Abstract
Background
Although systemic immune-inflammation index (SII) and the systemic inflammation response index (SIRI) have garnered attention as novel inflammatory response markers in various clinical contexts, their application in patients with ST-segment elevation myocardial infarction (STEMI) following percutaneous coronary intervention (PCI) remains unclear. This study aims to fill this gap by investigating and validating the relationships between SII, SIRI, and the risks for all-cause mortality and major adverse cardiovascular events (MACEs) in this specific patient population.
Methods
1222 participants diagnosed with STEMI and who underwent urgent PCI were included in the study. Cox proportional hazards regression analyses were employed to analyze the association between log10 SII, SIRI, and the occurrence of all-cause mortality and MACEs. Receiver operating characteristic (ROC) curves were used to compare the predictive values of log10 SII and SIRI for all-cause mortality and MACEs. Restricted cubic spline (RCS) curves were performed to explore the non-linear relationships between log10 SII, SIRI and all-cause mortality, MACEs. Additionally, subgroup analyses were conducted to further investigate the distribution characteristics of log10 SII and SIRI across different participant groups.
Results
After a 1-year follow-up, 106 experienced all-cause mortality, and 163 experienced MACEs. Higher levels of log10 SII and SIRI were independent risk factors for all-cause mortality (log10 SII: hazard ratio [HR] = 8.994, 95% confidence interval [CI] 1.265–63.948, p = 0.028; SIRI: HR = 3.671, 95% CI 1.004–13.425, p = 0.049) after adjusting for covariates. The area under the curve (AUC) for log10 SII and SIRI combined with other variables was 0.781. Meanwhile, higher levels of log10 SII and SIRI were also independent risk factors for MACEs (log10 SII: HR = 6.465, 95% CI 1.356–30.821, p = 0.019; SIRI: HR = 4.739, 95% CI 1.778–12.632, p = 0.002) after adjusting for covariates. The AUC for log10 SII and SIRI combined with other variables was 0.694. The restricted cubic spline (RCS) curves indicated non-linear relationships between SIRI not log10 SII and all-cause mortality, MACEs.
Conclusions
Log10 SII and SIRI are positively associated with the occurrence of all-cause mortality and MACEs, with this correlation being more significant in the higher-level log10 SII and SIRI groups.
Supplementary Information
The online version contains supplementary material available at 10.1186/s40001-025-03493-4.
Keywords: Systemic immune-inflammation index, Systemic inflammation response index, ST-segment elevation myocardial infarction, All-cause mortality, Major adverse cardiovascular events
Background
Atherosclerotic diseases are the most common prevalent form of cardiovascular diseases (CVD), with their pathogenesis closely related to inflammation and lipid metabolism. These conditions are also influenced by factors such as smoking, dietary habits, nutritional status, blood pressure, and glucose levels [1]. Timely PCI has been proven to be a life-saving intervention for many patients with ischemic heart diseases, such as ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI). While major adverse cardiovascular events (MACEs), including recurrent myocardial infarction, non-fatal stroke, and cardiovascular death, remain a major concern, other serious complications such as malignant arrhythmias, papillary muscle rupture, and mechanical damage should not be overlooked [2]. Studies have shown that various inflammatory factors and pathways, including high-sensitivity C-reactive protein (hsCRP), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-17 (IL-17), interleukin-1β (IL-1β), and NOD-like receptor protein 3 (NLRP3), are positively associated with the occurrence and prognosis of cardiovascular diseases [3–5]. A meta-analysis by S. Kaptoge et al. indicated that the log-transformed standardized hsCRP is significantly associated with adjusted all-cause mortality [6]. Additional research has shown that inhibiting inflammatory factors such as IL-6 and TNF-α can significantly reduce the burden of atherosclerosis [7, 8]. Moreover, a randomized, double-blind, controlled trial on the IL-1β inhibitor canakinumab demonstrated that, compared to the placebo group, the canakinumab treatment group had a significantly reduced risk of recurrent cardiovascular events [9]. COLCOT and LoDoCo2 trials showed a similar conclusion. Both of them demonstrated that the risk of cardiovascular events was significantly lower among those who received 0.5 mg colchicine once daily than among those who received placebo [10, 11].
Novel inflammatory markers based on the combination of multiple blood cell counts—such as neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI)—have been used to reflect the inflammatory status in patients with cardiovascular diseases. The NLR has been shown to be associated with mortality in patients with hypertension, the severity of coronary artery lesions, coronary artery disease (CAD) prognosis, etc. [12, 13]. Building on this foundation, Bo Hu et al. and Qi Qi et al. introduced and applied the concepts of SII and SIRI based on NLR, to investigate the prognosis of hepatocellular carcinoma (HCC) after curative resection and pancreatic cancer after chemotherapy for the first time [14, 15]. Since then, SII and SIRI have been widely applied across a variety of diseases, including CVD, gastrointestinal disorders, and several types of tumors [16, 17].
Several studies have shown that both SII and SIRI are independent risk factors for CVD death and all-cause mortality in patients with CAD and its complications [18–21]. However, the relationships between SII, SIRI and the prognosis of patients who undergo emergency PCI diagnosed with STEMI remain unclear. The purpose of this study is to investigate the relationships between SII, SIRI, traditional markers such as hsCRP, cardiac troponin I (cTnI) and all-cause mortality as well as the subsequent adverse events in this specific patient population.
Methods
Study population
This study included a total of 1,222 participants who were admitted to the Sino-French Wuhan Ecological Demonstration city branch of Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology from October 2016 to March 2024. These participants who presented with acute chest pain were diagnosed with STEMI, and subsequently underwent urgent PCI. Then, they were followed up for a period of 1 year. After excluding 150 participants due to loss to follow-up, data were finally obtained from the 1072 participants. Before the analyses, we compared the baseline characteristics between the included population and those lost to follow-up, and the results showed that there were no significant differences in baseline characteristics between the included participants and those who were excluded (Figure S1).
Inclusion criteria were: The patients who were diagnosed with STEMI according to the AHA/ACC guidelines [22], and underwent urgent PCI. Exclusion criteria were: 1. The patients who were diagnosed with gout, polymyositis/dermatomyositis (PM/DM) or other connective tissue diseases; 2. The patients who had rheumatic heart disease, valvular heart disease or pulmonary heart disease; 3. The patients who had acute inflammatory diseases; 4. The patients who had a history of malignant tumors; 5. The patients with incomplete medical records. All patients signed the informed consent before being included in this study. This study complies with the Declaration of Helsinki and has been approved by the institutional ethics committees of Tongji Hospital.
Definition of SII and SIRI
The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) were calculated from peripheral neutrophil counts, lymphocyte counts, platelet counts and monocyte counts. Definitions: SII = neutrophil counts*platelet counts/lymphocyte counts. SIRI = neutrophil counts*monocyte counts/lymphocyte counts.
Baseline characteristics of the participants and endpoint events
In this study, we included the following variables that may be associated with all-cause mortality and MACEs in patients with STEMI. These variables include demographic data (gender, age), clinical features (the history of hypertension, diabetes, hyperlipidemia, etc.), laboratory parameters (N-terminal pro-B-type natriuretic peptide [NT-proBNP], alanine aminotransferase [AST], systolic blood pressure [SBP], low-density lipoprotein cholesterol [LDL-C], hsCRP, log10 SII, SIRI, etc.), cardiac and angiographic characteristics (number of diseased coronary vessels, Killip class, etc.), key medication use (statins, ACE/ARB inhibitors, βblockers, etc.). All of these characteristics were collected from medical record system established by Tongji Hospital (Wuhan, China). Endpoint events: 1. All-cause mortality occurring within the 1-year follow-up period; 2. MACEs occurring within the 1-year follow-up period. MACEs definition: cardiovascular death, non-fatal myocardial infraction and non-fatal stoke.
Statistical analysis
SPSS version 25.0, GraphPad Prism version 10.1.2 and R version 4.4.1 were used for the statistical analyses. Firstly, normality tests were performed on all continuous variables using SPSS 25.0 through the Shapiro–Wilk test. The results showed that the data included in this study did not follow a normal distribution. Therefore, we used the Wilcoxon rank-sum test for inter-group comparisons. The continuous data were expressed using the median (interquartile range [IQR]). Categorical variables were expressed as percentages (%) to represent the distribution of the participants, and inter-group comparisons were performed using the Chi-square test. Cox proportional hazards regression analyses were used to perform multivariate analyses of the study variables. When constructing the Cox regression models, we transformed the SII into a base-10 logarithmic scale (log10) to normalize the data distribution and to meet the assumptions of the Cox proportional hazards model. In addition to exploring log10 SII and SIRI as continuous variables, log10 SII and SIRI were also categorized into four quartiles (Q1, Q2, Q3, Q4) to convert them into categorical variables to investigate the differences between the high and low-quartile groups. To systematically assess the impact of potential confounding factors, we constructed 3 progressively adjusted models: Model 1: This model included only the primary exposure variables (log10 SII and SIRI) without any adjustments for covariates. Model 2 included adjustments for gender and age, in addition to the primary exposure variables. Building upon Model 2, Model 3 further adjusted for the history of hypertension, diabetes, hyperlipidemia, previous cerebral infarction, previous PCI, cTnI, NT-proBNP, AST, serum creatinine, hsCRP, SBP, triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), LDL-C, hemoglobin A1c (HbA1c), number of diseased coronary vessels, Killip class, thrombolysis in myocardial infarction (TIMI) flow after PCI, β-blocker, and diuretics (these variables may have potential impacts on the occurrence of events). Secondly, GraphPad Prism 10.1.2 was used for correlation analyses, performing the receiver operating characteristic (ROC) analyses and calculating the area under the curves (AUCs). In addition, R 4.4.1 was used to construct Kaplan–Meier (KM) curves and restricted cubic splines (RCS) through the R package ggplot2 and rcssci, perform subgroup analyses and interaction testing through the R package forestplot. In all statistical analyses, the significance level was set at α = 0.05, with results considered statistically significant when p < 0.05.
Results
Baseline characteristics’ differences in the study participants
Following the exclusion of participants lost to follow-up, a total of 1,072 participants were included in the analysis, comprising 820 males and 252 females. The median age of the participants was 61 years, with the 25th and the 75th percentiles at 53 and 69 years. During the 1-year follow-up period, 106 participants experienced all-cause death, 163 participants experienced MACEs.
In the group experiencing all-cause mortality, significant differences (p < 0.05) were observed in several variables compared to the survival group. These variables included gender, age, the history of hyperlipidemia, body mass index (BMI), cTnI, NT-proBNP, leucocyte counts, neutrophil counts, lymphocyte counts, hemoglobin (Hb), admission glucose levels, TG, AST, albumin (ALB), serum creatinine, estimated glomerular filtration rate (eGFR), hsCRP, erythrocyte sedimentation rate (ESR), log10 SII, SIRI, LVEF, Killip class, TIMI flow after PCI, the number of diseased coronary vessels, β-blocker and diuretics. It can also be observed that, compared to the survival group, the all-cause mortality group had an older age, a higher proportion of females, a higher proportion of no hyperlipidemia, more than 3 diseased coronary vessels, and higher levels of cTnI, NT-proBNP, AST, serum creatinine, hsCRP, ESR, log10 SII and SIRI. In contrast, the levels of BMI, Hb, TG, ALB and LVEF were lower in the all-cause mortality group.
In the MACEs group, significant differences were observed in several variables compared to the group without MACEs. These variables included gender, age, the history of hyperlipidemia, previous cerebral infarction, BMI, SBP, cTnI, NT-proBNP, leucocyte counts, neutrophil counts, lymphocyte counts, Hb, admission glucose levels, TG, AST, ALB, serum creatinine, eGFR, hsCRP, ESR, log10 SII, SIRI, LVEF, Killip class, TIMI flow after PCI, number of diseased coronary vessels, β-blocker, and diuretics. Compared to the group without MACEs, the group with MACEs had an older age, a higher proportion of females, a higher proportion with no hyperlipidemia and more than 3 diseased coronary vessels. Additionally, there were higher levels of cTnI, NT-proBNP, AST, serum creatinine, hsCRP, ESR, log10 SII and SIRI, lower levels of BMI, Hb, TG, ALB and LVEF as well. The baseline characteristics differences can be seen in Table 1.
Table 1.
Baseline characteristics between all-cause mortality/MACEs and no such event
| Total | All-cause mortality | No all-cause mortality | p-value | MACEs | No MACEs | p-value | |
|---|---|---|---|---|---|---|---|
| N = 1072 | N = 106 | N = 966 | N = 163 | N = 909 | |||
| Gender (male), (%) | 820 (76.5) | 69 (65.1) | 751 (77.7) | 0.004 | 113 (69.3) | 707 (77.8) | 0.019 |
| Hypertension, (%) | 638 (59.5) | 65 (61.3) | 573 (59.3) | 0.690 | 104 (63.8) | 534 (58.7) | 0.226 |
| Diabetes, (%) | 334 (31.2) | 40 (37.7) | 294 (30.4) | 0.123 | 59 (36.2) | 275 (30.3) | 0.131 |
| Hyperlipidemia, (%) | 492 (45.9) | 34 (32.1) | 458 (47.4) | 0.003 | 48 (29.4) | 444 (48.8) | < 0.0001 |
| Previous cerebral infarction, (%) | 98 (9.1) | 13 (12.3) | 85 (8.8) | 0.240 | 23 (14.1) | 75 (8.3) | 0.017 |
| Previous PCI, (%) | 77 (7.2) | 9 (8.5) | 68 (7) | 0.583 | 14 (8.6) | 63 (6.9) | 0.450 |
| Age, years | 61 (53, 69) | 69 (62, 76) | 60 (52, 68) | < 0.0001 | 66 (58, 74) | 60 (52,68) | < 0.0001 |
| BMI, kg/m2 | 24.5 (22.3, 26.6) | 23.5 (21.5, 25.3) | 24.6 (22.5, 26.7) | 0.003 | 23.7 (21.6, 25.8) | 24.6 (22.5, 26.7) | 0.008 |
| D to B time, min | 86 (69, 122) | 111 (59, 135) | 83 (67, 117) | 0.242 | 101 (84, 140) | 83 (66, 113) | 0.084 |
| SBP, mmHg | 130 (115, 144) | 126 (107, 145) | 130 (116, 144) | 0.065 | 125 (108, 144) | 130 (116, 145) | 0.010 |
| DBP, mmHg | 80 (70, 87) | 76 (66, 86) | 80 (70, 88) | 0.097 | 78 (66, 86) | 80 (70, 88) | 0.072 |
| cTnI, pg/mL | 1757.4 (18.6, 32104.0) | 9251.3 (436.6, 50000.0) | 1431.3 (14.5, 26842.7) | < 0.0001 | 11265.8 (845.6, 50000.0) | 1129.3 (12.8, 25832.7) | < 0.0001 |
| NT-proBNP, pg/mL | 396.5 (98.0, 1926.3) | 4437.5 (947.3, 9906.5) | 310.5 (81.6, 1312.3) | < 0.0001 | 2746.0 (378.5, 8056.5) | 302.4 (77.5, 1281.0) | < 0.0001 |
| Leucocyte counts, 109/L | 8.43 (6.45, 11.10) | 10.05 (7.26, 14.14) | 8.31 (6.37, 10.88) | < 0.0001 | 10.00 (7.28, 13.85) | 8.21 (6.33, 10.79) | < 0.0001 |
| Neutrophil counts, 109/L | 5.99 (4.14, 8.93) | 7.84 (5.37, 12.42) | 5.89 (4.03, 8.67) | < 0.0001 | 7.74 (5.39, 12.13) | 5.75 (3.97, 8.62) | < 0.0001 |
| Lymphocyte counts, 109/L | 1.42 (1.03, 1.89) | 1.23 (0.78, 1.53) | 1.48 (1.07, 1.91) | < 0.0001 | 1.20 (0.80, 1.59) | 1.49 (1.09, 1.92) | < 0.0001 |
| Monocyte counts, 109/L | 0.54 (0.39, 0.73) | 0.58 (0.42, 0.81) | 0.53 (0.39, 0.73) | 0.176 | 0.59 (0.40, 0.80) | 0.53 (0.39, 0.73) | 0.180 |
| Hemoglobin, g/L | 136 (124, 149) | 124 (111, 139) | 137 (126, 150) | < 0.0001 | 130 (116, 146) | 137 (126, 150) | 0.001 |
| Platelet counts, 109/L | 213 (177, 260) | 208 (176, 262) | 214 (177, 259) | 0.294 | 208 (175, 263) | 215 (177, 259) | 0.377 |
| Admission glucose levels, mmol/L | 7.02 (5.8, 9.5) | 8.0 (6.5, 11.0) | 6.9 (5.8, 9.1) | < 0.0001 | 7.8 (6.4, 10.4) | 6.8 (5.8, 9.0) | 0.001 |
| HbA1c, % | 6.0 (5.6, 7.1) | 6.3 (5.6, 7.8) | 6.0 (5.6, 6.9) | 0.240 | 6.3 (5.6, 7.6) | 6.0 (5.6, 6.9) | 0.126 |
| TG, mmol/L | 1.34 (0.82, 2.09) | 1.08 (0.79, 1.68) | 1.35 (0.82, 2.15) | 0.004 | 1.09(0.68, 1.73) | 1.37 (0.85, 2.17) | < 0.0001 |
| TC, mmol/L | 4.12 (3.43, 4.89) | 4.00 (3.20, 4.90) | 4.12 (3.45, 4.89) | 0.330 | 4.04 (3.29, 4.89) | 4.12 (3.43, 4.89) | 0.680 |
| LDL-C, mmol/L | 2.64 (1.94, 3.34) | 2.58 (1.85, 3.51) | 2.6 (1.96, 3.34) | 0.909 | 2.61 (1.97, 3.51) | 2.6 (1.94, 3.32) | 0.476 |
| HDL-C, mmol/L | 0.99 (0.84, 1.16) | 0.99 (0.82, 1.17) | 0.99 (0.85, 1.16) | 0.731 | 1.00 (0.83, 1.16) | 0.99 (0.85, 1.16) | 0.944 |
| ALT, U/L | 24 (16, 41) | 27 (15, 55) | 24 (17, 39) | 0.274 | 27 (16, 50) | 24 (17, 39) | 0.111 |
| AST, U/L | 32 (21, 85) | 56 (23, 192) | 31 (20, 76) | 0.001 | 51 (25, 163) | 31 (20, 74) | < 0.0001 |
| Albumin, g/L | 41.5 (38.8, 44.0) | 38.6 (35.1, 41.7) | 41.7 (19.1, 44.3) | < 0.0001 | 39.3 (36.2, 42.5) | 41.7 (39.2, 44.3) | < 0.0001 |
| Serum creatinine, umol/L | 82 (69, 101) | 105 (82, 145) | 81 (68, 98) | < 0.0001 | 93 (74, 136) | 81 (69, 98) | < 0.0001 |
| eGFR, mL/min/1.73m2 | 86.5 (67.5, 106.1) | 60.6 (38.5, 80.4) | 88.7 (71.0, 108.2) | < 0.0001 | 72.1 (46.7, 90.6) | 88.9 (71.0, 108.1) | < 0.0001 |
| hsCRP, mg/L | 2.8 (1.1, 9.5) | 10.3 (2.1, 33.2) | 2.6 (1.1, 8.2) | < 0.0001 | 5.7 (1.4, 23.4) | 2.6 (1.0, 7.9) | 0.001 |
| ESR, mm/h | 7 (3, 15) | 14 (5, 37) | 7 (3, 14) | < 0.0001 | 9 (4, 28) | 7 (3, 14) | 0.001 |
| log10 SII | 2.9 (2.7, 3.2) | 3.2 (2.8, 3.4) | 2.9 (2.6, 3.2) | < 0.0001 | 3.1 (2.8, 3.4) | 2.9 (2.7, 3.2) | < 0.0001 |
| SIRI | 2.1 (1.2, 4.2) | 3.4 (1.9, 5.8) | 2.0 (1.1, 4.0) | < 0.0001 | 3.3 (1.9, 5.6) | 2.0 (1.1, 4.0) | < 0.0001 |
| LVEF, % | 59 (50, 64) | 49 (38, 58) | 60 (50, 65) | < 0.0001 | 53 (41, 60) | 60 (50, 65) | < 0.0001 |
| Killip class | < 0.0001 | < 0.0001 | |||||
| ≤ 2 | 959 (89.5) | 63 (59.4) | 896 (92.8) | 112 (68.7) | 847 (93.2) | ||
| > 2 | 113 (10.5) | 43 (40.6) | 70 (7.2) | 51 (31.3) | 62 (6.8) | ||
| TIMI flow after PCI | < 0.0001 | < 0.0001 | |||||
| 0–1 | 14 (1.3) | 6 (5.7) | 8 (0.8) | 7 (4.3) | 7 (0.8) | ||
| 2 | 34 (3.2) | 8 (7.5) | 26 (2.7) | 12 (7.4) | 22 (2.4) | ||
| 3 | 1024 (95.5) | 92 (86.8) | 932 (96.5) | 144 (88.3) | 880 (96.8) | ||
| Number of diseased coronary vessels | < 0.0001 | < 0.0001 | |||||
| 0–1 | 336 (31.3) | 18 (17) | 318 (32.9) | 28 (17.2) | 308 (33.9) | ||
| 2 | 357 (33.3) | 23 (21.7) | 334 (34.6) | 45 (27.6) | 312 (34.3) | ||
| 3 | 379 (35.4) | 65 (61.3) | 314 (32.5) | 90 (55.2) | 289 (31.8) | ||
| Culprit vessel | 0.408 | 0.503 | |||||
| LAD | 540 (50.4) | 51 (48) | 489 (50.6) | 84 (51.5) | 455 (50.1) | ||
| LCX | 178 (16.6) | 13 (12.2) | 165 (17.1) | 21 (12.9) | 158 (17.3) | ||
| RCA | 320 (29.8) | 38 (35.7) | 282 (29.2) | 52 (31.9) | 268 (29.5) | ||
| LM | 34 (3.2) | 4 (4.1) | 30 (3.1) | 6 (3.7) | 28 (3.1) | ||
| Medications | |||||||
| SAPT | 1059 (98.8) | 105 (99.1) | 954 (98.8) | 0.790 | 162 (99.4) | 897 (98.7) | 0.448 |
| DAPT | 1041 (97.1) | 103 (97.2) | 938 (97.1) | 0.909 | 160 (98.2) | 880 (96.8) | 0.441 |
| Statins | 1058 (98.7) | 104 (98.1) | 954 (98.8) | 0.579 | 161 (98.8) | 897 (98.7) | 0.923 |
| ACEI/ARB | 728 (67.9) | 67 (63.2) | 661 (68.4) | 0.275 | 112 (68.7) | 616 (67.8) | 0.812 |
| β-blocker | 785 (73.2) | 61 (57.5) | 724 (74.9) | < 0.0001 | 100 (61.3) | 685 (75.4) | < 0.0001 |
| Nitrates | 881 (82.2) | 91 (85.9) | 790 (81.8) | 0.411 | 137 (84) | 744 (81.8) | 0.635 |
| Diuretics | 181 (16.9) | 44 (41.5) | 137 (14.2) | < 0.0001 | 56 (34.4) | 125 (13.8) | 0.037 |
Values are medians (25th and 75th percentiles) or percentages (%). Previous PCI previous percutaneous coronary intervention, BMI body mass index, D to B door-to-balloon, SBP systolic blood pressure, DBP diastolic blood pressure, cTnI cardiac troponin I, NT-proBNP N-terminal pro-B-type natriuretic peptide, HbA1c hemoglobin A1c, TG triglycerides, TC total cholesterol, LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol, ALT alanine aminotransferase, AST aspartate aminotransferase, eGFR estimated glomerular filtration rate, hsCRP high-sensitivity C-reactive protein, ESR erythrocyte sedimentation rate, SII systemic immune-inflammation index, SIRI systemic inflammation response index, LVEF left ventricular ejection fraction, TIMI thrombolysis in myocardial infarction, LAD left anterior descending artery, LCX left circumflex artery, RCA right coronary artery, LM left main coronary artery, SAPT single antiplatelet therapy, DAPT dual antiplatelet therapy, ACEI/ARB angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers, MACEs major adverse cardiovascular events (cardiovascular death, recurrent myocardial infarction, non-fatal stroke)
Correlation among the included variables
We conducted Spearman correlation analyses to assess the relationships between variables. Through correlation analyses, we found that log10 SII and SIRI showed strong correlations with cTnI, NT-proBNP, AST, and hsCRP. Among them, the correlation coefficient (R) between SIRI and cTnI reached 0.583, while those with NT-proBNP and hsCRP were 0.371 and 0.412, respectively. All the results are presented in Figure S1.
The associations between log10 SII, SIRI and all-cause mortality, MACEs
To further elucidate the relationships between log10 SII, SIRI and the 2 outcomes (all-cause mortality and MACEs), log10 SII and SIRI were divided into four quartiles (Q1, Q2, Q3, Q4), with Q1 as the reference group. Cox proportional hazards regression analyses were conducted in the 3 Models mentioned above. The results indicated that, for both all-cause mortality and MACEs outcomes, the Q4 groups of log10 SII and SIRI remained significantly associated with the outcomes compared to the Q1 group, even in Model 3, which included multiple covariates (all-cause mortality: log10 SII: HR = 8.994, 95%CI 1.265–63.948, p = 0.028; SIRI: HR = 3.671, 95% CI 1.004–13.425, p = 0.049; MACEs: log10 SII: HR = 6.465, 95% CI 1.356–30.821, p = 0.019; SIRI: HR = 4.739, 95% CI 1.778–12.632, p = 0.002). The detailed data are presented in Table 2 and Table S2. Kaplan–Meier analysis was performed to assess the association between log10 SII, SIRI and the risk of MACEs, all-cause mortality, and participants in Q4 had significantly worse survival compared with the lower quartiles (Q1–Q3) (Fig. 1; Figure S2).
Table 2.
Multivariate Cox regression of MACEs with log10 SII and SIRI
| MACEs | Continuous | Q1 | Q2 | Q3 | Q4 |
|---|---|---|---|---|---|
| HR (95%CI) | HR (95%CI) | HR (95%CI) | HR (95%CI) | ||
| p | p | p | p | ||
| log10 SII | |||||
| Model 1 | 3.902 (2.568, 5.929) | REF | 1.628 (0.945, 2.805) | 2.038 (1.207, 3.442) | 3.347 (2.046, 5.474) |
| < 0.0001 | 0.079 | 0.008 | < 0.0001 | ||
| Model 2 | 3.637 (2.398, 5.515) | REF | 1.747 (1.013, 3.011) | 2.115 (1.252, 3.571) | 3.299 (2.017, 5.398) |
| < 0.0001 | 0.045 | 0.005 | < 0.0001 | ||
| Model 3 | 5.321 (1.485, 19.074) | REF | 5.934 (1.574, 22.375) | 4.981 (1.122, 22.119) | 6.465 (1.356, 30.821) |
| 0.010 | 0.009 | 0.035 | 0.019 | ||
| SIRI | |||||
| Model 1 | 1.004 (0.999, 1.009) | REF | 1.962 (1.077, 3.575) | 3.648 (2.095, 6.352) | 3.788 (2.176, 6.595) |
| 0.089 | 0.028 | < 0.0001 | < 0.0001 | ||
| Model 2 | 1.004 (0.999, 1.009) | REF | 2.11 (1.156, 3.851) | 3.902 (2.236, 6.807) | 3.859 (2.208, 6.744) |
| 0.152 | 0.015 | < 0.0001 | < 0.0001 | ||
| Model 3 | 1.005 (0.948, 1.066) | REF | 2.085 (0.739, 5.887) | 1.826 (0.612, 5.447) | 4.739 (1.778, 12.632) |
| 0.872 | 0.165 | 0.280 | 0.002 | ||
HR hazard ratio, 95% CI 95% confidence interval, REF reference. Model 1: unadjusted; Model 2: adjusted by age and gender; Model 3: adjusted by age, gender, BMI, hypertension, diabetes, hyperlipidemia, previous cerebral infarction, previous PCI, cTnI, NT-proBNP, AST, serum creatinine, hsCRP, SBP, TG, TC, HDL-C, LDL-C, HbA1c, number of diseased coronary vessels, Killip class, TIMI flow after PCI, β-blocker, diuretics
Fig. 1.
Kaplan–Meier survival analyses of MACEs stratified by log10 SII and SIRI: A MACEs of log10 SII; B MACEs of SIRI
The prediction values of log10 SII and SIRI
To assess the predictive value of log10 SII and SIRI for all-cause mortality and MACEs, we constructed ROC curves for log10 SII and SIRI individually, as well as for the combination with multiple variables (cTnI, NT-proBNP, hsCRP). In the MACEs group, the combined predictive performance of log10 SII, SIRI, cTnI, NT-proBNP and hsCRP demonstrated higher AUCs compared to when either log10 SII or SIRI was used alone. Specifically, the AUCs for log10 SII, SIRI, and multivariable combined were 0.652, 0.659, and 0.694. While in the all-cause mortality group, the AUCs were 0.640, 0.653, and 0.781 (Fig. 2). This suggested that the combined use of log10 SII, SIRI and multiple variables provided a more robust predictive model. Furthermore, SIRI exhibited better predictive value for both all-cause mortality and MACEs compared to log10 SII.
Fig. 2.
ROC curves of MACEs and all-cause mortality. A ROC curves of log10 SII, SIRI and multivariable combined for MACEs; B ROC curves of log10 SII, SIRI and multivariable combined for all-cause mortality; multivariable combined: log10 SII, SIRI, cTnI, NT-proBNP and hsCRP
Non-linear association analyses between log10 SII, SIRI and all-cause mortality, MACEs
To further explore the potential non-linear relationships between log10 SII, SIRI, and the occurrence of the 2 different outcomes, we used RCS in the study. After adjusting for gender, age, the history of hypertension, diabetes, hyperlipidemia, previous cerebral infarction, previous PCI, cTnI, NT-proBNP, AST, serum creatinine, hsCRP, SBP, TG, TC, HDL-C, LDL-C, HbA1c, number of diseased coronary vessels, Killip class, TIMI flow after PCI, β-blocker, diuretics, we found that SIRI exhibited non-linear associations with the occurrence of all-cause mortality (p-overall = 0.008, p-non-linear = 0.010) and MACEs (p-overall < 0.001, p-non-linear < 0.001). This indicated that the impact of SIRI on all-cause mortality and MACEs may differ at various levels of SIRI, rather than following a consistent linear trend. However, we did not find non-linear relationships between log10 SII and the 2 outcomes (p-non-linear > 0.05). The results are presented in Fig. 3 and Figure S3.
Fig. 3.
Restricted cubic spline regression analyses of log10 SII and SIRI with MACEs. A The RCS plot of log10 SII for MACEs, adjusted by age, gender, BMI, hypertension, diabetes, hyperlipidemia, previous cerebral infarction, previous PCI, cTnI, NT-proBNP, AST, serum creatinine, hsCRP, SBP, TG, TC, HDL-C, LDL-C, HbA1c, number of diseased coronary vessels, Killip class, TIMI flow after PCI, βblocker, diuretics; B The RCS plot of SIRI for MACEs, adjusted by age, gender, BMI, hypertension, diabetes, hyperlipidemia, previous cerebral infarction, previous PCI, cTnI, NT-proBNP, AST, serum creatinine, hsCRP, SBP, TG, TC, HDL-C, LDL-C, HbA1c, number of diseased coronary vessels, Killip class, TIMI flow after PCI, βblocker, diuretics
Subgroup analyses for log10 SII and SIRI
To further investigate whether log10 SII and SIRI are associated with all-cause mortality and MACEs across different subgroups, we stratified the study population based on the following variables, including gender, age, history of hypertension, diabetes, hyperlipidemia, previous cerebral infarction, previous PCI, eGFR, Killip class, number of diseased coronary vessels and culprit vessel. As shown in Fig. 4 and Figure S4, among patients with MACEs or all-cause mortality, the incidence rates of MACEs or all-cause mortality were significantly higher in males, with no hypertension, a history of hyperlipidemia, no previous cerebral infarction and 3 diseased coronary vessels with the increase of SIRI (p for interaction < 0.05). Regarding log10 SII, we observed that, except for an interaction between log10 SII and the hypertension subgroup (p for interaction < 0.05), there were no significant differences in the association between log10 SII and the 2 outcomes across the other subgroups.
Fig. 4.
Subgroup analyses of log10 SII and SIRI with MACEs. A Subgroup analysis for the association between log10 SII and MACEs; B subgroup analysis for the association between SIRI and MACEs
Discussion
This retrospective study aimed to investigate whether log10 SII and SIRI are independently associated with all-cause mortality and MACEs, and to evaluate their predictive values during the recovery phase of STEMI patients following PCI treatment. The results showed that after adjusting for multiple covariates, both log10 SII and SIRI were independent risk factors for all-cause mortality and MACEs. Furthermore, the higher levels of log10 SII and SIRI were more strongly associated with the occurrence of the two outcomes compared to the lower level. And SIRI demonstrated non-linear relationships between all-cause mortality and MACEs.
According to the 2019 Global Burden of Disease study, cardiovascular diseases (CVD) still account for the highest incidence and mortality rate among global non-communicable diseases, and the age-standardized rates have shown a declining trend, with CVD becoming increasingly prevalent in younger populations [23]. Atherosclerosis is the primary cause of CVD, its development is associated with sustained increases in lipids, lipid deposition and local inflammatory responses. Atherosclerotic plaques formed in blood vessels are often unstable, and when these plaques rupture, they trigger a series of inflammatory responses, which are often accompanied by thrombus formation, embolism, aneurysm formation, etc. Especially in the case of in situ thrombus formation, it worsens the existing arterial stenosis and even leads to occlusion, resulting in various CVD, such as acute coronary syndrome (ACS) [24]. As for lipids, in recent years, some studies have shifted focus toward the components of apolipoproteins and the apolipoprotein particles themselves. There have been more evidences that HDL-C does not exhibit a completely linear relationship with the occurrence and prognosis of CVD. A mendelian randomization (MR) study on lipid metabolism revealed that HDL-C levels are not significantly genetically associated with the occurrence of CVD, and both extremely high and low HDL-C levels may be related to all-cause mortality, dementia and infections. Also, HDL particles themselves may be better predictors of CVD events than HDL-C alone [25–27]. In this study, we found that participants with higher TG and a history of hyperlipidemia had a lower probability of experiencing all-cause mortality and MACEs in the long term compared to those with lower TG and no hyperlipidemia. A study investigating the predictive value of SII in heart failure populations showed that patients with higher SII values appeared less likely to develop hyperlipidemia and obesity [28]. However, these outcomes require further research to clarify.
To the aspect of inflammation, we can observe that in some acute myocardial infarction (AMI) patients, there is no significant increase in LDL-C levels, but there is an enhancement of inflammatory responses in the body [7]. As parts of the inflammatory process, blood cells such as neutrophils, lymphocytes, monocytes and platelets are all associated with the formation of atherosclerosis and the occurrence of myocardial infarction (MI), which has led to the development of novel inflammatory indicators, such as neutrophil-to-lymphocyte ratio (NLR). Many studies have shown that NLR is positively correlated with IL-6, hsCRP and other inflammation markers, and NLR has some predictive value for the occurrence of cardiovascular death, all-cause mortality, and complications after ACS [12, 29, 30].
To integrate more blood cells and better reflect the body’s inflammatory status, SII and SIRI were built. Bo Hu et al. proposed and applied SII when investigating the prognosis of HCC after curative resection [15]. Qi Qi et al. used SIRI to investigate outcomes after chemotherapy for pancreatic cancer [14]. Subsequently, an increasing number of studies have demonstrated that SII and SIRI are important predictive factors in the occurrence and development of various metabolic diseases, rheumatic diseases, kidney diseases and tumors [20, 31–33]. In the field of cardiology, numerous studies have shown that SII and SIRI are associated with the occurrence of CVD and the increased severity of vascular stenosis [19, 33–35]. SII and SIRI have become powerful prognostic factors for various CVD, including heart failure, coronary artery diseases, etc. [18, 28, 32, 36]. Furthermore, in a 10-year follow-up study conducted by Jin et al., it was shown that after adjusting for covariates, both high-quartile SII and SIRI were associated with stroke and all-cause mortality compared to the low-quartile group. However, only high-quartile SIRI was associated with the occurrence of MI [37]. Another retrospective study involving 1,701 ACS patients who underwent PCI showed that NLR, SII and SIRI were all independently associated with the occurrence of MACEs. However, SIRI demonstrated a higher predictive value [38]. In this study, similar conclusions can be observed. The predictive value of SIRI was indeed higher than that of log10 SII (0.659 versus 0.652; 0.653 versus 0.640).
Combining previous studies and our own study, we hypothesize that SIRI, which includes monocytes, may predict the occurrence of CVD more accurately than SII. Meanwhile, SII, which integrates platelets, is more probably related to thrombosis formation and progression, making it potentially a better predictor of prognosis in PCI patients [34, 38]. In this study, we found that as a continuous variable, log10 SII demonstrated better predictive value for all-cause mortality and MACEs compared to SIRI. However, when log10 SII and SIRI were converted into categorical variables, the predictive value of high-level log10 SII for all-cause mortality and MACEs was seemingly lower than that of SIRI when compared to the low-level group. We also observed that log10 SII and SIRI were strongly correlated with traditional clinical markers such as cTnI, NT-proBNP, and hsCRP, suggesting that log10 SII and SIRI may have certain values in the occurrence and prognosis of ACS, especially STEMI, while being relatively easier to obtain. Based on ROC curve analyses, SIRI appears to have higher predictive value than SII, and the combined prediction using multiple indicators seems to perform better for all-cause mortality than for MACEs.
There are some limitations in this study. First of all, this is a small-sample, single-center, retrospective study, with participants mainly from the central region of China, carrying an inherent risk of bias and residual confounding. Future multi-center, large-sample, prospective studies are required to determine whether similar findings can be replicated in other regions or ethnic groups. Additionally, due to the large number of background covariates, we were unable to include all potential variables (such as occupation, socioeconomic status, etc.) that could influence the outcomes. Moreover, we only included baseline log10 SII and SIRI at the time of onset, without considering their dynamic changes and the potential impact of these changes on prognosis. Lastly, variations in PCI strategies among different patients, including the differences in the number of stents implanted, the use of thrombolytic therapy, and the choice of vascular access sites, may potentially influence the observed outcomes.
Conclusions
Log10 SII and SIRI are independent risk factors for all-cause mortality and MACEs. Compared to low-level groups, the associations between high-level log10 SII, SIRI and all-cause mortality, MACEs are more significant. The predictive value of cTnI, NT-proBNP, and hsCRP combined with log10 SII and SIRI is higher than the predictive value of log10 SII and SIRI alone. Log10 SII and SIRI can serve as convenient prognostic indicators for patients with STEMI following PCI. Nevertheless, large-scale clinical trials and prospective studies still need to be carried out to confirm these findings.
Supplementary Information
Acknowledgements
Not applicable.
Abbreviations
- ACEI/ARB
Angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers
- ACS
Acute coronary syndrome
- ALT
Alanine aminotransferase
- AMI
Acute myocardial infarction
- AS
Atherosclerosis
- AST
Aspartate aminotransferase
- BMI
Body mass index
- CAD
Coronary artery disease
- CI
Confidence interval
- cTnI
Cardiac troponin I
- CVD
Cardiovascular diseases
- DAPT
Dual antiplatelet therapy
- DBP
Diastolic blood pressure
- D to B time
Door-to-balloon time
- eGFR
Estimated glomerular filtration rate
- ESR
Erythrocyte sedimentation rate
- HCC
Hepatocellular carcinoma
- HbA1c
Hemoglobin A1c
- HDL-C
High-density lipoprotein cholesterol
- HR
Hazard ratio
- hsCRP
High-sensitivity C-reactive protein
- IL-1β
Interleukin-1β
- IL-6
Interleukin-6
- IL-17
Interleukin-17
- LAD
Left anterior descending artery
- LCX
Left circumflex artery
- LDL-C
Low-density lipoprotein cholesterol
- LM
Left main coronary artery
- LVEF
Left ventricular ejection fraction
- MACEs
Major adverse cardiovascular events
- MI
Myocardial infarction
- MR
Mendelian randomization
- NLR
Neutrophil-to-lymphocyte ratio
- NLRP3
NOD-like receptor protein 3
- NSTEMI
Non-ST-segment elevation myocardial infarction
- NT-proBNP
N-terminal pro-B-type natriuretic peptide
- PCI
Percutaneous coronary intervention
- PM/DM
Polymyositis/dermatomyositis
- RCA
Right coronary artery
- RCS
Restricted cubic spline
- ROC
Receiver operating characteristic
- SAPT
Single antiplatelet therapy
- SBP
Systolic blood pressure
- SII
Systemic immune-inflammation index
- SIRI
Systemic inflammation response index
- STEMI
ST-segment elevation myocardial infarction
- TC
Total cholesterol
- TG
Triglycerides
- TIMI
Thrombolysis in myocardial infarction
- TNF-α
Tumor necrosis factor-α
Author contributions
Haoyu Yan, Yan Ye and Senlin Hu carried out the epidemiological investigation, completed the collection of follow-up data, performed statistical analyses and drafted the manuscript. Yang Sun, Yanghui Chen and Rui Li critically revised the manuscript. Guanglin Cui conceived the study, participated in the research design and edited the final manuscript. All authors have read and approved final vision of the manuscript.
Funding
This study was funded by the National Key Research and Development Program of China (2022YFE0209900, 2017YFC0909400).
Data availability
The data relevant to the study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
All patients signed the informed consent before being included in this study. This study complies with the Declaration of Helsinki and has been approved by the institutional ethics committees of the Tongji Hospital.
Consent for publication
Written informed consent for publication of their clinical details and/or clinical images was obtained from the patient/parent/guardian/relative of the patient.
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.
Haoyu Yan and Yan Ye have contributed equally to this work.
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Associated Data
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Supplementary Materials
Data Availability Statement
The data relevant to the study are available from the corresponding author upon reasonable request.