Systemic immune-inflammation index associated with contrast-induced nephropathy after elective percutaneous coronary intervention in a case-control study : Coronary Artery Disease

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Original research

Systemic immune-inflammation index associated with contrast-induced nephropathy after elective percutaneous coronary intervention in a case-control study

Ma, Xiao; Mo, Changhua; Li, Yujuan; Gui, Chun

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Coronary Artery Disease ():10.1097/MCA.0000000000001253, May 24, 2023. | DOI: 10.1097/MCA.0000000000001253
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With the development of cardiovascular interventional techniques, the incidence of acute kidney injury caused by widely used contrast agents has increased significantly, and contrast-induced nephropathy (CIN) has gradually become one of the complications of contrast examinations. This injury may be transient and mild or persistent, resulting in prolonged hospitalization and a significantly higher mortality rate [1]. The incidence of CIN in percutaneous coronary intervention (PCI) patients ranges from approximately 5–25%, is still on the rise, and has become the third most common cause of hospital-acquired acute renal impairment [2,3]. The progression of CIN can increase the incidence of cardiovascular events, renal failure, and death, which seriously threaten patients’ life [4]. Currently, there is no specific drug for the treatment of CIN, so early and accurate identification and prediction of high-risk CIN is the key to preventing acute renal injury and improving patients’ prognosis in the near and long term. Serum creatinine (SCr) and urea (BUN) are commonly used in the clinical assessment of renal impairment and diagnosis of CIN. However, these indicators are easily affected by diet, age, body mass, and other factors, and there is some lag in the diagnosis. Therefore, there is an urgent need to find biomarkers to predict renal function impairment at an early stage.

The possible mechanisms of CIN after PCI include direct injury of renal tubular epithelial cells, inflammation, renal vasoconstriction, oxidative stress, medullary hypoxia, endothelial dysfunction, renal blood flow reduction, and reactive oxygen species generation [5,6]. Inflammatory, immune circulating cells and prothrombotic milieu play a critical role in the occurrence and development of CIN. Many inflammatory markers, including neutrophil to lymphocyte ratio (NLR), and platelet to lymphocyte ratio (PLR), have been explored to evaluate and predict CIN [7,8]. Recently, Hu et al. found a novel biomarker called systemic immune-inflammation index (SII) through a prospective cohort study [9]. SII is an inflammation-related index combined with neutrophil, lymphocyte, and platelet count, which can reflect the body’s comprehensive inflammation, immunity, and thrombotic over NLR and PLR [10]. Many studies have revealed that SII is a reliable biomarker for predicting the prognosis of patients with severity of coronary lesions, acute myocardial infarction, and clinical outcomes of other inflammatory diseases and have attracted the attention of a growing number of researchers [11,12]. However, the association of SII and CIN in patients undergoing elective PCI has not been reported.

The present study explored the relationship between SII and CIN in patients undergoing elective PCI.

Materials and methods

Patient selection

Between March 2018 and July 2020, 241 patients with coronary heart disease undergoing elective PCI in the First Affiliated Hospital of Guangxi Medical University were included in this study. Patients with elective PCI were included, and the exclusion criteria were (1) patients who had been exposed to contrast agents within 1 week before the procedure; (2) patients who were allergic to iodinated contrast agents; (3) patients with acute cerebrovascular disease, arrhythmia, cardiogenic stroke, or severe valvular heart disease, severe heart failure [left ventricular ejection fraction (LVEF) <30%]; (4) those who had been exposed to any nephrotoxic drug within 2 weeks prior to the procedure; (5) those who presented with severe liver disease, thyroid dysfunction, malignancy or infectious disease, and recent surgery or trauma (within 3 months); and (6) individuals less than 18 years of age, those with a history of kidney disease or nephrectomy, and those with related medical records. Treatment at admission and peri-procedural anticoagulation regimens were selected according to accepted guidelines and standard methods. Interventional cardiologists chose dual antiplatelet therapy agents of aspirin and P2Y12 inhibitors, initiated at admission, during PCI, or immediately after PCI. Unfractionated heparin was used during surgery. The surgical team decided whether to use glycoprotein IIb/IIIa inhibitors in the perioperative period. Experienced angiographers recorded coronary angiographic features. The requirement for informed consent was not necessary because of the retrospective nature of this study. This study has been approved by the Human Research Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (2017, KY-E-102), China.

Data acquisition

Patient data were retrospectively collected. Baseline demographic characteristics included age and sex. Vital signs included hypertension and diabetes mellitus. Laboratory parameters on admission included white blood cell count, hemoglobin (Hb), platelet (PLT) count, Mean platelet volume(MPV), Lymphocyte count, uric acid (UA), blood urea nitrogen, alanine aminotransferase, total protein, blood Albumin (ALB), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), homocysteine, NT-proBNP and presence of proteinuria. Cardiac ultrasound and coronary angiography/PCI data were also collected. SCr was measured at baseline, 24 h, 72 h, and longer after the procedure.

Related definitions

Coronary artery disease was defined as having at least one coronary stenosis of more than 50%. CIN was defined as any of the following: increase in SCr level by ≥0.5 mg/dl (≥44.2 mol/L) or increase in SCr to ≥25% over the baseline value within 48–72 h after contrast agent administration; or urine volume <0.5 ml/kg/h for 6 h [13]. The neutrophil-lymphocyte ratio was defined as the ratio of the neutrophil count to the lymphocyte count. The SII was derived by multiplying the NLR with the absolute platelet count. The estimated glomerular filtration rate (eGFR) is based on the Levey-modified Modification of Diet in Renal Disease formula: eGFR (mL.min−1 per 1.732 m) = (186.3 × SCr (mg/dl) − 1.154) × (age−0.203) × (0.79 if female) [14]. Hypertension was defined as a systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg on three different days. Diabetes mellitus was defined as a fasting plasma glucose level, fasting blood glucose level >7.0 mmol/L or 2 h postprandial blood glucose >11.1 mmol/L or active use of an anti-diabetic agent. Anemia was defined as hemoglobin less than 120 g/L in males and 110 g/L in females. Weight and height were measured in the initial visit. BMI was calculated as follows: body weight (kg)/[height (m)]2.

Statistical analysis

Statistical analyses were performed using SPSS 25.0 and R software (Version x64, 4.0.3; for Windows. The Kolmogorov–Smirnov was used to test the normality of the numeric variables. For continuous variables, if data conform to normal distribution, they will be expressed as mean ± SD. If not, they will be shown as median and quartile. Continuous variables were compared by t-test, and variables with abnormal distribution were compared by Mann–Whitney U test. Categorical variables were described as counts and percentages. The comparison of categorical variables was performed by Chi-square or Fisher exact test. The least absolute shrinkage and selection operator (LASSO) method [15,16], which is suitable for the reduction in high dimensional data, was used to preliminary select the possible predictive factors to identify CIN from the PCI patients. Preliminary screening factors would be considered as optimal predictive features if they differed between CIN and non-CIN (P < 0.05). The correlation statistics of optimal predictive features associated with the SII levels were performed using the Spearman correlation test. The optimal predictive features were further used to perform univariate analysis and incorporated into the multivariate logistic regression model. Due to the skewed distribution, the logarithmic transformation of SII with base two was performed and used for logistic regression analysis. A two-sided P < 0.05 was considered significant.


Characteristics of the study

The basic clinical characteristics of the patients with and without CIN are shown in Table 1. The present study included 241 patients who underwent elective PCI. Among 241 subjects enrolled in this study, 40 (16.59%) patients developed CIN after the procedure. The hemoglobin, eGFR, and lymphocyte count of patients in the CIN group were lower than the no-CIN group in the cohort. In contrast, age, numbers of diabetes, neutrophil count, UA, CysC, NT-proBNP, and Hcy were significantly higher in patients with CIN in comparison to those not developing CIN. Body-mass index, diabetes, hypertension, and smoking status did not differ significantly between participants in whom CIN developed and those in whom it did not. Hemodynamic parameters, such as heart rate, SBP, and DBP, were similar between the elective PCI patients with or without CIN. Also, the LVEF was similar between the two groups. Angiographic and medication data were also similar between the two groups, as presented in Table 2. The amount of contrast medium administered was similar between the two groups (P = 0.443). There was also no significant difference between the two groups regarding the procedure duration (P = 0.242).

Table 1 - Clinical features in the contrast-induced nephropathy group and the non-contrast-induced nephropathy group
All subjects Non-CIN CIN P values
Patients (n) 241 201 40 -
Age (years) 63 (54–68) 62 (53.5–67) 67.5 (56.75–72) 0.009
Male sex, n (%) 193 166 27 0.029
Hypertension, n (%) 164 136 28 0.772
Diabetes, n (%) 70 53 17 0.040
Smoking, n (%) 130 112 18 0.214
Dyslipidemia, n (%) 57 50 7 0.316
Heart rate 76 (66–84) 76 (65–82.5) 76 (67–86.75) 0.685
SBP (mmHg) 131 (121–145.5) 130 (121–144.5) 140 (125–152) 0.165
DBP (mmHg) 75 (68–82) 76 (69–82) 74 (65–84.5) 0.326
BMI (kg/m2) 24.88 ± 3.55 24.98 ± 3.43 24.41 ± 4.09 0.36
Hemoglobin (g/L) 133 (123.1–144.05) 135 (125–144.5) 122.6 (114.65–133.6) 0.000
Platelet count (×103/µl) 234.3 (203–278.7) 232.9 (196.45–277.8) 252.25 (209.4–280.7) 0.138
WBC (×103/µl) 7.07 (5.94–8.38) 6.94 (5.94–8.12) 7.45 (6.38–9.07) 0.083
Neutrophil counts (×103/µl) 4.31 (3.39–5.21) 4.2 (3.34–5.1) 4.78 (3.97–6.0) 0.002
LYM (×103/µl) 1.78 (1.37–2.2) 1.83 (1.41–2.24) 1.53 (1.28–1.92) 0.015
N/L ratio 2.43 (1.82–3.23) 2.33 (1.78–3.11) 2.96 (2.37–4.35) 0.000
P/L ratio 137.3 (105.44–178.31) 133.5 (101.9–172.43) 161.05 (132–203.23) 0.001
SII 590.60 (414.41–876.62) 565.45 (392–826) 772 (570–1097) 0.000
Fasting glucose (mmol/L) 4.98 (4.49–5.67) 4.97 (4.53–5.6) 5.1 (4.13–6.29) 0.814
HBAc1 (%) 6.2 (5.8–6.5) 6.2 (5.8–6.5) 6.3 (5.93–7.08) 0.147
LDL cholesterol (mg/dl) 2.45 (1.89–3.13) 2.36 (1.84–2.98) 2.68 (2.13–3.8) 0.009
HDL cholesterol (mg/dl) 0.95 (0.83–1.11) 0.95 (0.82–1.11) 0.97 (0.85–1.11) 0.741
Triglyceride (mg/dl) 1.38 (0.99–2.04) 1.36 (0.98–2.03) 1.54 (1.04–2.27) 0.658
CK-MB (U/L) 13.5 (10–17.7) 13.4 (10–17) 13.85 (9.1–19.0) 0.534
UA (µmol/L) 396 (328–454.5) 387 (324–452) 429 (373.5–510.75) 0.012
Serum creatinine (µmol/L) 84 (72–96) 84 (72–95.5) 83.5 (70–107) 0.997
eGFR (ml/min/1.73m2) 76.18 (62.08–93.74) 77.6 (63.33–93.57) 63.02 (50.84–96.65) 0.039
CysC (mg/L) 0.98 (0.84–1.15) 0.98 (0.84–1.13) 1.07 (0.85–1.47) 0.046
Total bilirubin (mg/dl) 7.7 (5.6–11.4)) 7.9 (5.9–11.4) 6.35 (4.1–12.05) 0.155
ALB(g/L) 40.82 ± 4.54 40.70 ± 4.52 41.44 ± 4.61 0.34
Homocysteine (ng/ml) 12.49 (10.82–14.38) 12.46 (10.71–14.00) 13.39 (11.65–16.35) 0.02
CRP (mg/L) 8.33 (1.85–28.99) 9.8 (1.99–29.5) 4.9 (1.49–16.99) 0.095
NT-proBNP (pg/ml) 238.4 (65.31–852.2) 175.4 (62.17–636.35) 878.85 (249.23–2252.25) 0.000
LVEF (%) 66 (58–71) 66 (60–71) 61.5 (47–71) 0.113
LVESD (mm) 32 (29–36) 32 (29–35.5) 33.5 (29–44) 0.2
LVEDD (mm) 50 (48–54) 50 (48–54) 51 (48–58) 0.216
Significance for bold values means that P < 0.05.
ALB, albumin; DBP, diastolic blood pressure; eGFR, estimated glomerular filtrationrate; HDL, high-density lipoprotein; L, lymphocyte; LDL, low-density lipoprotein; LVEDD, left ventricular end-diastolic dimension; LVEF, left ventricular ejection fraction; LVESD, left ventricular end-systolic dimension; N, neutrophil; P, platelet; SBP, systolic blood pressure; SII, systemic immune-inflammation index; UA, uric acid; WBC, white blood cells.

Table 2 - Medications and angiographic
Characteristics All patients (241) No-CIN (n = 201) CIN (n = 40) P value
Angiographic characteristics
 Total time of procedure, minutes 80 (65–100) 80 (65–92) 80 (70–100) 0.242
 Total amount of contrast medium, mL 172 (168–187) 172 (164–187) 174 (172–176) 0.443
 Multivessel disease, n (%) 192 159 (82.81%) 33 (%) 0.394
 Chronic total occlusion, n (%) 67 54 (80.59%) 13 (%) 0.468
 Stent implantation, n (%) 0.425
  1 177 146 (%) 31 (%) /
  2 56 47 (%) 9 (%) /
  3 8 8 (%) 0 (%) /
 Total length of stent, mm 30 (27.5–35) 30 (27.5–35) 30 (30–35) 0.342
 Stent diameter, mm 2.5 (2–3.5) 2.5 (2–3.5) 2.85 (2.0–3.45) 0.781
Medications during hospitalization
 Aspirin, n (%) 238 199 (99%) 39 (97.5%) 0.615
 Clopidogrel, n (%) 136 115 (57.21%) 21 (52.5%) 0.583
 Ticagrelor, n (%) 121 96 (47.76%) 25 (62.5%) 0.089
 Statins, n (%) 237 198 (98.5%) 39 (97.5%) 0.649
 β-blocker, n (%) 221 185 (92.03%) 36 (90%) 0.669
 Ca-blocker, n (%) 61 50 (81.97%) 11 (18.03%) 0.727
 ACE-I/ARB, n (%) 197 168 (24.88%) 29 (72.5%) 0.098
 Nitrate esters, n (%) 104 88 (43.78%) 16 (40%) 0.659
ACE-I, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; CIN, contrast-induced nephropathy.

LASSO regression suggested SII levels as a potential predictor of CIN

Fourteen potential predictors were screened from 42 features (including demographic characteristics, clinical findings, and laboratory tests) based on the LASSO regression analysis of 241 patients (shown in Fig. 1). Their coefficients in the LASSO regression were non-zero. These 14 potential predictors included age, BMI, SBP, diabetes, hyperlipemia, eGFR, UA, SII, CysC, CK-MB, hemoglobin, PLT, SII, and LDL. LASSO regression results initially suggested that SII and CIN were correlated. SII was calculated based on NLR and PLT. Therefore, we further analyzed these three factors in CIN and non-CIN. Our results showed that PLT levels were not significantly different between the CIN and non-CIN groups, while NLR and SII were both significantly elevated in the CIN group, and SII was more significantly different (Fig. 2).

Fig. 1:
Feature selection using the LASSO regression analysis. a. LASSO coefficient profiles of the non-zero variables of CIN patients. a. coefficient profile plot was produced against the log (lambda) sequence. b. thirteen features with non-zero coefficients were selected by optimal lambda. By verifying the optimal parameter (lambda) in the LASSO model, the partial likelihood deviance (binomial deviance) curve was plotted versus log (lambda). CIN, contrast-induced nephropathy; LASSO, least absolute shrinkage and selection operator.
Fig. 2:
The levels of NLR (a), PLT (b), and SII (c) were determined by hematology analyzer in CIN patients (N = 40) and non-CIN (N = 201). Data are presented as means ± SEM. **P < 0.05. CIN, contrast-induced nephropathy.

The correlations between SII levels and clinical features

Among these 14 variables, eight factors including age, eGFR, UA, Diabetes, CysC, Hb, SII, and LDL showed significant differences between CIN and non-CIN. The Spearman correlation analysis between those eight factors was performed through R’s ‘ggcorrplot’ package. The results showed that SII levels were positively correlated with CysC [Spearman correlation coefficient (rsp) = 0.07, P < 0.05], UA (rsp = 0.21, P = 0.05), but inversely correlated with eGFR (rsp = −0.47, P < 0.05). No correlations were found between SII levels and age, LDL-C, and hemoglobin(Fig. 3a). To validate the correlation of SII and these three factors, we performed linear regression analysis with SII as a dependent variable through the ‘ggstatsplot’ package of R. No correlations were found between SII levels and CysC (rsp = 0.07, P = 0.27) (Fig. 3b–3d).

Fig. 3:
Correlation analysis between each features. a. Correlation heat map of SII and the seven optimal predictive features. The depth of the color represents the strength of the correlation; red represents a positive correlation, blue represents a negative correlation. The‘x’means irrelevance. b.Correlation analysis of SII and CysC. c. Correlation analysis of SII and eGFR. d. Correlation analysis of SII and UA. eGFR, estimated glomerular filtration rate; SII, systemic immune-inflammation index; UA, uric acid.

Risk factor analysis for CIN

In a univariate model, elevated log2SII was a risk factor for the development of CIN in patients undergoing elective PCI. Similar results were also observed when SII levels were analyzed as an ordinal variable. The odds ratio (OR) for the highest versus the lowest quartile of SII was 8.415 [95% confidence interval (CI) 2.389–29.642; P = 0.001] in PCI patients. Age, UA, diabetes, CysC, hemoglobin, and LDL were also associated with the development of CIN. Multivariate logistic regression analysis revealed that log2SII (OR: 2.686, 95% CI: 1.457–4.953, P = 0.002), age(OR:1.067, 95%CI:1.01–1.13, P = 0.019), eGFR(OR:1.027, 95%CI:1.008–1.048, P = 0.007), Hemoglobin(OR:0.971, 95%CI:0.948–0.99, P = 0.013) and LDL(OR:1.794, 95%CI:1.187–2.71, P = 0.006) were independent risk factors for the development of CIN in patients undergoing elective PCI (Table 3).

Table 3 - Univariate and multivariate regression analysis of predictors of for prediction of contrast-induced nephropathy
Univariate Multivariate
Odds ratio 95% CI P-value Odds ratio 95% CI P-value
Age 1.054 1.01–1.10 0.01 1.067 1.01–1.13 0.019
eGFR 0.992 0.979–1.01 0.273 1.027 1.008–1.048 0.007
Diabetes 2.064 1.02–4.16 0.043 2.031 0.9–4.59 0.088
Hemoglobin 0.968 0.97–0.99 0.002 0.971 0.948–0.99 0.013
LDL 1.562 1.12–2.19 0.01 1.794 1.187–2.71 0.006
UA 1.004 1.00–1.01 0.006 1.003 0.999–1.006 0.167
CysC 4.370 1.81–10.53 0.001 3.703 0.974–14.078 0.055
SIIlog2 3.033 1.78–5.16 0.000 2.686 1.457–4.953 0.002
CI, confidence interval; eGFR, estimated glomerular filtrationrate; LDL, low-density lipoprotein; SII, systemic immune-inflammation index; UA, uric acid.

Interaction of SII with risk factors on the presence of CIN

Figure 4 shows a significant interaction of log2SII with SEX, diabetes, eGFR, and UA on the development of CIN (Interaction P < 0.05). Interestingly, elevated log2SII levels were strongly associated with the development of CIN in male patients (OR per 1-SD increase: 2.143, 95% CI 1.37–3.35; P = 0.001), but no significant association was found in female patients (OR per 1-SD increase: 1.225, 95% CI 0.90–1.67; P = 0.201). However, no interaction was observed between log2SII and age (>60, ≤60), sex (male, female), BMI (≥25, <25), hemoglobin (≥110, <110), and eGFR (eGFR > 30, eGFR ≤ 30) on the development of CIN (Fig. 4).

Fig. 4:
Association of log2SII levels with CIN risk in the subgroup analysis. Subgroups were as follows: age (> 60, ≤60), sex (male, female), BMI(≥25,<25), hemoglobin(≥110,<110), and eGFR (eGFR > 30, eGFR ≤ 30). CIN, contrast-induced nephropathy; eGFR, estimated glomerular filtration rate.

Predictive value of SII for CIN

A receiver operating characteristic curve was performed to evaluate the predictive value of the SII (Fig. 4). For patients with CIN after PCI, the SII had an area under the curve (AUC) value of 0.701 (CI: 0.62–0.782, P = 0.000). For a cutoff value of 586.19, the SII had a 75% sensitivity and 54.2% specificity for predicting CIN. SII higher than 586.19 contributed to a higher sensitivity but lower specificity (Fig. 5).

Fig. 5:
ROC curve analysis for predictive value of SII in detecting contrast-induced nephropathy. ROC, receiver operating characteristic.


To the best of our knowledge, this study for the first reported that SII is associated with the development of CIN in patients undergoing elective PCI. Our research showed that SII was an independent predictor of CIN after adjusting for other risk factors, especially in male patients. In addition, our results showed that SII has a negative association with eGFR and a positive correlation to UA.

The commonly accepted definition of CIN is an absolute (0.5 mg/dl) or relative (25%) increased SCr from baseline after contrast medium exposure. Although the increasing SCr is usually transient, previous studies have demonstrated that the hospital mortality rate is significantly higher in patients with CIN than in non-CIN [17]. Therefore, early identification of patients with high-risk contrast nephropathy is essential, especially in situations where ACS can be treated with elective PCI. Early clinical evaluation of patients with elective PCI for ACS-NST allows more time for intervention with prophylactic measures, such as administration of various drugs (e.g. intravenous fluids, n-acetylcysteine, sodium bicarbonate) for prophylaxis before and after invasive cardiac procedures [18]. And during the period of coronary intervention in these patients, less contrast agent can also be given to protect the kidneys and reduce or even avoid the development of CIN. The incidence of contrast nephropathy is generally 2% in low-risk and 50% in high-risk populations [19]. Our study found the incidence of CIN to be 16.59%, which seems to be higher than the previously thought incidence. Although we excluded patients with emergency PCI procedures, we enrolled patients with ACS, which may partly explain the higher incidence of CIN in our study. Likewise, previous researches also reported a similar incidence of CIN in these patients [20,21].

SII, a novel defined index that reflects the balance between body inflammation and immune status, combines platelet count and NLR [22]. SII is an inexpensive and noninvasive parameter because it is easily calculated through complete blood counts. This parameter has been shown the predictive value of mortality in patients with malignancy and acute disease. Accumulated evidence demonstrated SII’s potential diagnostic and prognostic value in cardiovascular disease. For example, Erdogan et al. found that elevated SII was a better predictor of hemodynamically significant coronary artery obstruction than NLR and PLR in patients with chronic coronary syndrome [23]. Another study also found that SII was independently associated with no-reflow phenomenon in patients with acute STEMI treated with primary PCI [24]. SII had a better prognostic effect on cardiovascular disease as an inflammatory factor than NLR and PLR [12]. The possible reason for this is that SII contains neutrophil, lymphocyte, and platelet count, combining the predictive power of both NLR and PLR. Gok et al. established an association between high SII levels and disease severity in patients with acute pulmonary embolism [25]. Kelesoglu S et al. first reported that SII might be an independent CIN predicted factor for emergency PCI in non-ST-segment elevation myocardial infarction [26]. Furthermore, AliBag˘ci and Recep O¨ztu¨rk et al. also found that SII can be a potential CIN predictor of emergency PCI in acute ST-segment elevation myocardial infarction [27,28]. Although the above three studies have explored the relationship between SII and CIN, the patients included were those undergoing emergency PCI, for whom the time available for evaluation and implementation of interventions was short, so some preventive strategies couldn’t be adequately applied. Our study extended the previous findings. We found that higher SII levels may also increase the risk of CIN development in patients undergoing elective PCI, especially in male patients. The role of SII in predicting CIN may be more valuable in patients with elective PCI compared to emergency PCI patients. More time can be used to perform a careful risk assessment and adequate intensive treatment in patients with elective PCI.

Several limitations should be acknowledged. First, the present study was retrospective research, so the number of included patients was limited. Second, we did not dynamically investigate the correlation between temporal changes in SII parameters during hospitalization and the risk of CIN. Third, our study only assessed renal function follow-up within 1–3 days after PCI. Therefore, we may have missed some patients whose SCr was not elevated within 72 h after the procedure but whose renal function deteriorated thereafter. This may have led to a slight underestimation of CIN. Moreover, in our study, we only assessed the LVEF of patients and did not evaluate patients according to the clinical characteristics of congestive heart failure. Finally, further multicenter prospective studies are needed to elucidate the exact relationship between SII levels and the risk of CIN in patients with elective PCI. Despite these limitations, the present study may provide additional information for predicting CIN in patients with elective PCI.

In conclusion, our study suggests elevated pre-procedure SII levels are associated with the development of CIN in patients undergoing elective PCI. As a simple and inexpensive indicator of inflammation, SII can be used as part of risk stratification in the group of patients at high risk for CIN.


This work was supported by the Guangxi Natural Science Foundation(Grant No. 2020GXNSFDA297014) and the Innovative Research Team Project of Guangxi Natural Science Foundation(Grant No. 2018GXNSFGA281006).

Concept and design of the work by Chun Gui, Xiao Ma. Writing of the text by Xiao Ma, Changhua MO. Statistical analysis by Yujuan Li. Editing by Chun Gui, Xiao Ma.

Consent for publication: All the co-authors agreed to the publication of this article.

Conflicts of interest

There are no conflicts of interest.


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biomarker; contrast-induced nephropathy; elective percutaneous coronary intervention; systemic immune-inflammation index

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