
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12517-0
10.1016/j.heliyon.2024.e36486
e36486
Research Article
Systemic immune inflammation index is associated with in-stent neoatherosclerosis and plaque vulnerability: An optical coherence tomography study
Sheng Jin 1
Yang Shuangya 1
Gu Ning
Deng Chancui
Shen Youcheng
Xia Qianhang
Zhao Yongchao
Wang Xi
Deng Yi
Zhao Ranzun kouke80@126.com
⁎⁎
Shi Bei shibei2147@163.com
⁎
Department of Cardiology, Affiliated Hospital of Zunyi Medical University, Zunyi, China
⁎ Corresponding author. shibei2147@163.com
⁎⁎ Corresponding author. kouke80@126.com
1 Jin Sheng and Shuangya Yang are co-first authors.

17 8 2024
30 8 2024
17 8 2024
10 16 e364866 6 2024
4 8 2024
16 8 2024
© 2024 Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

In-stent neoatherosclerosis (ISNA) is identified as the primary cause of in-stent restenosis (ISR). The systemic immune inflammation index (SII), shows promise for predicting post-percutaneous coronary intervention (PCI) adverse cardiovascular events and is associated with coronary stenosis severity; however, its specific relationship with ISNA remains unclear. This study aimed to investigate the association between the SII and ISNA after drug-eluting stent (DES) implantation.

Methods

This cross-sectional study included 195 participants with 195 ISR lesions who underwent optical coherence tomography (OCT)-guided PCI between August 2018 and October 2022. Participants were categorized based on the SII levels into Tertile 1 (SII <432.37, n = 65), Tertile 2 (432.37 ≤ SII ≤751.94, n = 65), and Tertile 3 (SII >751.94, n = 65). Baseline Clinical, angiographic, and OCT characteristics were analyzed. The association of the SII with ISNA and thin-fibroatheroma (TCFA) was investigated using univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic accuracy of the SII in detecting ISNA and TCFA.

Results

Patients in Tertile 3 had a significantly higher incidences of ISNA and TCFA than did those in Tertile 1. Logistic regression analysis revealed the SII is an independent indicator of ISNA and TCFA in ISR lesions (P = 0.045 and P = 0.002, respectively). The areas under the ROC curves for ISNA and TCFA were 0.611 and 0.671, respectively.

Conclusion

The SII is associated with ISNA and TCFA and may serve as an independent indicator in patients with ISR.

Keywords

Optical coherence tomography
In-stent restenosis
In-stent neoatherosclerosis
Systemic immune inflammation index
Thin-cap fibroatheroma
==== Body
pmcAbbreviations and acronyms

SII = systemic immune inflammation index

ISNA = in-stent neoatherosclerosis

DES = drug-eluting stent

ISR = in-stent restenosis

OCT = optical coherence tomography

TCFA = thin fibrous caps

AUC = area under the receiver operating characteristic curve

CAD = coronary artery disease

HDL = high-density lipoprotein cholesterol

SES = sirolimus-eluting stent

STEMI = ST-elevation myocardial infarction

MSA = minimal stent area

ACS = acute coronary syndrome

MLA = minimum lumen area

FCT = fiber cap thickness

MACE = major adverse cardiac events

PCI = percutaneous coronary intervention

TC = total cholesterol

ox-LDL = oxidized low-density lipoprotein

LDL = low-density lipoprotein

CHD = coronary heart disease

CAG = coronary angiography

NIH = neointimal hyperplasia

AS = atherosclerosis

1 Introduction

In-stent restenosis (ISR) after drug-eluting stents (DES) implantation and late thrombosis in patients with coronary artery disease (CAD) following percutaneous coronary intervention (PCI) can result in serious cardiovascular events. Recent imaging and pathological findings suggest that in-stent neoatherosclerosis (ISNA) plays a significant role in late stent failure [1,2]. The development of ISNA mirrors that of primary atherosclerosis (AS), which is characterized by chronic inflammation and the accumulation of various inflammatory cells, including macrophages, neutrophils, lymphocytes, and platelets [3,4]. Both conditions exhibit features such as necrotic cores, intraplaque hemorrhage, and thin-cap fibroatheroma (TCFA) with foam cell infiltration. However, unlike AS, which requires decades to develop, ISNA progresses rapidly after stent implantation [5]. Neutrophils-to-lymphocytes and platelets-to-lymphocytes ratios have emerged as important indicators for assessing inflammation and immune status, aiding in the early detection of adverse clinical events [6,7]. These ratios have shown promise in predicting coronary artery stenosis and are correlated with poor prognosis in myocardial infarction cases [8,9]. For instance, Karabağ et al. found that the C-reactive protein/albumin ratio (CAR) was associated with CAD severity in patients with stable angina, suggesting its prognostic potential [10]. Similarly, Karakayali et al. identified the HALP (hemoglobin, albumin, lymphocytes, and platelets) score as a predictor of in-hospital mortality in ST-segment elevation myocardial infarction (STEMI), highlighting the importance of inflammation and nutrition in cardiovascular outcomes [11]. Routine blood tests, such as the systemic immune-inflammation index (SII), calculated from neutrophil, lymphocyte, and platelet counts, are cost-effective and accessible. The SII is associated with atherosclerosis and predicts coronary artery stenosis [12,13]. The early detection of ISNA is crucial for improving patient management and reducing cardiovascular events. The correlation between the SII and inflammation makes it a promising candidate for early detection. However, the association of the SII with ISNA and TCFA in patients with ISR, assessed using optical coherence tomography (OCT), remains unexplored. Therefore, this study aims to use OCT to explore the relationship between the SII and ISNA in patients with DES-ISR, enhancing our understanding of this disease.

2 Materials and methods

2.1 Study population and procedures

A retrospective analysis was conducted on 195 patients with ISR after PCI who were admitted to the Department of Cardiology at Zunyi Medical University Affiliated Hospital between August 2018 and October 2022. These patients underwent coronary angiography (CAG) and OCT. The inclusion criteria were previous DES implantation and CAG revealing in-stent restenosis, followed by OCT examination. The exclusion criteria included acute heart failure, cardiogenic shock, severe arrhythmia during surgery, heavy thrombus load, tortuous vessels in the stent segment, and severe vascular calcification, all of which could impede OCT imaging. Additionally, patients with newly implanted stents (within the previous 6 months), missing medical record data, unclear OCT images, poor imaging quality, chronic inflammatory diseases, tumors, hematologic disorders, and severe liver or renal diseases were excluded. This study was approved by the Human Research Committee of the Affiliated Hospital of Zunyi Medical University (approval number: [2024] No. 1–205).

ISR is diagnosed using CAG, which identifies lumen stenosis ≥50 % within a stent or within 5 mm of its proximal and distal ends. Neoatherosclerosis, assessed by OCT, includes lipid-rich plaques such as TCFAs, with or without intimal rupture and/or thrombus. It also encompasses calcified plaques that may or may not exhibit neovascularization and/or macrophage accumulation. Following the application of strict inclusion and exclusion criteria, this study included 195 ISR patients with 195 lesions, all of whom underwent OCT-guided intervention (Fig. 1). The 195 patients with ISR were divided into three groups based on the SII tertiles: Tertile 1 (SII <432.37, n = 65), Tertile 2 (432.37 ≤ SII ≤751.94, n = 65), and Tertile 3 (SII >751.94, n = 65).Fig. 1 Study flowchart A total of 195 ISR lesions in 195 patients were included in the final analysis. According to their SII tertile, the patients were divided into three groups (Tertile 1: SII<432.37, n = 65; Tertile 2: 432.37≤SII≤751.94, n = 65; or Tertile 3: SII>751.94, n = 65). ISR = in-stent restenosis; CAG = coronary angiography; OCT = optical coherence tomography.

Fig. 1

2.2 Patient characteristics and angiographic analysis

Patient demographic data, such as gender, age, and past medical history, were collected. Clinical data, including white blood cell count, lymphocyte count, platelet count, and neutrophil counts, endogenous creatinine clearance rate, triglycerides, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein (LDL) cholesterol, and albumin, were also recorded. Blood samples were collected from patients after a 12-h fast and analyzed in the central laboratory of the Affiliated Hospital of Zunyi Medical University using standard procedures. The SII was calculated based on the collected data as SII = PLT × NEU/LYM, where PLT represents platelet count, NEU represents neutrophil count, and LYM represents lymphocyte count.

CAG was performed by skilled cardiovascular interventionalists via the radial artery using the Seldinger technique to puncture and insert a 6F sheath. Coronary vessels stenosis was assessed from various angles, and the CAG findings were evaluated by two interventionalists using computer quantification. QAngio XA software (https://medicalit.hr/solutions/medis) was used for the analysis.

2.3 OCT image acquisition and analysis

OCT examination of the patient's restenotic vessel in the stent was conducted using the ILUMIENTMOPTIS system and the DragonflyTM image pipeline. ISR imaging was performed at a retraction speed of 20 mm/s using an automatic pullback device to capture detailed images of the narrowed blood vessels. The acquired images were measured and analyzed using a C7-XRTM OCT Intravascular Imaging System. Two technicians, blinded to the clinical data and CAG results, conducted offline software (Illumina Optis, St. Jude Medical) analysis to identify plaque types and coronary artery wall microstructures, such as fibrous, lipid, stratified, and calcified plaques, plaque rupture, TCFA, macrophage infiltration, microvessels, thrombus, and late stent malapposition (Fig. 2). Late stent malapposition was defined as stents implanted for over 1 year with a gap between the stent struts and the vessel wall of >200 μm (0.2 mm) and no neointimal tissue covering the stent struts. TCFA was defined as a fibrous cap thickness ≤65 μm and lipid curvature ≥180°. Two experienced technicians who were blinded to the patients' clinical and angiographic details independently evaluated the OCT images. Any disagreements were resolved by a third physician, who re-examined the original OCT images using an offline review workstation (Illumina Optis, St. Jude Medical).Fig. 2 Typical optical coherence tomography images showcasing various vascular morphologies Various classical images of OCT. (A) Homogenous hyperplasia, high intensity. (B) Heterogeneous, low-intensity. (C) Layered, low-intensity. (D) Calcified plaque. (E) Lipid plaques, TCFA. (F) Thrombus. (G) Microvessels (arrow). (H) Macrophages(arrow). (I) Late stent Malapposition (arrow). OCT = optical coherence tomography; ISR = in-stent restenosis; TCFA = thin-cap fibroatheroma.

Fig. 2

2.4 Statistical analysis

Data were analyzed using SPSS (version 29.0; IBM Corp., Armonk, NY, USA). Normally distributed quantitative data were presented as means ± standard deviations and were compared using independent samples t-tests. Non-normally distributed quantitative data were presented as medians (P25, P75) and were compared using the Mann-Whitney U test. Categorical data were expressed as numbers (percentages) and compared using the chi-square test. The association of the SII with ISNA and TCFA was analyzed using Spearman's correlation analysis. Univariate and multivariate logistic regression analyses were conducted to investigate the independent factors influencing ISNA and TCFA in patients with ISR. The predictive ability of the SII for ISNA and TCFA was evaluated using the receiver operating characteristic (ROC) curves. A P-value <0.05 was considered statistically significant.

3 Results

3.1 Patient characteristics

We excluded 161 patients who lacked complete clinical data and those with infectious or autoimmune diseases that could have influenced inflammation indicators. Compared with patients in Tertile 1, those in Tertile 3 had significantly higher white blood cell count (5.60 [4.77, 6.58] vs. 8.71 [7.33, 11.07], respectively; P < 0.001), NEU (3.33 [2.64, 3.96] vs. 6.27 [5.27, 8.47], respectively; P < 0.001), PLT (163.23 [57.81] vs. 235.34 [81.24], respectively; P < 0.001), total cholesterol (3.74 [3.23, 4.71] vs. 4.21 [3.67, 5.38], respectively; P = 0.007), and LDL cholesterol (2.07 [1.75, 2.68] vs. 2.51 [2.08, 3.16], respectively; P = 0.007) (Table 1).Table 1 Baseline characteristics.

Table 1	Tertile 1a (n = 65) (＜432.37)	Tertile 2a (n = 65) (432.37–751.94)	Tertile 3a (n = 65) (＞751.94)	P Value	
Age, M(SD), years	64.28(10.68)	64.09(10.93)	63.29(9.92)	0.852	
Male, n(%)	51(78.5)	52(80)	49(75.4)	0.812	
Current smoker, n(%)	24(36.9)	29(44.6)	25(38.5)	0.638	
Hypertension, n(%)	40(61.5)	41(63.1)	44(67.7)	0.748	
Diabetes, n(%)	17(26.2)	19(29.2)	14(21.5)	0.600	
Hyperlipidemia, n(%)	16(24.6)	15(23.1)	18(27.7)	0.826	
Laboratory fingdings	
White blood cells, M(IQR), × 109/L	5.60 (4.77, 6.58)	4.70 (4.01, 5.53)	6.27 (5.27，8.47)	＜0.001	
Neutrophil, M(IQR), × 109/L	3.33 (2.64, 3.96)	4.44 (3.91, 5.01)	6.77 (5.59, 8.79)	＜0.001	
Hemoglobin, M(SD), g/L	137.03(17.02)	140.06(18.74)	139.98(19.18)	0.563	
Platelet, M(IQR), × 109/L	163.23(57.81)	205.94 (51.69)	235.34 (81.24)	＜0.001	
Total cholesterol, M(IQR), mmol/L	3.74 (3.23, 4.71)	3.88 (3.21, 4.84)	4.21 (3.67, 5.38)	0.007	
Triglycerides, M(IQR), mmol/L	1.58 (1.16, 2.25)	2.41 (1.49, 3.74)	1.72 (1.17, 2.59)	0.807	
HDL-C, M(IQR), mmol/L	1.11 (0.94, 1.24)	1.06 (0.89, 1.19)	1.13 (0.94, 1.28)	0.238	
LDL-C, M(IQR), mmol/L	2.07 (1.75, 2.68)	2.31 (1.87, 2.82)	2.51 (2.08, 3.16)	0.005	
eGRF, M(IQR), ml/min/1.73m2	77.75 (24.79)	74.29 (27.73)	80.56 (28.26)	0.756	
Medicine use	
Aspirin, n(%)	61(93.8)	64(98.5)	60(92.3)	0.254	
Clopidogrel, n(%)	58(89.2)	60(92.3)	58(89.2)	0.792	
Statin, n(%)	57(87.7)	58(89.2)	59(90.8)	0.852	
a Tertiles 1, 2, and 3 are determined based on the Systemic immune inflammation index (SII, platelets × neutrophils/lymphocytes). M(SD) = mean (stand deviation); M(IQR) = median (interquartile range); HDL-C = high-density lipoprotein cholesterol; LDL-C = low-density lipoprotein cholesterol; eGFR = estimated glomerular filtration rate.

3.2 Angiographic results

The angiographic findings are summarized in Table 2. No significant differences were observed between the groups.Table 2 Angiography characteristics.

Table 2	Tertile 1a (n = 65)
(＜432.37)	Tertile 2a (n = 65)
(432.37–751.94)	Tertile 3a (n = 65)
(＞751.94)	P Value	
Time since implantation, M(IQR), years	3(1, 5)	3(1, 7)	3(1, 5)	0.897	
DES typesb				ns	
paclitaxel	0(0.0)	0(0.0)	0(0.0)		
sirolimus	65(100.0)	65 (100.0)	65 (100.0)		
Culprit vessel	
Left anterior descending, n(%)	44(67.7)	39(60)	43(66.2)	0.624	
Circumflex, n(%)	2(3.1)	8(12.3)	6(9.2)	0.149	
Right, n(%)	18(27.7)	17(26.2)	16(24.6)	0.923	
Other, n(%)	1(1.5)	1(1.5)	0(0.0)	0.603	
Lesion Characteristics	
Bifurcation(＞1.5 mm), n(%)	12(18.5)	19(29.2)	14(21.5)	0.324	
Ostial location, n(%)	6(9.2)	5(7.7)	4(6.2)	0.805	
In-stent restenosis patternc	
Type Ⅰ, n(%)	17(26.2)	23(35.4)	16(24.6)	0.341	
Type Ⅱ, n(%)	18(27.7)	11(16.9)	16(24.6)	0.324	
Type Ⅲ, n(%)	19(29.2)	13(20)	15(23.1)	0.456	
Type Ⅳ, n(%)	11(16.9)	18(27.7)	18(27.7)	0.253	
a Tertiles 1, 2, and 3 are determined based on the Systemic immune inflammation index (SII, platelets × neutrophils/lymphocytes). DES = Drug-eluting stents; M(IQR) = median (interquartile range).

b The type of DES is only sirolimus-eluting stents.

c In-stent restenosis pattern was defined as per Mehran's classification.

3.3 OCT findings

The OCT measurements of the neointimal indicators within the stents are presented in Table 3. Qualitative assessment revealed significant variability in the distribution of ISR stenotic tissue structure patterns among the three tertiles (Fig. 3). Tertile 3 presented more cases of heterogeneous neointima (56.9 % [n = 37] vs. 32.3 % [n = 21], P = 0.034), TCFA (36.9 % [n = 24] vs. 10.8 % [n = 7], P = 0.034), and ISNA (58.5 % [n = 38] vs. 36.9 % [n = 24], P = 0.045) than Tertile 1 did. The incidences of ISNA and TCFA increased with increasing SII values. Moreover, patients in tertile 3 showed a higher prevalence of lipid plaques (61.5 % [n = 40] vs. 35.4 % [n = 23]; P = 0.008), thrombus (52.3 % [n = 34] vs. 26.2 % [n = 17]; P = 0.008), plaque erosion (50.8 % [n = 33] vs. 27.7 % [n = 18]; P = 0.016), and neointimal macrophages (32.3 % [n = 21] vs. 9.2 % [n = 6]; P = 0.006), whereas fibrous plaque (38.5 % [n = 25] vs. 63.1 % [n = 41]; P = 0.016) was less frequently identified. Our findings indicated no statistically significant differences in calcific plaques, spotty calcifications, microvessels, or late stent malapposition among the tertiles. Notably, Tertile 3 exhibited increased macrophage infiltration (P < 0.05) and a higher prevalence of vulnerable plaques, suggesting an elevated risk of future cardiovascular events.Table 3 ISR characteristics evaluated by optical coherence tomography.

Table 3	Tertile 1a (n = 65)
(＜432.37)	Tertile 2a (n = 65)
(432.37–751.94)	Tertile 3a (n = 65)
(＞751.94)	P Value	
Quantitative assessment	
Distal reference lumen area, M(IQR), mm2	4.96 (3.61, 6.12)	4.48 (3.31, 5.31)	5.04 (3.62, 6.30)	0.131	
Distal reference lumen diameter, M(IQR), mm	2.47 (2.18, 2.74)	2.32 (1.90, 2.58)	2.57 (2.12, 2.97)	0.013	
Proximal reference lumen area, M(IQR), mm2	8.57 (6.44, 9.57)	7.61 (6.77, 8.97)	7.54 (6.14, 9.30)	0.420	
Proximal reference lumen diameter, M(IQR), mm	3.25 (2.87, 3.48)	3.09 (2.93, 3.38)	3.18 (2.81, 3.45)	0.645	
Minimum lumen area, M(IQR), mm2	1.78 (1.38, 2.24)	1.61 (1.35, 2.17)	1.49 (1.09, 2.26)	0.196	
Minimum lumen diameter, M(IQR), mm	1.48 (1.29, 1.65)	1.40 (1.29, 1.64)	1.33 (1.13, 1.62)	0.190	
Minimum stent area, M(IQR), mm2	6.96 (5.48, 8.15)	5.98 (4.69, 7.27)	6.37 (4.68, 7.82)	0.051	
Minimum stent diameter, M(IQR), mm	2.85 (2.53, 3.11)	2.65 (2.34, 2.95)	2.72 (2.35, 3.11)	0.061	
Maximal NIH,%	62(95.4)	63 (96.9)	61 (93.8)	0.705	
Qualitative assessment	
Neoatherosclerosis, n(%)	24(36.9)	33(50.8)	38(58.5)	0.045	
Restenotic tissue structure				0.034	
Homogeneous, n(%)	39(60.0)	28(43.1)	21(32.3)		
Heterogeneous, n(%)	21(32.3)	30(46.2)	37(56.9)		
Layered, n(%)	5(7.7)	7(10.8)	7(10.8)		
Lipid plaque, n(%)	23(35.4)	36(55.4)	40(61.5)	0.008	
TCFA, n(%)	7(10.8)	19(29.2)	24(36.9)	0.002	
Calcific plaque, n(%)	6(9.2)	5(7.7)	3(4.6)	0.584	
Spotty calcification, n(%)	4(6.2)	5(7.7)	4(6.2)	0.921	
Fibrous plaque, n(%)	41(63.1)	30(46.2)	25(38.5)	0.016	
Thrombus, n(%)	17(26.2)	23(35.4)	34(52.3)	0.008	
Neointimal macrophages, n(%)	6(9.2)	15(23.1)	21(32.3)	0.006	
Microvessels, n(%)	27(41.5)	24(36.9)	22(33.8)	0.660	
stent malapposition, n(%)	8(12.3)	11(16.9)	14(21.5)	0.373	
plaque erosion, n(%)	18(27.7)	21(32.3)	33(50.8)	0.016	
a Tertiles 1, 2, and 3 are determined based on the the Systemic immune inflammation index (SII, platelets × neutrophils/lymphocytes). M(SD) = mean (stand deviation); M(IQR) = median (interquartile range); NIH: neointimal hyperplasia; TCFA: thin-cap fibroatheroma.

Fig. 3 Pattern of restenotic tissues

More homogeneous neointima (56.9 % vs. 43.1 % vs. 30.6 %) in the lower SII group; more TCFA (41.7 % vs. 26.4 % vs. 9.7 %) and ISNA (37.5 % vs. 47.2 % vs. 66.7 %) in the higher SII group (p < 0.05). TCFA: thin-cap fibroatheroma. ISNA: In-stent neoatherosclerosis. Tertiles 1, 2, and 3 are determined based on the systemic immune inflammation index (SII, platelet × neutrophil/lymphocyte ratio).

Fig. 3

3.4 ISNA prediction through ROC analysis

Several factors associated with ISNA and plaque vulnerability were investigated using univariate and multivariate analyses. Both ISNA and TCFA showed a positive correlation with SII. After adjusting for confounding factors, the SII emerged as a significant predictor of ISNA and plaque susceptibility. Univariate analysis (odds ratio [OR], 1.021; 95 % confidence interval [CI], 1.001–1.042; P = 0.044) and multivariate analysis (OR, 1.010; 95 % CI, 1.001–1.018; P = 0.025) analyses demonstrated a significant association between SII levels and ISNA incidence (Table 4). Similarly, the SII was significantly associated with the presence of TCFA in both univariate (OR, 1.006; 95 % CI, 1.002–1.010; P = 0.003) and multivariate (OR, 1.003; 95 % CI, 1.001–1.005; P = 0.002) analyses.Table 4 Univariate and multivariate logistic regression analysis to determine the independent risk factors of ISNA and TCFA.

Table 4	In-stent neoatherosclerosis	Thin-cap fibroatheroma	
Univariate ananlysis	Multivariate ananlysis	Univariate ananlysis	Multivariate ananlysis	
Odds ration (95 % CI)	P Value	Odds ration (95 % CI)	P Value	Odds ration (95 % CI)	P Value	Odds ration (95 % CI)	P Value	
Age,years	1.029(1.001–1.058)	0.043	1.031(0.996–1.067)	0.086	1.024(0.992–1.057)	0.148	1.017(0.976–1.059)	0.421	
Sex	0.893(0.453–1.759)	0.743	0.774(0.557–2.999)	0.551	0.717(0.316–1.623)	0.424	1.049(0.343–2.652)	0.928	
Current smoker	1.187(0.669–2.106)	0.559	0.801(0.625–2.495)	0.529	1.249(0.651–2.394)	0.504	0.756(0.597–2.930)	0.492	
Hypertension	0.772(0.430–1.388)	0.387	0.613(0.314–1.194)	0.150	1.261(0.637–2.496)	0.506	0.761(0.593–2.906)	0.501	
Diabetes mellitus	1.193(0.627–2.271)	0.590	1.146(0.563–2.331)	0.708	0.889(0.421–1.877)	0.758	1.197(0.364–1.919)	0.671	
eGRF, M(IQR), ml/min/1.73m2	0.989(0.979–1.000)	0.052	0.991(0.979–1.004)	0.191	0.986(0.973–0.999)	0.029	0.985(0.970–1.000)	0.057	
Triglycerides, mmol/L	0.958(0.814–1.128)	0.606	0.946(0.789–1.135)	0.552	0.955(0.774–1.177)	0.665	0.913(0.712–1.170)	0.471	
Total cholesterol, mmol/L	1.010(0.821–1.242)	0.927	1.028(0.654–1.615)	0.906	1.040(0.824–1.312)	0.742	1.075(0.661–1.751)	0.770	
HDL-C, mmol/L	0.543(0.201–1.469)	0.229	0.245(0.064–0.936)	0.040	0.384(0.109–1.346)	0.135	0.104(0.018–0.619)	0.013	
LDL-C, mmol/L	1.102(0.803–1.512)	0.548	1.187(0.614–2.295)	0.610	1.128(0.796–1.599)	0.498	1.117(0.550–2.267)	0.760	
NIH＞50 %	1.957(0.475–8.061)	0.352	0.371(0.547–13.25)	0.223	0.676(0.163–2.811)	0.591	0.499(0.095–2.633)	0.413	
Time since implantation, years	1.009(0.945–1.077)	0.799	1.004(0.936–1.078)	0.909	1.014(0.943–1.091)	0.705	1.024(0.945–1.110)	0.559	
Minimum lumen area, mm2	1.043(0.712–1.528)	0.829	1.217(0.784–1.889)	0.381	0.856(0.545–1.344)	0.499	0.876(0.532–1.443)	0.604	
SII	1.021(1.001–1.042)	0.044	1.010(1.001–1.018)	0.025	1.006(1.002–1.010)	0.003	1.003(1.001–1.005)	0.002	
HDL-C = high-density lipoprotein cholesterol; LDL-C = low-density lipoprotein cholesterol; eGFR = estimated glomerular filtration rate; NIH = neointimal hyperplasia; SII = platelets × neutrophils/lymphocytes.

The area under the curve (AUC) values for the SII in predicting ISNA and TCFA in patients with DES-ISR were 0.611 (95 % CI, 0.583–0.729; P < 0.01) and 0.671 (95 % CI, 0.568–0.756; P < 0.01), respectively. The optimal SII cut-off values for detecting ISNA and TCFA were 567.71 (sensitivity, 64.2 %; specificity, 63.0 %) and 583.65 (sensitivity, 72.0 %; specificity, 60.0 %), respectively. Additionally, our study found that patients in SII tertile 3 had greater macrophage infiltration (P < 0.05) and more vulnerable plaques compared to those in SII tertiles 1 and 2, indicating a higher risk of future cardiovascular events (Fig. 4).Fig. 4 ROC analysis ISNA, in-stent neoatherosclerosis; TCFA, thin-cap fibroatheroma.

Fig. 4

4 Discussion

To our knowledge, this is the first study to use OCT to determine the association of the SII with ISNA and TCFA in patients with DES-ISR. Our findings indicate that patients in the lowest SII tertile (Tertile 1) had significantly lower levels of inflammation and lipid-related markers than did those in the higher tertiles (Tertiles 2 and 3). Moreover, Tertile 3 was associated with a higher prevalence of lipid plaques, thrombus, neointimal macrophages, and plaque erosion, and a lower prevalence of fibrous plaques. The incidences of ISNA and TCFA were significantly higher in the Tertile 3 (Table 3). These results suggest that elevated SII levels are positively associated with increased plaque vulnerability and a higher proportion of ISNA, establishing the SII as a distinct risk factor for ISNA plaque susceptibility.

PCI is a vital treatment for ischemic heart disease; however, managing ISR remains a challenge and often leads to adverse outcomes [14]. The primary pathophysiological mechanisms underlying ISR and ISNA are chronic inflammation and delayed endothelial healing [15]. Atherosclerosis is intricately linked to the inflammatory response, in which immune processes interact with metabolic factors to initiate and propagate atherosclerotic lesions [16]. Inflammatory mediators released by neutrophils induce endothelial dysfunction and vessel wall degradation [17,18]. Platelets release chemokines, proinflammatory cytokines, and growth factors that contribute to endothelial cell dysfunction [19,20]. Neutrophils interact with platelets, proteolyze coagulation factors, release prothrombotic molecules, and facilitate monocyte infiltration, and promote atherosclerosis and thrombosis, ultimately leading to cardiovascular events [21]. In contrast, lymphocytes regulate inflammatory responses and exhibit anti-atherosclerotic effects [22]. The suppression of the inflammatory response can delay the progression of atherosclerosis and reduce cardiovascular events [23].

Several studies have shown that individual cellular components or blood biochemical indicators may not have the optimal predictive power for coronary heart disease development. Therefore, researchers have focused on the ratio of multiple indicators, such as neutrophil-to-lymphocytes and platelet-to-lymphocyte ratios. These combined indicators may offer greater value than the individual cell components or biochemical markers alone do. The CAR has been identified as an independent predictor of stent restenosis in patients with STEMI, providing a better predictive value, compared with C-reactive protein or albumin alone [24]. Similarly, the CAR has been shown to correlate with CAD severity in patients with acute coronary syndrome (ACS) [25].

Karakayali et al. demonstrated that the SII, which integrates lymphocytes, platelets, and neutrophils, is significantly associated with ischemia with non-obstructive coronary arteries (INOCA), highlighting its potential as a predictive marker [26]. The SII provides a more balanced and comprehensive evaluation of the body's immune and inflammatory responses, compared with traditional ratios involving one- or two-cell components. The SII has demonstrated predictive value and offers unique advantages over other biological markers, such as the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and C-reactive protein level [27]. Liu et al. found that the SII demonstrated superior predictive ability for coronary heart disease compared with other markers, such as the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and C-reactive protein level [12]. The SII appears to be less influenced by fluid load, conpared with the other ratios [28]. Analyzing 669 patients undergoing CAG, Candemir et al. found that the SII was an independent predictor of severe stenosis, suggesting its potential for assessing coronary atherosclerosis severity [29]. Elevated SII levels is associated with atherosclerotic lesions and better prognosis prediction in patients with ACS, indicating a link to increased inflammatory activity. The study observed higher platelet and neutrophil counts and lower lymphocyte counts in cases of elevated SII, reflecting heightened inflammatory activity. Some studies have also found that during long-term follow-up, the SII is more closely associated with cardiovascular disease in patients with hypertension or diabetes. This relationship may be attributed to endothelial dysfunction, inflammatory infiltration, and vascular remodeling resulting from chronic inflammation in patients [30,31].

Our study revealed that patients with higher SII values exhibited higher rates of neoatherosclerosis, macrophage infiltration, plaque erosion, vulnerable plaques, and stent thrombosis, indicating a poorer prognosis. Consequently, treatment adjustment may be necessary in patients with elevated SII. Closer monitoring and potentially more aggressive anti-inflammatory treatments may be beneficial for these high-risk patients. Implementing SII measurements in clinical practice may help identify patients who would benefit from tailored therapeutic strategies to mitigate inflammation and reduce the risk of adverse outcomes. Further research is warranted to explore the optimal therapeutic interventions for patients with elevated SII and to validate the clinical benefits of such approaches.

Previous studies have clearly indicated that elevated SII values play a crucial predictive role in chronic heart failure, effectively predicting poor patient outcomes [32]. High SII has been confirmed as an independent predictor of cardiovascular events, including cardiac death, nonfatal myocardial infarction, stroke, and hospitalization for heart failure [33]. This association indicates that the SII not only has predictive value in chronic heart failure but also plays a crucial role in evaluating cardiovascular risk and forecasting related events [34,35]. Emerging inflammatory biomarkers connect the three immune cell types, delineating the balance between pro- and anti-inflammatory states in the body and providing insight into the involvement of the immune system in ISNA.

4.1 Limitations

This study has some limitations. First, we did not compare the SII with conventional inflammatory markers, such as C-reactive protein and fibrinogen. Future research should consider integrating the SII with other relevant markers for a more comprehensive analysis. Second, the lack of follow-up data for our patient cohort highlights the need for further investigation into the relationship between the SII and ISNA plaque vulnerability and their potential clinical implications. Third, our study was retrospective and single-center in nature, and despite our efforts to include as many patients as possible, the sample size was limited. Although we found an initial association of the SII with ISNA and TCFA in patients with restenosis, the predictive value of the SII in this study was limited. This limitation may stem from a small sample size or differences in the mechanisms underlying ISR and de novo atherosclerosis progression, warranting further investigation. This exploratory study suggests that the SII could serve as a predictive factor. Future large-scale, prospective, multi-center studies are crucial to validate the assciation of the SII with ISNA and TCFA and to investigate anti-inflammatory treatment strategies for patients with high SII and restenosis. Despite these limitations, our study provides valuable insights that will pave the way for future research aimed at identifying novel therapeutic targets for ISNA in chronic diseases.

5 Conclusion

Patients with ISR and with higher SII levels showed increased incidences of ISNA and plaque vulnerability. Although the SII is an independent risk factor for ISNA and TCFA, larger studies are needed to fully validate its predictive value.

Ethical approval statement

The Human Research Committee of the Affiliated Hospital of Zunyi Medical University approved the study after obtaining written informed consent from all the participants ([2024] No. 1–205).

Funding

This study was supported by the Guizhou Provincial Health Commission Science and Technology Fund Project [gzwkj2024-014 ].

Data availability statement

The data associated with this study has not been deposited into a publicly available repository. However, under reasonable request, the data can be obtained by contacting the corresponding author.

CRediT authorship contribution statement

Jin Sheng: Writing – original draft, Data curation, Conceptualization. Shuangya Yang: Writing – original draft, Funding acquisition, Data curation, Conceptualization. Ning Gu: Formal analysis, Data curation, Conceptualization. Chancui Deng: Formal analysis, Data curation. Youcheng Shen: Formal analysis, Data curation. Qianhang Xia: Formal analysis, Data curation. Yongchao Zhao: Formal analysis, Data curation. Xi Wang: Formal analysis, Data curation. Yi Deng: Formal analysis, Data curation. Ranzun Zhao: Writing – review & editing, Supervision, Formal analysis, Data curation, Conceptualization. Bei Shi: Writing – review & editing, Supervision, Formal analysis, Data curation, Conceptualization.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Shuangya yang reports financial support was provided by 10.13039/100017957 Guizhou Provincial Health Commission . If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We would like to thank the 10.13039/100017957 Guizhou Provincial Health Commission for their financial support of this study (grant number gzwkj2024-014 ). We also express our gratitude to all the participants and staff involved in this study for their valuable contributions.
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