Journal Logo

Original Research in CAD

Systemic immune-inflammation index predicts in-hospital and long-term outcomes in patients with ST-segment elevation myocardial infarction

Öcal, Lütfia; Keskin, Muhammedb; Cerşit, Sinana,c; Eren, Hayatid; Özgün Çakmak, Endera; Karagöz, Alia; Çakir, Hakana; Gürsoy, Mustafa Ozane; Doğan, Selamib; Zhalilov, Myrzabekc; Türkmen, Mehmet Muhsina

Author Information
doi: 10.1097/MCA.0000000000001117
  • Free

Abstract

Introduction

Ischaemic heart disease is the most common cause of death worldwide [1]. ST-segment elevation myocardial infarction (STEMI) is at the top of ischemic heart disease and should be immediately and appropriately managed to reduce mortality. The existence of emergency medical system based STEMI networks and 24 h/7 days primary percutaneous coronary intervention (pPCI) centers constitute the basis of timely intervention [2]. In countries that have now provided this, it is necessary to focus on the other causes of mortality in STEMI patients and to select the patients with higher risk. It is also important to distinguish the patients who are good candidates for early mobilization and those who should be closely monitored.

Additionally, the mortality in STEMI patients has been influenced by several factors such as advanced age, Killip class, history of myocardial infarction (MI), diabetes mellitus, renal failure, number of diseased coronary arteries and left ventricular ejection fraction (LVEF) [3,4]. Therefore, it is particularly important to identify the patients who are at high risk of developing future complications.

Atherosclerosis is now recognized not only as a cholesterol disorder that accumulates in the vessel walls but also as a persistent, dynamic and inflammatory process in the vasculature. Inflammatory markers are significantly associated with cardiovascular disease and many biomarkers have been used as a predictor in patients with STEMI [5–7]. Some researchers have studied potentially new and practical hematological markers. In fact, these markers are directly calculated and given in some laboratory test results. These include the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR), which provide useful information for predicting future events in patients with STEMI [8–11].

Recently, the systemic immune inflammation index (SII) has been developed based on platelet count and NLR (SII, platelet count × neutrophil/lymphocyte ratio) to simultaneously assess the inflammatory and immune status of the patients. It had been reported that the high SII was associated with poor outcomes in many malignant diseases, particularly solid tumors, hepatocellular carcinoma, gastrointestinal cancers, lung cancers, gynecological and breast cancers [12–17]. Recently, it has been mentioned that SII may also be associated with adverse outcomes suggesting potential effects on chronic heart failure and coronary artery disease (CAD) [18,19]. However, the relationship between SII and clinical outcomes in patients with STEMI remains unclear. Therefore, the aim of this study was to investigate the prognostic value of SII regarding cardiovascular outcomes (in-hospital and long-term) in patients with STEMI.

Materials and methods

Study population

A total of 1660 patients who presented with acute STEMI within 12 h of symptom onset in a high-volume tertiary center and who underwent pPCI between January 2014 and December 2017 were included. Consistent with the Declaration of Helsinki, this study was approved by the Institutional Ethics Committee.

The exclusion criteria were comprised of known hypersensitivity to contrast media, statins, PY212 receptor inhibitors, aspirin, angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers, beta-blockers. We also excluded patients who had pregnancy, breastfeeding status, end-stage renal failure [estimated glomerular filtration rate (eGFR) <15 mL/min/l.73 m2 or dialysis treatment), autoimmune diseases, active cancer, severe liver diseases, clinically evident acute or chronic inflammation, hematological disease (including anemia), oncological disease, severe valvular disease, hypo- and hyperthyroidism.

Definitions and endpoints

Diagnostic criteria for STEMI are as follows: (a) typical chest pain lasting more than 30 min (b) ST-segment elevation in at least two leads (V2–V3 at least 0.2 mV in men or 0.15 mV in women and/or in other leads at least 0.1 mV) (c) definitive or probable new left bundle branch block and (d) ST-segment elevation in V3R - V4R and V7–V9 leads obtained in indication. [2].

Hypertension was defined as receiving antihypertensive therapy and/or having an arterial blood pressure (BP) >140 mmHg for SBP and/or 90 mmHg for DBP. Diabetes mellitus was diagnosed as the use of an antidiabetic drug and a fasting blood glucose >126 mg/dL or glucose value measured at any time >200 mg/dL or HbA1c ≥6.5%. Hyperlipidemia was considered in case of total cholesterol >200 mg/dL, low-density lipoprotein cholesterol (LDL-C) >130 mg/dL, triglyceride >150 mg/dL, or receiving lipid-lowering medication. The patients, who currently smoke or quitted smoking within the last year, were classified as smokers.

The primary endpoint was determined as in-hospital cardiogenic shock, acute respiratory failure, acute kidney injury, ventricular arrhythmia, stent thrombosis, recurrent MI, repeat revascularization, major adverse cardiac events (MACE), mortality and all-cause mortality in the long-term follow-up period. MACE included stent thrombosis, recurrent MI, need for repeat revascularization and mortality. Stent thrombosis was defined as angiographic confirmation of a thrombus originating from a stent or 5 mm proximal or distal to the stent, with total or partial vessel occlusion leading to MI. Recurrent MI was defined as the recurrence of chest pain and/or new electrocardiogram changes with a new dynamic change (>20% rise from baseline) in cardiac biomarkers (creatine kinase myocardial band and troponin I levels).

An antecubital venous blood sample was drawn upon admission from each patient either in the coronary care unit or in the emergency department before administration of any medication. Complete blood counts, which included platelets, total white blood cells, neutrophils and lymphocytes, were obtained using an automatic blood counter (Beckman Coulter LH 750, Fullerton, California, USA). Blood urea nitrogen and serum creatinine levels were measured as part of biochemical parameters using an Architect Plusci 4100 (Abbott Laboratories, Abbott Park, Illinois, USA). The eGFR was calculated by using Cockcroft–Gault equation [20].

Transthoracic echocardiography was performed in all patients within 48 h after admission using GE Vivid 7 systems (GE Vivid 5 and 7; GE Healthcare, Piscataway, New Jersey, USA). The LVEF of each patient was calculated using the modified biplane Simpson method.

SII index was calculated as total peripheral platelets count (P) × NLR (N/L) (SII = P × N/L ratio) [12].

This hospital is a high-volume tertiary cardiology center hospital where patients with STEMI are referred from various hospitals. There is an experienced interventional cardiology team which has the capability to perform pPCI for 24 h/7 days. A cardiology specialist primarily assesses every patient with chest pain admitted to the emergency department. Premedication (intravenous unfractionated heparin 100 IU/kg, 600 mg clopidogrel and 300 mg aspirin) is given as soon as we diagnosed with STEMI in the emergency department. The patient is immediately transferred to the angiography laboratory and the average door balloon time is approximately 30 min. Coronary lesions were treated using standard PCI techniques. The choice of the stent type and the decision to use glycoprotein IIb/IIIa antagonists was at the discretion of the operator. Postdilatation was performed with additional balloons to ensure optimum stent apposition. The drugs were administered during the hospitalization and after discharge according to the European Society of Cardiology Guidelines.

Cardiovascular mortality was defined according to the consensus article [21]. Additionally, we evaluated the association between SII and different ‘types’ of deaths, and revised the manuscript accordingly:

Cardiovascular death includes acute myocardial infarction, fatal arrhythmia, sudden cardiac death, heart failure, stroke, cardiovascular procedures and cardiovascular hemorrhage. The number of cardiovascular death and noncardiovascular death was 60 and 12, respectively, during the hospitalization. Noncardiovascular death includes cancer, infections, nonprocedural bleeding, acute kidney and hepatic injury and sepsis.

Statistical analysis

The baseline characteristics of the patients were categorized according to admission SII levels: Q1, Q2, Q3 and Q4. Kolmogorov–Smirnov test was used for testing normality. Continuous variables with normal distributions were expressed as mean ± SD and compared using one-way analysis of variance. Continuous variables with skewed distributions were expressed as mean ± SD and compared using the Kruskal–Wallis test. Categorical variables were expressed as numbers and percentages and Pearson’s χ2 or Fisher’s exact tests were used to evaluate the differences. A restricted cubic spline basis for SII was used to investigate a possible nonlinear association with the parameters. Hierarchical logistic regression was used to assess the independent relationship between SII levels and in-hospital mortality and cardiogenic shock, after adjustment for confounders. After 33.3 ± 8.8 months of follow-up, survival times of the four groups were compared using the Kaplan–Meier survival method. Overall survival was calculated from the day of diagnosis to the day of death or last follow-up. Differences between the groups were analyzed by the log-rank test. A forward Cox proportional regression model was used for multivariable analysis. The hazard ratio indicates the relative risk of death in each SII subgroup compared with those in the lowest-risk subgroup (Q1). In multivariable models, confounders in the bivariate analysis were considered as predictors of mortality. Four models were generated to indicate the effects of potential confounders on the association between SII level and long-term. These four models include unadjusted; adjusted for age, sex, Killip class and LVEF; adjusted for eGFR and comorbidities; adjusted for all confounders including demographics (age and sex); first measurement during hospitalization of the following laboratory values (admission glomerular filtration rate calculated by Cockcroft–Gault, blood urea nitrogen, glucose, white blood cell count, hematocrit and platelet count); Killip class and LVEF; chest pain and door-to-balloon period; comorbidities (diabetes, chronic kidney disease and hypertension); medications during hospitalization.

A two-tailed P < 0.05 was considered statistically significant, and 95% confidence intervals (CI) were presented for all hazard ratio. Finally, we performed a restricted cubic spline analysis to assess the all-cause mortality as a function of SII, in a continuous fashion. Analyses were performed using Statistical Package for Social Sciences software, version 20 (IBM, Armonk, New York, USA).

Results

The study included 1660 patients, who were divided into four quartiles (Q) according to the SII levels at admission. The baseline characteristics of the patients categorized according to SII levels were listed in Table 1. SII levels tended to increase from Q1 to Q4. Age, gender, BMI, history of hypertension, diabetes mellitus, hyperlipidemia, heart failure, chronic kidney disease, previous PCI and smoking status were similar between the groups. The frequency of previous MI and coronary artery bypass grafting were found to be higher in the Q1 group compared to other groups. At admission, the mean SBP and DBP and heart rates were similar between the groups. The incidence of Killip class III and IV were more common in the Q3 and Q4 groups compared to Q1 and Q2 groups. LVEF of the patients were lower in the Q3 and Q4 groups compared to Q1 and Q2 groups. The frequency of anterior MI, duration of chest pain, pain to balloon time and door to balloon time were similar between the groups. The hematocrit and creatinine levels and eGFR on admission were similar between the groups. White blood cell, neutrophil and platelet counts, blood urea nitrogen and glucose levels on admission were significantly higher in the Q3 and Q4 groups compared to Q1 and Q2 groups. Admission lymphocyte count was significantly lower in the Q3 and Q4 groups compared to Q1 and Q2 groups. The number of diseased coronary arteries, types of PCI, types of stent were similar between the groups. The need for diuretic treatment was higher in the Q3 and Q4 groups compared to Q1 and Q2 groups although the other medications of the patients were similar.

Table 1 - Baseline characteristics and outcomes of patients classified by Systemic Immune-Inflammation Index level
Admission SII level (n = 1660)
I
(n = 415)
II
(n = 415)
III
(n = 415)
IV
(n = 415)
P value
Age 57.4 ± 10.7 56.3 ± 11.3 56.0 ± 11.6 58.0 ± 12.4 0.037
Male gender 346 (83.4) 351 (84.6) 349 (84.1) 342 (82.4) 0.847
BMI 26.7 ± 3.1 26.9 ± 3.0 26.8 ± 3.1 26.7 ± 3.0 0.873
History
 Hypertension 178 (42.9) 177 (42.7) 170 (41.0) 169 (40.7) 0.886
 Diabetes mellitus 85 (20.5) 90 (21.7) 94 (22.7) 92 (22.2) 0.889
 Hyperlipidemia 97 (23.4) 105 (25.3) 114 (27.5) 124 (29.9) 0.170
 Heart failure 34 (8.2) 34 (8.2) 32 (7.7) 41 (9.9) 0.694
 Current smoking status 276 (66.5) 257 (61.9) 266 (64.1) 267 (64.3) 0.594
 Previous MI 127 (30.6) 89 (21.4) 89 (21.4) 100 (24.1) 0.006
 Previous PCI 96 (23.1) 71 (17.1) 74 (17.8) 77 (18.6) 0.115
 Previous CABG 42 (10.1) 19 (4.6) 18 (4.3) 20 (4.8) 0.001
 Chronic kidney disease 24 (5.8) 26 (6.3) 32 (7.7) 36 (8.7) 0.345
At admission
 SBP (mm Hg) 134.8 ± 22 133.8 ± 23 133.6 ± 23.4 131.8 ± 24.4 0.317
 DBP (mm Hg) 71.7 ± 11.8 70.7 ± 12.3 70.4 ± 12.8 70.8 ± 13.3 0.264
 Heart rate (beats per minute) 78.1 ± 12.7 77.2 ± 12.9 76.3 ± 15.3 76.5 ± 16.6 0.272
 Killip Class 3–4 6 (1.4) 11 (2.7) 16 (3.9) 27 (6.5) 0.001
 Left ventricular ejection fraction (%) 51.3 ± 9.8 49.0 ± 9.9 46.9 ± 10.8 44.9 ± 11.4 <0.001
 Anterior MI 180 (43.4) 193 (46.5) 197 (47.5) 215 (51.8) 0.109
 Chest pain period (h) 7.5 ± 5.9 7.3 ± 5.7 7.2 ± 5.9 7.5 ± 6.1 0.831
 Pain-to-balloon time (h) 8.0 ± 5.9 7.8 ± 5.7 7.6 ± 5.8 8.0 ± 6.1 0.813
 Door-to-balloon time (min) 26.9 ± 7.8 27.2 ± 7.6 27.4 ± 7.9 27.2 ± 8.6 0.286
Admission laboratory variables
 Creatinine (mg/dL) 0.9 ± 0.3 0.9 ± 0.5 0.9 ± 0.5 1.0 ± 0.7 0.072
 eGFR (ml/min/1.73 m2) 112.7 ± 35.1 110.7 ± 40.0 111.8 ± 46.8 106.7 ± 40.4 0.059
 White blood cell count (cells/µL) 8.8 ± 2.5 11.0 ± 3.0 12.5 ± 3.5 15.0 ± 5.7 <0.001
 Neutrophil count (cells/µL) 6.0 ± 2.1 8.7 ± 2.5 10.7 ± 3.0 13.7 ± 5.4 <0.001
 Lymphocyte count (cells/µL) 2.8 ± 0.9 2.2 ± 0.7 1.8 ± 0.6 1.3 ± 0.5 <0.001
 Hematocrit (%) 40.6 ± 5.4 40.6 ± 5.0 40.6 ± 5.6 40.0 ± 5.5 0.351
 Platelet count (cells/µL) 204.9 ± 54.6 239.3 ± 67.6 252.5 ± 63.2 296.2 ± 89.9 <0.001
 Blood urea nitrogen (mg/dL) 16.6 ± 5.7 16.8 ± 6.2 17.0 ± 8.2 18.8 ± 8.4 <0.001
 Glucose (mg/dL) 145.5 ± 74.0 145.5 ± 66.3 154.8 ± 68.0 170.9 ± 89.1 <0.001
Vessel disease (stenosis >50%)
 1 vessel 224 (54.2) 220 (53.7) 222 (53.8) 239 (58.2) 0.512
 2 vessels 87 (21.1) 102 (24.9) 105 (25.4) 99 (24.1) 0.462
 3 vessels 102 (24.7) 88 (21.5) 86 (20.8) 73 (17.8) 0.112
PCI type
 Only PTCA 49 (11.8) 59 (14.2) 50 (12.0) 73 (17.6) 0.059
 PTCA and Stent 229 (55.2) 236 (56.9) 216 (52.0) 235 (56.6) 0.480
Stent type
 Drug eluting stent 243 (58.6) 246 (59.3) 249 (60.0) 238 (57.3) 0.883
 Bare metal stent 48 (11.6) 37 (8.9) 33 (8.0) 42 (10.1) 0.323
Out-hospital medication
 Aspirin 402 (96.9) 404 (97.3) 407 (98.1) 412 (99.3) 0.085
 PY212 receptor inhibitors 407 (98.1) 410 (98.8) 410 (98.8) 414 (98.9) 0.153
 Β-blocker 381 (91.8) 378 (91.1) 380 (91.6) 375 (90.4) 0.889
 Statin 376 (90.6) 377 (90.8) 373 (89.9) 373 (89.9) 0.950
 Diuretics 23 (5.5) 24 (5.8) 46 (11.1) 71 (17.1) <0.001
 ACEIs or ARBs 359 (86.5) 349 (84.1) 355 (85.5) 335 (80.7) 0.112
 Insulin treatment 17 (3.6) 18(3.9) 21 (5.1) 22 (8.9) 0.186
Continuous variables are presented as mean ± SD; nominal variables presented as frequency (%).
ACEIs, angiotensin-converting enzyme inhibitors; ARB, angiotensin II receptor blockers; CABG, coronary artery bypass grafting; eGFR, estimated glomerular filtration rate; MI, myocardial infarction; PCI, percutaneous coronary intervention; PTCA, percutaneous transluminal coronary angioplasty; SII, systemic immune-inflammation index.

In-hospital and long-term (3-year) outcomes of the patients were demonstrated in Table 2. In-hospital cardiogenic shock, acute respiratory failure, acute kidney injury, ventricular arrhythmia, stent thrombosis, recurrent MI, repeat revascularization, MACE and mortality were significantly higher in the high SII groups (Q3 and Q4). All-cause mortality was higher in the high SII groups (Q3 and Q4) compared to Q1 and Q2.

Table 2 - In-hospital and long-term (3-year) event rates by admission Systemic Immune-Inflammation Index level
Admission Systemic Immune-Inflammation Index Level (n = 1660)
I
(n = 415)
II
(n = 415)
III
(n = 415)
IV
(n = 415)
P value
In-hospital course
 Cardiogenic shock 7 (1.7) 12 (2.9) 16 (3.9) 47 (11.3) <0.001
 Acute respiratory failure 13 (3.1) 15 (3.6) 25 (6.0) 37 (8.9) 0.001
 Acute kidney injury 30 (7.2) 31 (7.5) 55 (13.3) 74 (17.8) <0.001
 Ventricular arrhythmia 11 (2.7) 14 (3.4) 28 (6.7) 34 (8.2) 0.001
 Stent thrombosis 5 (1.2) 13 (3.1) 15 (3.6) 21 (5.1) 0.018
 Recurrent myocardial infarction 7 (1.7) 11 (2.7) 15 (3.6) 25 (6.0) 0.005
 Repeat revascularization 15 (3.6) 18 (4.3) 21 (5.1) 40 (9.6) 0.001
 Major adverse cardiac events 23 (5.5) 28 (6.7) 40 (9.6) 63 (15.2) <0.001
 Mortality 5 (1.2) 6 (1.4) 17 (4.1) 44 (10.6) <0.001
Out-hospital course
 Follow-up time (month) 36 36 36 36
 All-cause mortality 10 (2.4) 13 (3.1) 35 (8.4) 79 (19.0) <0.001
Variables presented as frequency (%).

According to cardiovascular death prediction model, NLR and SII were independently associated with cardiovascular death (Table 3).

Table 3 - Cardiovascular death prediction (n = 60)
Variables OR, CI 95% P value
NLR (increase from 3.08–7.67)* 5.45 (2.52–11.81) <0.001
Platelet count (increase from 202–283)* 1.09 (0.76–1.57) 0.582
SII (increase from 679–1942)* 7.81(3.12–19.50) <0.001
Adjusted for age, gender, GFR and HGB.
*Regression coefficient of SII, NLR and platelet count given as increase from 25th to 75th percentile.
CI, confidence interval; GFR, glomerular filtration rate; HGB. hemoglobin; NLR, neutrophil to lymphocyte ratio; OR, odds ratio; SII, systemic immune-inflammation index.

According to noncardiovascular death prediction model platelet count and SII were independently associated with cardiovascular death (Table 4).

Table 4 - Noncardiovascular death prediction (n = 12)
Variables OR, CI 95% P value
NLR (increase from 3.08–7.67)a 1.32 (0.90–1.92) 0.14
Platelet (increase from 202–283)a 1.87 (1.35–2.59) 0.005
SII (increase from 679–1942)a 1.34 (1.01–1.77) 0.037
aRegression coefficient of SII, NLR and platelet given as increase from 25th to 75th percentile.
CI, confidence interval; NLR, neutrophil to lymphocyte ratio; OR, odds ratio; SII, systemic immune-inflammation.

Table 5 lists unadjusted and adjusted logistic regression analysis for in-hospital mortality and cardiogenic shock and Cox proportional regression analysis for long-term mortality, categorized by SII quartiles. Q3 and Q4 had 3.5- and 9.7-times, respectively, higher in-hospital mortality rates (95% CI, 1.2–9.5 and 3.8–24.7) than Q1 which had the lowest rates and was used as the reference. Receiver operating characteristic analysis showed that the best cutoff value of the SII to predict the in-hospital mortality was 1781 with 66% sensitivity and 74% specificity (area under the curve, 0.75; 95% CI, 0.69–0.81; P < 0.001). The discriminative value of SII was higher than NLR (Fig. 1). Additionally, the frequency of cardiogenic shock was also higher in patients Q4 that was 7.4-times higher (95% CI, 3.3–16.6) than Q1. Kaplan–Meier overall survivals for Q1, Q2, Q3 and Q4 were 97.6, 96.9, 91.6 and 81.0% respectively (Fig. 2). Q3 and Q4 had 3.5- and 8.4-times, respectively, higher long-term mortality rates (95% CI, 1.7–7.1 and 4.3–16.3) than Q1 which had the lowest rates and used as the reference. These significant relationships also persisted even after adjustment for all confounders.

Table 5 - In-hospital event rates and logistic regression models for mortality and cardiogenic shock by Systemic Immune-Inflammation Index and Cox proportional analysis and 3-year mortality by Systemic Immune-Inflammation Index
Systemic Immune-Inflammation Index (n =1660)
Q1 (n = 415) Q2 (n = 415) Q3 (n = 415) Q4 (n = 415)
In-hospital mortality
 Number of deaths 5 6 17 44
 Mortality,% 1.2 1.4 4.1 10.6
Mortality, OR (95% CI)
 Model 1: unadjusted 1 [reference] 1.20 (0.36–3.97) 3.50 (1.28–9.58)* 9.72 (3.81–24.78)**
 Model 2: adjusted for age, sex, Killip class, and left ventricular ejection fraction 1 [reference] 1.16 (0.25–5.26) 3.04 (0.84–11.02) 7.86 (2.34–26.31)**
 Model 3: adjusted for comorbidities and GFR 1 [reference] 1.13 (0.32–3.99) 3.17 (1.23–7.86)* 8.64 (3.72–22.93)**
 Model 4: adjusted for all covariatesa 1 [reference] 1.14 (0.32–4.38) 3.15 (1.04–9.59)* 7.31 (2.65–20.15)**
In-hospital cardiogenic shock
 Number of events 7 12 16 47
 Event rate, % 1.7 2.9 3.9 11.3
Mortality, OR (95% CI)
 Model 1: unadjusted 1 [reference] 1.73 (0.67–4.45) 2.33 (0.95–5.74) 7.44 (3.32–16.67)**
 Model 2: adjusted for age, sex, Killip class, and left ventricular ejection fraction 1 [reference] 1.53 (0.37–6.25) 1.20 (0.31–4.65) 5.74 (1.68–19.52)**
 Model 3: adjusted for comorbidities and GFR 1 [reference] 1.65 (0.66–4.32) 2.19 (0.96–4.89) 6.93 (3.08–15.59)**
 Model 4: adjusted for all covariatesa 1 [reference] 1.68 (0.65–4.38) 1.87 (0.74–4.72) 4.58 (1.98–10.60)**
3-year mortality
 Number of deaths 10 13 35 79
 Mortality, % 2.4 3.1 8.4 19.0
Mortality, HR (95% CI)
 Model 1: unadjusted 1 [reference] 1.30 (0.57–2.97) 3.56 (1.76–7.19)** 8.44 (4.37–16.30)**
 Model 2: adjusted for age, sex, Killip class and left ventricular ejection fraction 1 [reference] 1.28 (0.51–3.18) 2.75 (1.24–6.12)* 7.01 (3.33–14.75)**
 Model 3: adjusted for comorbidities and GFR 1 [reference] 1.23 (0.53–2.87) 3.19 (1.67–6.67)* 7.49 (3.39–15.41)**
 Model 4: adjusted for all covariatesa 1 [reference] 1.15 (0.45–2.94) 2.50 (1.10–5.68)* 6.03 (3.10–12.97)**
GFR, glomerular filtration rate; HR, hazard ratio; OR, odds ratio.
aIncludes demographics (age, sex); first measurement during hospitalization of the following laboratory values (admission glomerular filtration rate calculated by Cockcroft–Gault, blood urea nitrogen, glucose, white blood cell count, hematocrit, platelet count); Killip class and left ventricular ejection fraction; chest pain and door-to-balloon period; comorbidities (diabetes, chronic kidney disease, hypertension); medications during hospitalization.
*P<0.05.
**P<0.005.

F1
Fig. 1:
ROC analysis showed that the best cutoff value of the Systemic Immune-Inflammatory Index to predict the in-hospital mortality was 1781 with 66% sensitivity and 74% specificity (AUC, 0.75; 95% CI,0.69–0.81; P < 0.001). The discriminative value of SII was higher than NLR. AUC, area under the curve; NLR, neutrophil to lymphocyte ratio; ROC, receiver operating characteristic; SII, systemic immune-inflammation.
F2
Fig. 2:
Kaplan–Meier curve for overall survival in patients with ST elevation myocardial infarction (STEMI) (n = 1660) stratified by Systemic Immune-Inflammatory Index level.

The restricted cubic spline analysis depicts hazard ratios for all-cause mortality as a function of SII in continuous fashion (Fig. 3). We observed the curve appears to plateau in all-cause mortality with SII, up to a value of 3000. Thereafter, a nearly linear increase was seen in all-cause mortality with SII above this level (Fig. 3).

F3
Fig. 3:
Restricted cubic spline analysis of all-cause mortality as a function of Systemic inflammatory Index (SII). Black line indicates hazard ratio of all-cause mortality, and gray shading demonstrates 95% confidence interval. A nearly linear increase was seen in all-cause mortality with SII above 3000.

Figure 4a–e shows the distribution of SII, NLR and platelet, neutrophil and lymphocyte count.

F4
Fig. 4:
(a–e). The distribution plots of SII, NLR and Platelet, neutrophil and lymphocyte counts. NLR, neutrophil to lymphocyte ratio; SII, systemic immune-inflammation.

Table 6 demonstrated the univariate and multivariable Cox regression analysis for 3-year mortality and included the neutrophil count, lymphocyte count, platelet count, NLR and SII as confounders. In univariate analysis, whole of these confounders were associated with 3-year mortality. In multivariable analysis neutrophil count, platelet count and SII were found to be independently associated with 3-year mortality.

Table 6 - Univariate analysis and multivariate model for 3-year mortality
Univariate analysis P value HR (95% CI) Multivariate analysis P value HR (95% CI)
Neutrophil count <0.001 1.14 (1.11–1.18) Neutrophil count 0.031 1.04 (1.00–1.09)
Lymphocyte count <0.001 0.51 (0.40–0.65) Lymphocyte count
Platelet count 0.007 1.00 (1.00–1.00) Platelet count 0.024 0.99 (0.99–1.00)
NLR <0.001 1.16 (1.12–1.19) NLR
SII <0.001 1.00 (1.00–1.00) SII <0.001 1.00 (1.00–1.00)
CI, confidence interval; HR, hazard ratio; NLR, neutrophil to lymphocyte ratio; SII, systemic immune inflammation index

According to the performance of SII and its components in Cox regression analysis, SII, NLR and Platelet count were independently associated with 3-year death (Table 7).

Table 7 - The performance of systemic immune inflammation index, neutrophil to lymphocyte ratio and platelet count
Model-SII*** Model NLR and platelet count***
Variable* HR, CI-95% P-value HR, CI 95% P-value
SII (increase from 679–1942)** 3.38 (2.17–5.27) <0.001
NLR (increase from 3.08–7.67)** 2.43 (1.63–3.63) <0.001
Platelet count (increase from 202–283) mm3)** 1.44 (1.11–1.85) 0.022
*SII, platelet and NLR used with restrictive cubic spline with 4 knots.
**Regression coefficient of SII, NLR and platelet count given as increase from 25th to 75th percentile.
***These 2 models adjusted for age gender DM, HT, CKD, GFR, Killip class and HGB.
CI, confidence interval; CKD, chronic kidney disease; DM, diabetes mellitus; GFR, glomerular filtration rate; HGB, hemoglobin; HR, hazard ratio; HT, hypertension; NLR, neutrophil to lymphocyte ratio; SII, systemic immune-inflammation.

The models were compared according to Adjusted R2, the assessment of fit (likelihood ratio Chi-square) and quality [akaike information criterion (AIC)]; the model-SII adjusted R2 with likelihood R2 value higher(higher is better), in addition model quality AIC value low (lower is better) (Table 8).

Table 8 - The models performance comparison between model systemic immune-inflammation index and model-neutrophil to lymphocyte ratio with platelet
Adjusted R2 Likelihood ratio X2 AIC
Model-SII 0.128 155.7 1869
Model-NLR with platelet 0.104 151.1 1922
AIC, akaike information criterion; NLR, neutrophil to lymphocyte ratio; SII, systemic immune-inflammation.

Discussion

To the best of our knowledge, this is the first study representing the value of SII in predicting in-hospital and long-term mortality in patients with STEMI who underwent pPCI. In-hospital cardiogenic shock, acute respiratory failure, acute kidney injury, ventricular arrhythmia, stent thrombosis, recurrent MI, major adverse cardiac events and mortality were significantly found to be higher in the high SII groups (Q3 and Q4). Furthermore, the long-term mortality rate was significantly higher in the high SII groups.

STEMI results from coronary plaque rupture, followed by thrombus accumulation resulting in occlusion of a coronary artery leading to myocardial damage [22]. Inflammation and thrombosis play a key role in the initiation and progression of this process and its poor consequences. Among many inflammatory factors that have been investigated as determinants of the prognosis of MI, high neutrophil count in the blood has been found to be particularly important [23]. They are the first inflammatory cells involved in plaque formation, attracting leukocytes, promoting foam cell formation [24]. It has also been shown that peripheral lymphopenia during MI may be a sign of severe response to this disease and high levels of cortisol secretion in the body. Higher platelet counts may lead to worse outcomes by demonstrating increased release of inflammatory mediators, increased platelet activation leading to a destructive inflammatory response, prothrombotic state and a higher tendency to form platelet-rich thrombus in atherosclerotic plaques [25].

NLR and PLR have been introduced as novel markers. NLR and PLR have been reported to be independent predictors of cardiovascular events and mortality in STEMI [8–11,26]. A meta-analysis evaluated 14 studies and demonstrated that a high NLR value was significantly associated with a higher risk of death [27,28]. Furthermore, a meta-analysis, which evaluated 11 studies demonstrated that in STEMI patients who were treated with pPCI, higher PLR before the procedure was associated with poor in-hospital and long-term prognosis [29–31]. Çiçek et al. [29] had demonstrated for the first time that the combined use of NLR and PLR had short- and long-term prognostic significance in patients undergoing pPCI for STEMI Although these indexes were useful on their own, it was considered that it would be much more useful to interprete them together.

Recently, SII has been developed as a new index based on circulating platelets, neutrophils and lymphocytes. It has been widely reported that the index is associated with poor outcomes in patients with a variety of malignancies [12–17]. It has also been stated that SII may be associated with adverse outcomes suggesting potential effects on chronic heart failure and CAD [18,19]. In a study authored by Yang, et al. [19] the higher SII was independently found to be associated with a higher future risk of developing future cardiac death, nonfatal MI, nonfatal stroke or hospitalization for heart failure in patients with CAD [19].

This study had several limitations. First, it was a single-center, retrospective study that did not involve randomization and was therefore subject to selection bias. However, consecutive patients were selected to reduce the possible effects of selection bias. Second, inflammatory markers such as CRP, interleukin-6 or thromboxane A2 were not included in this study. Third, long-term mortality was obtained from the National Death Declaration system and we could not reach the specific cause of cardiovascular and/or noncardiovascular deaths.

The evaluation of these items might have strengthened the study, but this is unlikely to be applicable in all routine STEMI patients. Despite these limitations, we present the first study that has focused on the predictive value of SII in the settings of STEMI.

In conclusion, the results of the present study suggest that significant prognostic information can be obtained from routine blood test results in patients undergoing pPCI for STEMI. This study showed that high SII values at admission were independently associated with in-hospital MACE and long-term death in STEMI patients. SII improved risk prediction of major cardiovascular events than traditional risk factors in patients with STEMI who underwent pPCI. The index could be used as an easy and practical indicator to identify high-risk STEMI patients after pPCI.

Acknowledgements

All authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.

Conflicts of interest

There are no conflicts of interest.

References

1. Yusuf S, Reddy S, Ounpuu S, Anand S. Gloal burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization. Circulation 2001; 104:2746–2753.
2. Ibanez B, James S, Agewall S, Antunes MJ, Bucciarelli-Ducci C, Bueno H, et al.; ESC Scientific Document Group. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: the Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J 2018; 39:119–177.
3. Morrow DA, Antman EM, Charlesworth A, Cairns R, Murphy SA, de Lemos JA, et al. TIMI risk score for ST-elevation myocardial infarction: a convenient, bedside, clinical score for risk assessment at presentation: an intravenous nPA for treatment of infarcting myocardium early II trial substudy. Circulation 2000; 102:2031–2037.
4. Fox KA, Dabbous OH, Goldberg RJ, Pieper KS, Eagle KA, Van de Werf F, et al. Prediction of risk of death and myocardial infarction in the six months after presentation with acute coronary syndrome: prospective multinational observational study (GRACE). BMJ 2006; 333:1091.
5. Koenig W, Khuseyinova N. Biomarkers of atherosclerotic plaque instability and rupture. Arterioscler Thromb Vasc Biol 2007; 27:15–26.
6. Oh TJ, Ahn CH, Kim BR, Kim KM, Moon JH, Lim S, et al. Circulating sortilin level as a potential biomarker for coronary atherosclerosis and diabetes mellitus. Cardiovasc Diabetol 2017; 16:92.
7. Yu P, Zhao J, Jiang H, Liu M, Yang X, Zhang B, et al. Neural cell adhesion molecule-1 may be a new biomarker of coronary artery disease. Int J Cardiol 2018; 257:238–242.
8. Hartopo AB, Puspitawati I, Setianto BY. On-admission high neutrophil to lymphocyte ratio as predictor of in-hospital adverse cardiac event in ST-elevation myocardial infarction. Acta Med Indones 2015; 47:3–10.
9. Han YC, Yang TH, Kim DI, Jin HY, Chung SR, Seo JS, et al. Neutrophil to lymphocyte ratio predicts long-term clinical outcomes in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. Korean Circ J 2013; 43:93–99.
10. Ozcan Cetin EH, Cetin MS, Aras D, Topaloglu S, Temizhan A, Kisacik HL, Aydogdu S. Platelet to lymphocyte ratio as a prognostic marker of in-hospital and long-term major adverse cardiovascular events in ST-segment elevation myocardial infarction. Angiology 2016; 67:336–345.
11. Toprak C, Tabakci MM, Simsek Z, Arslantas U, Durmus HI, Ocal L, et al. Platelet/lymphocyte ratio was associated with impaired myocardial perfusion and both in-hospital and long-term adverse outcome in pa- tients with ST-segment elevation acute myocardial infarc- tion undergoing primary coronary intervention. PostepyKardiolInterwencyjnej 2015; 11:288–97.
12. Hu B, Yang XR, Xu Y, Sun YF, Sun C, Guo W, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res 2014; 20:6212–6222.
13. Yang R, Chang Q, Meng X, Gao N, Wang W. Prognostic value of Systemic immune-inflammation index in cancer: a meta-analysis. J Cancer 2018; 9:3295–3302.
14. Zhong JH, Huang DH, Chen ZY. Prognostic role of systemic immune-inflammation index in solid tumors: a systematic review and meta-analysis. Oncotarget 2017; 8:75381–75388.
15. Zhang Y, Lin S, Yang X, Wang R, Luo L. Prognostic value of pretreatment systemic immune-inflammation index in patients with gastrointestinal cancers. J Cell Physiol 2019; 234:5555–5563.
16. Yongfang J, Wang H. Prognostic prediction of systemic immune-inflammation index for patients with gynecological and breast cancers: a meta-analysis. World J Surg Oncol 2020; 18: 1–11.
17. Zhang Y, Chen B, Wang L, Wang R, Yang X. Systemic immune-inflammation index is a promising noninvasive marker to predict survival of lung cancer: a meta-analysis. Medicine 2019; 98:3.
18. Seo M, Yamada T, Morita T. Prognostic value of systemic immune-inflammation index in patients with chronic heart failure. Eur Heart J 2018; 39:ehy564.P589.
19. Yang YL, Wu CH, Hsu PF, Chen SC, Huang SS, Chan WL, et al. Systemic immune-inflammation index (SII) predicted clinical outcome in patients with coronary artery disease. Eur J Clin Invest 2020; 50:e13230.
20. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976; 16:31–41.
21. Hicks KA, Mahaffey KW, Mehran R, Nissen SE, Wiviott SD, Dunn B, et al.; Standardized Data Collection for Cardiovascular Trials Initiative (SCTI). 2017 Cardiovascular and stroke endpoint definitions for clinical trials. Circulation 2018; 137:961–972.
22. Davies MJ, Thomas AC. Plaque fissuring–the cause of acute myocardial infarction, sudden ischaemic death, and crescendo angina. Br Heart J 1985; 53:363–373.
23. Guasti L, Dentali F, Castiglioni L, Maroni L, Marino F, Squizzato A, et al. Neutrophils and clinical outcomes in patients with acute coronary syndromes and/or cardiac revascularisation. A systematic review on more than 34,000 subjects. Thromb Haemost 2011; 106:591–599.
24. Hansen PR. Role of neutrophils in myocardial ischemia and reperfusion. Circulation 1995; 91:1872–1885.
25. Balta S, Ozturk C. The platelet-lymphocyte ratio: a simple, inexpensive and rapid prognostic marker for cardiovascular events. Platelets 2014; 26:1–2.
26. Erkol A, Oduncu V, Turan B, Kiliçgedik A, Karabay CY, Akgün T, et al. Neutrophil to lymphocyte ratio in acute ST-segment elevation myocardial infarction. Am J Med Sci 2014; 348:37–42.
27. Dentali F, Nigro O, Squizzato A, Gianni M, Zuretti F, Grandi AM, Guasti L. Impact of neutrophils to lymphocytes ratio on major clinical outcomes in patients with acute coronary syndromes: a systematic review and meta-analysis of the literature. Int J Cardiol 2018; 266:31–37.
28. Dong G, Huang A, Liu L. Platelet-to-lymphocyte ratio and prognosis in STEMI: a meta-analysis. Eur J Clin Invest 2021; 51:e13386.
29. Çiçek G, Açikgoz SK, Bozbay M, Altay S, Uğur M, Uluganyan M, Uyarel H. Neutrophil-lymphocyte ratio and platelet-lymphocyte ratio combination can predict prognosis in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. Angiology 2015; 66:441–447.
30. Cersit S, Cay S, Koza Y, Acikgoz SK, Cabuk G, Senturk B, Dogan P. Association between plasma fibrinogen level and saphenous vein graft patency. Acta Cardiol Sin 2014; 30:223–228.
31. Cerşit S, Gündüz S, Ozan Gürsoy M, Karakoyun S, Kalçik M, Bayam E, et al. Relationship between pulmonary venous flow and prosthetic mitral valve thrombosis. J Heart Valve Dis 2018; 27:65–70.
Keywords:

cardiovascular outcomes; primary percutaneous coronary intervention; ST segment elevation myocardial infarction; Systemic immune inflammation index

Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.