml | 1.5 ± 0.6 | 1.8 ± 0.6 | 1.5 ± 0.5 | 1.2 ± 0.4 | < 0.001 |
| Neutrophils, *103cells/ml | 4.8 ± 2.0 | 3.2 ± 1.1 | 4.6 ± 1.3 | 6.5 ± 2.1 | < 0.001 |
| Platelet count | 201.5 ± 66.0 | 164.9 ± 47.7 | 204.3 ± 55.4 | 235.4 ± 72.7 | < 0.001 |
| Echocardiography data | |||||
| LAVI | 42.5 ± 6.5 | 41.8 ± 6.2 | 42.6 ± 6.5 | 43.2 ± 6.7 | 0.169 |
| LVEF, % | 60.7 ± 4.9 | 61.6 ± 5.0 | 60.1 ± 4.3 | 60.3 ± 5.1 | 0.018 |
| E/e’ | 14.6 ± 3.9 | 13.8 ± 3.9 | 14.7 ± 3.7 | 15.2 ± 3.9 | 0.005 |
| e’ | 6.8 ± 1.4 | 7.0 ± 1.2 | 6.8 ± 1.6 | 6.5 ± 1.3 | 0.003 |
| LVEDD | 47.1 ± 5.6 | 47.1 ± 6.3 | 47.8 ± 4.8 | 46.4 ± 5.7 | 0.095 |
| PASP | 38.8 ± 8.3 | 38.1 ± 7.9 | 38.3 ± 6.8 | 40.0 ± 9.7 | 0.115 |
| LVESD | 31.5 ± 6.6 | 31.4 ± 6.6 | 31.9 ± 5.9 | 31.3 ± 7.2 | 0.699 |
| Medications | |||||
| Diuretics | 235 (51.3) | 75 (49.3) | 81 (52.6) | 79 (52.0) | 0.833 |
| Statins | 385 (84.1) | 132 (86.8) | 129 (83.8) | 124 (81.6) | 0.452 |
| ACE inhibitors/ARB | 230 (50.2) | 78 (51.3) | 78 (50.6) | 74 (48.7) | 0.892 |
| Calcium channel blockers | 142 (31.0) | 47 (30.9) | 45 (29.2) | 50 (32.9) | 0.785 |
| MCRA | 151 (33.0) | 54 (35.5) | 53 (34.4) | 44 (28.9) | 0.426 |
| Beta-blockers | 276 (60.3) | 85 (55.9) | 97 (63.0) | 94 (61.8) | 0.400 |
NYHA: New York heart association class, BMI: body mass index, CKD: chronic kidney disease, FBS: fasting blood glucose, Hb1AC: glycated hemoglobin, eGFR: estimated glomerular filtration rate, NTproBNP: HDL: high-density cholesterol: N-terminal pro-B-type natriuretic peptide, LAVI: left atrial volume index, LVEF: left ventricular ejection fraction, E/e: mean septal diastolic velocity, e’: peak LV velocity, LVEDD: left ventricular end-diastolic dimension, PASP: pulmonary artery systolic pressure, LVESD: left ventricular end-systolic dimension, ACE inhibitors/ARB: angiotensin-converting enzyme inhibitor/ angiotensin receptor blocker, MCRA: mineralocorticoid receptor antagonists, SII: systemic immune-inflammation index
Composite outcome according to SII groups
The clinical outcomes of the present study include all-cause mortality, cardiovascular mortality, and HF rehospitalization. During a 41-month follow-up time, a total of 211 events (46.1%) were documented, including 65 deaths (14.2%) due to cardiovascular causes and 81 deaths (17.7%) from all causes. Additionally, 130 patients (28.4%) were re-hospitalized for HF. Analysis across the SII tertiles revealed that the third tertile (Tertile 3) exhibited the highest rates of cardiovascular mortality 31 (20.4%) and all-cause mortality 41 (27.0%), while the first tertile (Tertile 1) demonstrated the lowest mortality rates, with 10 (6.6%) deaths. Although HF rehospitalization rates did not vary significantly among the tertiles, there was a discernible trend toward a higher risk of HF rehospitalization in Tertile 3. The event rate for composite outcomes for SII tertiles is displayed in Table 2.
Table 2.
Composite event rate according to SII groups
| Overall (n = 458) |
Tertile 1 (n = 152) |
Tertile 2 (n = 154) |
Tertile 3 (n = 152) |
P value | |
|---|---|---|---|---|---|
| Total events | 211 (46.1) | 52(34.2) | 67 (43.5) | 92(60.5) | < 0.001 |
| Cardiovascular mortality | 65 (14.2) | 9 (5.9) | 25 (16.2) | 31 (20.4) | 0.001 |
| All-cause mortality | 81 (17.7) | 10 (6.6) | 30 (19.5) | 41 (27.0) | < 0.001 |
| HF readmissions | 130 (28.4) | 42 (27.6) | 37 (24.0) | 51 (33.6) | 0.176 |
Sample size and power analysis
Post-hoc power analysis demonstrated 86.8% statistical power (α = 0.05, two-tailed) to detect the observed difference in composite event rates between SII tertiles (34.2% vs. 60.5%), confirming adequate study power for the primary analysis.
Association of SII with long-term survival in HFpEF population
A Kaplan-Meier analysis was performed to examine the relationship between SII and the long-term survival in the HFpEF population. Figure 1 illustrates Kaplan-Meier curves depicting survival disparities across SII tertiles among HFpEF patients, demonstrating that the highest SII levels correlate with poorer clinical outcomes. On individual analysis, we observed that patients in the highest SII tertile (Tertile 3) exhibit a significantly greater risk of composite adverse events compared to those in lower tertiles (Log-rank p < 0.001) (A). All-cause and cardiovascular-related deaths were notably higher in Tertile 3, with significantly lower survival relative to Tertiles 1 and 2 (B) and (C), respectively (all log-rank p < 0.05). Additionally, survival free from HF hospitalization was also significantly impaired in the highest SII tertile (Log-rank p = 0.033) (D).
Fig. 1.
A Kaplan-Meier survival curves for composite events stratified by SII tertiles; B All-cause mortality; C Cardiovascular mortality; D HF rehospitalization. SII: systemic immune-inflammation index
Prognostic impact of SII on outcomes in overall, diabetic, and non-diabetic HFpEF patients
The Cox regression analysis results across all models are presented in Table 3. When analyzed as a continuous variable, higher log-transformed SII was associated with increased risk of composite outcome of death and HF rehospitalization in unadjusted analysis (Model 1) for the overall cohort (HR 1.82, 95% CI 1.46–2.26, p < 0.001), diabetic patients (HR 2.07, 95% CI 1.52–2.81, p < 0.001), and non-diabetic patients (HR 1.47, 95% CI 1.07–2.02, p = 0.019). After full multivariable adjustment (Model 3), these associations remained significant in the overall cohort (HR 1.50, 95% CI 1.18–1.92, p = 0.001) and in diabetic patients (HR 1.88, 95% CI 1.32–2.67, p < 0.001), but became non-significant in non-diabetic patients (HR 1.23, 95% CI 0.86–1.77, p = 0.251). In tertile-based analysis, the highest SII tertile was associated with increased risk in unadjusted analysis (Model 1) for the overall cohort (HR 2.22, 95% CI 1.58–3.13, p < 0.001) and remained significant after full multivariable adjustment (Model 3) (HR 1.70, 95% CI 1.15–2.52, p = 0.008). Importantly, the highest SII tertile conferred substantially increased risk of long-term outcomes in diabetic patients in both unadjusted (Model 1) (HR 3.40, 95% CI 1.93–5.98, p < 0.001) and fully adjusted analyses (Model 3) (HR 2.63, 95% CI 1.43–4.85, p = 0.002), whereas no significant association was observed in non-diabetic patients in either analysis (adjusted HR 1.13, 95% CI 0.63–2.02, p = 0.690). The prognostic effect of SII differed significantly by diabetes status (p-interaction = 0.038). The proportional hazards assumption was assessed using Schoenfeld residuals for each covariate and globally. No significant violations were observed (global test p = 0.313). Schoenfeld residual plots are provided in the Appendix.
Table 3.
Association between SII and primary outcomes in overall, diabetic and non-diabetic HFpEF patients
| Events (%) | Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|---|
| HR (95% CI) | P-value | HR (95% CI) | P-value | HR (95% CI) | P-value | ||
| Overall cohort | |||||||
| Log SII (Cont.) | 1.82 (1.46–2.26) | < 0.001 | 1.76 (1.41–2.19) | < 0.001 | 1.50 (1.18–1.92) | 0.001 | |
| SII1 | 52 (34.2) | Reference | Reference | Reference | |||
| SII2 | 67 (43.5) | 1.30 (0.91–1.87) | 0.156 | 1.27 (0.88–1.83) | 0.197 | 1.26 (0.85–1.88) | 0.249 |
| SII3 | 92 (60.5) | 2.22 (1.58–3.13) | < 0.001 | 2.12 (1.51–2.99) | < 0.001 | 1.70 (1.15–2.52) | 0.008 |
| With diabetes | |||||||
| Log SII (Cont.) | 2.07 (1.52–2.81) | < 0.001 | 2.06(1.49–2.85) | < 0.001 | 1.88 (1.32–2.67) | < 0.001 | |
| SII1 | 15 (31.9) | Reference | Reference | ||||
| SII2 | 30 (48.4) | 1.83 (0.98–3.40) | 0.056 | 1.60 (0.85–3.02) | 0.143 | 1.62 (0.83–3.14) | 0.157 |
| SII3 | 63 (75.0) | 3.40 (1.93–5.98) | < 0.001 | 3.25 (1.84–5.74) | < 0.001 | 2.63 (1.43–4.85) | 0.002 |
| Without diabete | |||||||
| Log SII (Cont.) | 1.47 (1.07–2.02) | 0.019 | 1.39 (1.01–1.92) | 0.042 | 1.23 (0.86–1.77) | 0.251 | |
| SII1 | 37 (35.2) | Reference | Reference | ||||
| SII2 | 37 (40.2) | 1.08 (0.68–1.70) | 0.750 | 1.08 (0.69–1.71) | 0.726 | 1.12 (0.66–1.89) | 0.675 |
| SII3 | 29 (42.6) | 1.42 (0.87–2.31) | 0.160 | 1.32 (0.80–2.15) | 0.276 | 1.13 (0.63–2.02) | 0.690 |
Model 1: Unadjusted model
Model 2: Adjusted for age and sex
Model 3: Adjusted for Model 2 + diabetes, CKD, heart rate, diastolic blood pressure, NTproBNP, creatinine, total cholesterol, HDL, HbA1c, E/e, LVEDD, and LVEF
CKD: chronic kidney disease, NTproBNP: N-terminal pro-B-type natriuretic peptide, HDL: high-density cholesterol, Hb1AC: glycated hemoglobin, LVEF: left ventricular ejection fraction, E/e: mean septal diastolic velocity, LVEDD: left ventricular end-diastolic dimension, SII: systemic immune-inflammation index, HR: hazard ratio, CI: confidence interval
The RCS analysis revealed a significant association between higher log-transformed SII and increased composite risk in the fully adjusted models (P = 0.001). Notably, the analysis showed no evidence of nonlinearity (p for nonlinearity = 0.145) (Fig. 2).
Fig. 2.
Restricted spline curves illustrating the associations of SII with composite outcomes among HFpEF cohort. Log-SII: Log transformed systemic immune-inflammation index, CI: confidence interval
To further explore the impact of diabetes on the relationship between SII and clinical outcomes, we performed a Kaplan-Meier analysis with patients categorized into six groups based on SII tertiles and diabetes status: Tertile 1 and Non-Diabetes, Tertile 1 and Diabetes, Tertile 2 and Non-Diabetes, Tertile 2 and Diabetes, Tertile 3 and Non-Diabetes, and Tertile 3 and Diabetes. We found that the Tertile 3 and Diabetes group exhibited significantly poorer survival outcomes than other groups (Log-rank p < 0.001) (Fig. 3).
Fig. 3.
Kaplan-Meier survival curves for composite events based on diabetes status. SII: systemic immune-inflammation index
Predictive value of SII in composite event prediction
We further evaluated the predictive value of the SII in predicting composite events, alongside the NLR and PLR. The results revealed that SII (AUC: 0.6404, 95% CI: 0.5897–0.6912, P < 0.001) exhibited superior discriminative power compared to both NLR (AUC: 0.6247, 95% CI: 0.5735–0.6760, P < 0.001) and PLR (AUC: 0.6241, 95% CI: 0.5730–0.6753, P < 0.001) in predicting composite events. (Supplemental Fig. S2, ROC curves). Model calibration was assessed using the Hosmer-Lemeshow goodness-of-fit statistic (χ² = 10.68, df = 8, p = 0.220), demonstrating good calibration with no significant deviation between predicted and observed risk. Furthermore, decision curve analysis (DCA) demonstrated that the SII-enhanced model provided superior net benefit compared to the basic clinical model across threshold probabilities of 40–80%. This indicates that incorporating SII into risk assessment improves clinical decision-making for patients with intermediate to high baseline risk of composite outcomes (Fig. 4).
Fig. 4.
Decision curve analysis comparing SII-enhanced and basic clinical models for composite event prediction. The SII-enhanced model (blue line) demonstrates superior net benefit compared to the basic clinical model (green line) across threshold probabilities of 40–80%. SII: systemic immune-inflammation index
Additionally, we assessed the distribution of the MAGGIC risk score across SII tertiles. We observed that patients in tertile 3 had a significantly higher MAGGIC risk score compared to those in the lower tertiles (24.6 vs. 20.8 and 22.4, p < 0.001) (Supplemental Fig. S3, MAGGIC score across SII tertiles).
Discussion
Here, we demonstrated for the first time that the SII independently predicts adverse outcomes in HFpEF patients, with particularly robust prognostic value in diabetic patients. Over 41.8 months of follow-up, patients in the highest SII tertile showed significantly increased risk for the composite endpoint of death and HF rehospitalization, with this association remaining significant after comprehensive multivariable adjustment. Most importantly, SII demonstrated amplified prognostic strength in diabetic HFpEF patients who represent a high-risk phenotype with substantial inflammatory burden and poor outcomes, suggesting its potential as a cost-effective and clinically meaningful biomarker for risk stratification in this high-risk phenotype.
HFpEF affects millions globally and is characterized by substantial cardiometabolic comorbidity burden [1, 2], with composite mortality and rehospitalization rates ranging from 19 to 29% at one year to 75% over five years [3, 4, 21]. Our observed 46.1% composite event rate aligns with this established burden, yet current risk stratification tools inadequately capture the inflammatory heterogeneity that drives variable outcomes in this population. These findings underscore the clinical need for affordable and accessible inflammatory biomarkers that can enhance prognostic accuracy in HFpEF management.
Our findings reveal that SII demonstrates particularly strong prognostic value in diabetic HFpEF patients, representing a critical advance in risk stratification for this high-risk subgroup. Diabetes in HFpEF exacerbates systemic inflammation, promotes myocardial stiffness, and amplifies cardiovascular risk through multiple pathophysiological mechanisms [5, 12]. Consistent with this mechanistic rationale, we found that higher SII levels were associated with significantly worse outcomes in diabetic HFpEF patients compared to non-diabetics, with the diabetic subgroup showing the lowest survival rates. This observation aligns with emerging evidence from other cardiovascular populations where higher SII and diabetes revealed lowest survival outcomes [18, 22]. In a large acute myocardial infarction cohort, elevated SII independently predicted long-term mortality, with the strongest association observed in diabetic patients [16]. Similarly, CAD patients with elevated SII and diabetes demonstrated the greatest risk for adverse outcomes [23]. Our study extends these findings specifically to the HFpEF population, confirming that the combination of elevated SII and diabetes identifies patients at particularly high risk for death and rehospitalization. This reinforces SII’s prognostic value and highlights diabetes as a key comorbidity needing focus.
Systemic inflammation represents a central pathophysiological pathway in HFpEF, where comorbidities trigger inflammatory cascades driving disease severity and adverse outcomes [24]. This inflammatory milieu is reflected in elevated levels of circulating biomarkers, including CRP, NLR, and PLR in line with the comorbidity burden and may predict poor clinical outcomes [9, 25]. The mechanistic basis for this enhanced prognostic value likely relates to diabetes-mediated amplification of inflammatory cascades. Diabetic HFpEF patients exhibit heightened systemic inflammation, which triggers coronary microvascular dysfunction, endothelial inflammation, and ultimately myocardial stiffening and fibrosis, the hallmark features of HFpEF [5, 26]. SII captures this inflammatory burden through its integration of neutrophils and platelets (which drive cardiac inflammation and fibrosis) and lymphocytes (whose dysfunction contributes to impaired immune regulation and tissue injury) [14, 27–30]. The comprehensive inflammatory assessment provided by SII may explain why it outperformed individual inflammatory markers like NLR and PLR in our analysis and previous research [14, 31]. SII offers significant practical advantages for clinical implementation as a risk stratification tool in HFpEF. Unlike complex inflammatory biomarkers such as TNF-α, TGF-β, cell adhesion molecules, or soluble ST2 [8], which are expensive and not routinely available, SII is derived from standard complete blood count parameters accessible in any clinical setting [14]. The accessibility of SII makes it a potentially valuable tool for widespread implementation in HFpEF risk assessment.
Our analysis demonstrates that SII provides clinically meaningful discriminative ability for identifying high-risk patients. With an AUC of 0.64 and positive decision curve analysis showing net benefit in the 40–80% risk threshold range, SII demonstrates particular utility for risk stratification in patients where treatment decisions are most challenging - those with intermediate to high baseline risk. Importantly, our restricted cubic spline analysis revealed a linear relationship between SII and composite outcomes, indicating that risk increases proportionally across the entire SII spectrum without apparent threshold effects. This linear association simplifies clinical interpretation by allowing SII to be used as a continuous variable, enhancing its practical utility in clinical decision-making. Incorporating SII into routine risk assessment could improve patient outcomes by identifying those who would benefit from more intensive monitoring or targeted interventions.
Furthermore, elevated SII is associated with individuals diagnosed with diabetes, CKD, obesity, and metabolic syndrome, which are common risk factors for developing HFpEF [17, 22, 32]. These factors may drive and amplify inflammation as indicated by elevated SII, contributing to the structural and functional imbalance characteristic of HFpEF. Our findings show that patients in the highest SII tertile exhibited significantly worse baseline characteristics, including greater comorbidity burden (diabetes, dyslipidemia, anemia, CKD), poorer functional status (NYHA class), adverse laboratory profiles (hemoglobin, FBG, eGFR, CRP), and impaired cardiac structure and function (E/e’, e’, NTproBNP). This pattern suggests that SII serves as a valuable integrative marker of disease severity and inflammatory burden in HFpEF. The correlation with established risk scores like MAGGIC further validates SII’s utility as a complementary risk stratification tool.
The potential clinical applications of SII extend beyond risk stratification. As anti-inflammatory therapies continue to be investigated in HFpEF, following mixed results from trials like D-HART and TOPCAT [33, 34], SII may help identify patients most likely to benefit from such interventions. An important future research question is whether stratifying treatment decisions by SII levels could optimize therapeutic outcomes in HFpEF patients. While these findings demonstrate SII’s potential clinical utility, several factors underscore the critical need for external validation before clinical implementation.
Several limitations are associated with our study. The assessment of SII at only one time point represents a key limitation, as inflammatory status may change over time due to disease progression, therapeutic interventions, or intercurrent illnesses. Longitudinal studies with repeated SII measurements will be essential to validate the temporal stability of SII’s prognostic value and establish optimal monitoring strategies. Our retrospective design precludes establishing causal relationships between systemic inflammation and prognosis, and despite comprehensive multivariable adjustment, unmeasured confounding cannot be entirely excluded. The single-center nature of our study with modest sample size further limits generalizability to broader HFpEF populations. Importantly, lack of comprehensive medication adjustment in our final model, particularly novel HFpEF therapies such as SGLT2 inhibitors, which were not widely prescribed during our study period. Additionally, immunomodulatory medications (colchicine, methotrexate, corticosteroids) that may directly influence systemic inflammation were not systematically recorded. These unmeasured confounders may affect both SII levels and clinical outcomes, potentially influencing the observed associations. Future studies incorporating contemporary HFpEF pharmacotherapy and systematic anti-inflammatory medication data will be essential to validate SII’s prognostic utility in current clinical practice. Future validation studies should include larger, multicenter cohorts with diverse patient populations to establish SII’s generalizability across different HFpEF phenotypes and healthcare settings. These studies must also establish optimal SII cutoff values for clinical decision-making and determine how SII performs when integrated with existing risk assessment tools and contemporary HFpEF therapies.
Conclusions
SII represents a promising, accessible biomarker for risk stratification in HFpEF, with particularly robust prognostic value in diabetic patients who face the highest risk burden. Its derivation from routine laboratory parameters positions it as a potentially valuable addition to current risk assessment strategies. However, rigorous external validation in diverse, prospective cohorts is essential before clinical implementation to ensure reproducibility and establish evidence-based clinical applications.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The authors thank the study coordinators and participants for their support.
Abbreviations
- SII
Systemic inflammatory index
- HF
Heart failure
- HFpEF
Heart failure and preserved ejection fraction
- AF
Atrial fibrillation
- CKD
Chronic kidney disease
- NYHA
New York Heart Association
- BMI
Body mass index
- LVEDD
Left ventricular end-diastolic diameter
- e’
Peak LV velocity
- E/e’
Mean septal velocity
- LAVI
Left atrial volume index
- LVEF
Left ventricular ejection fractions
- PASP
Pulmonary artery systolic pressure
- NT-proBNP
N-terminal pro-B-type natriuretic peptide
- NLR
Neutrophil-to-lymphocyte ratio
- PLR
Platelet-to-lymphocyte ratio
- CRP
C-reactive protein
- eGFR
Estimated glomerular filtration rate
- FBG
Fasting blood glucose
- HbA1c
Glycated hemoglobin
Appendix. Schoenfeld residual plots for Cox model covariates
The following plots illustrate the Schoenfeld residuals to assess the proportional hazards assumption.
Appendix: Schoenfeld residual plots for key covariates included in the multivariable Cox regression model. The plots assess the proportional hazards assumption over time. The global test for proportionality yielded p = 0.313, indicating no violation of the proportional hazards assumption.
Author contributions
A.Q. and F.A. were responsible for the study’s design. A.Q., Y.L., Y.C., and J.A. were tasked with gathering the research data. The cleaning, follow-up, and verification of the data were managed by R.M., S.Y, W.Z., L.L., and G.Y. The manuscript was written by A.Q. and F.A., with substantial intellectual contributions from others. The final version of the manuscript was approved by W.C., F.A., and X.W. All authors actively participated in the revision process, thoroughly reviewed the manuscript, and gave their consent for its final submission.
Funding
This work was supported by in part by Chinese National Natural Science Foundation (82170521), Shanghai Natural Science Foundation of China (21ZR1449500), Foundation of Shanghai Municipal Health Commission (202140263), Tibet Natural Science Foundation of China (XZ2022ZR-ZY27(Z), XZ202301ZR0032G), Foundation of Chongming (CKY2021-21, CKY2020-29), Clinical Research Plan of Shanghai Tenth People’s Hospital (YNCR2A001), Clinical Research Plan of SHDC (SHDC2020CR4065), and Foundation of the Science and Technology Commission of Shanghai Municipality (Grant No.20dz1207200).
Data availability
The authors are willing to share the raw data that underpins the conclusions of this article upon request, with no limitations.
Declarations
Ethics approval and consent to participate
The approval for the study is obtained from the Ethics Committee of Shanghai Tenth Peoples Hospital, and all participants had provided written informed consent.
Consent for publication
Informed consent was obtained from all participants for publication.
Competing interests
The authors have no competing interests to declare.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Fuad A. Abdu, Email: 1691026@tongji.edu.cn
Wenliang Che, Email: chewenliang@tongji.edu.cn.
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Supplementary Materials
Data Availability Statement
The authors are willing to share the raw data that underpins the conclusions of this article upon request, with no limitations.