Introduction: Few studies have evaluated different patterns of in-stent restenosis by optical coherence tomography (OCT). This study aimed to identify in vivo predictors for focal restenosis in patients with in-stent restenosis (ISR). Methods: The study recruited patients with ISR who underwent OCT examination in the Cardiology Department of the Affiliated Hospital of Zunyi Medical University from October 2018 to December 2022. Based on the angiographic classification of ISR lesions, the patients were divided into two groups: the focal group (n = 58) and the non-focal group (n = 158). Results: The white blood cell count was higher in the non-focal group than those in focal type (7.8 ± 3.0 vs. 6.6 ± 2.1, p = 0.007). The prevalence of lipid-rich plaque was higher in patients with focal ISR (65.5% vs. 42.4%, p = 0.003). The occurrence of red thrombus (27.8% vs. 12.1%, p = 0.016) and white thrombus (41.1% vs. 24.1%, p = 0.021) was higher in the non-focal group. Multivariate analysis showed that low-density lipoprotein cholesterol C (odds ratio [OR]: 3.341, 95% confidence interval [CI]: 1.714–9.784, p = 0.046) was independently associated with focal restenosis. While white blood cell count (OR: 0.814, 95% CI: 0.657–0.913, p = 0.047) and stent malapposition (OR: 0.228, 95% CI: 0.057–0.896, p = 0.037) were independently associated with non-focal restenosis. Conclusion: There were significant differences in clinical baselines and OCT identified morphological characteristics in patients between focal and non-focal groups. Low-density lipoprotein cholesterol C was independently associated with focal restenosis. White blood cell count and stent malapposition were correlated with non-focal restenosis.

With the iteration of stent technology, the implication of drug-eluting stents (DES) greatly reduces the occurrence of stent failure [1]. In-stent restenosis (ISR) is one of the major complications in patients with coronary heart disease after stent implantation, and the ISR still occurs at a rate of 1%–2% per year nowadays [2]. Despite significant improvements in-stent technology in recent years, ISR remains a clinical challenge [2]. Current European guidelines recommend DES or drug coated balloon (DCB) to treat ISR with a Class I indication [3].

However, previous studies have divided restenosis lesions into focal and non-focal types. The research studies have shown that patients with focal ISR have a better prognosis compared with non-focal ISR [4‒6], while few studies have investigated the differences between the two restenosis types, especially in intraluminal imaging level. Optical coherence tomography (OCT) with its superior tissue resolution, can offer a fresh perspective on the mechanism of ISR [7]. Therefore, the main objective of this study was to investigate the differences in patients with different restenosis patterns by using OCT, which may provide valuable insights into the pathogenesis of ISR and potentially contribute to the development of new therapeutic strategies.

Study Population

This study is a single-center, retrospective study. We collected 568 patients who were diagnosed with ISR at the Affiliated Hospital of Zunyi Medical University from October 2018 and December 2022. ISR is defined as a reduction in the diameter of the lumen within the stented vessel segment of ≥50% as assessed by coronary angiography visually. The exclusion criteria are as follows: (1) without OCT examination (n = 320); (2) incomplete clinical data (n = 20); (3) poor quality of OCT or angiographic images (n = 12). Ultimately, a total of 216 patients with 216 lesions were included in the analysis. These patients were divided into focal group or non-focal group according to the restenosis pattern (shown in Fig. 1). The clinical baseline data, angiographic data, and related OCT data were analyzed.

Fig. 1.

Study flow diagram. ISR, in-stent restenosis; OCT, optical coherence tomography; PCI, percutaneous coronary intervention.

Fig. 1.

Study flow diagram. ISR, in-stent restenosis; OCT, optical coherence tomography; PCI, percutaneous coronary intervention.

Close modal

Clinical Baseline and Angiographic Characteristic

The baseline data including age, sex, coronary risk factors, laboratory data, past medicine use, time from stent implantation to ISR and underlying stent type was collected by experienced physicians from electronic medical record systems. The underlying stent type was based on the medical records provided by patients or the interventional cardiologist’s estimation according to the time of stent implantation when the medical records cannot be offered. The coronary angiographic features of all patients were analyzed by Yi Deng and Shuai Ma, two veteran cardiologists from the Department of Cardiology at the Affiliated Hospital of Zunyi Medical University. Based on Mehran’s classification [8], patterns of ISR were classified as type I, focal (lesions ≤10 mm); type II, diffuse in-stent (lesions >10 mm, confined to the stent); type III, diffuse proliferative (lesions >10 mm, beyond the stent edge); and type IV (total occlusion). Proximal and distal reference diameters, minimal luminal diameter (MLD), and diameter stenosis (DS) were calculated by experienced physicians (Yi Deng and Shuai Ma) who were blinded to the clinical characteristics.

OCT Image Acquisition and Assessment

The frequency-domain OCT system (C7XR, Ilumien or Ilumien Optis) was used to perform OCT evaluation after completion of coronary angiography by an interventional cardiologist as previously described [9]. All OCT images were analyzed by two experienced doctors (Chancui Deng and Zhijiang, Liu) who were blinded to angiographic and clinical data. All cross-sectional images were initially screened through quality assessment. The reference site for analysis was determined as the area with the largest lumen area either proximal or distal to the stenosis. The reference lumen diameter, area of the proximal or distal site, minimum lumen area (MLA), and area stenosis (AS) were performed by semiautomatic measurement. The profiles of the stent and lumen were automatically traced. When the profiles did not match well, we would trace them manually.

For qualitative analysis, the neointima was classified as homogeneous neointima and heterogeneous neointima. The homogeneous neointima was defined as high backscattering regions without focal signal attenuation [10]. The heterogeneous neointima was defined as low-signal regions with focal signal attenuation [10]. Lipid-rich plaque (LRP) was defined as a low-signal region with a diffuse border [11]. Neointimal calcification was defined as a well-delineated and signal-poor region with sharp borders [12]. In-stent neoatherosclerosis (ISNA) was identified must meet the following criteria: macrophage infiltration and/or lipid-laden tissue within the stent or neointimal calcification [13]. The thin-cap fibroatheroma (TCFA) was defined as a lipid neointima with a fibrous cap thickness of ≤65 µm at its thinnest point [14]. A thrombus was defined as an irregular mass with a minimum diameter of at least 250 mm, attached to the vascular wall orfloating in the lumen. Red thrombus was defined as highly backscattering with high attenuation whereas white thrombus was defined as less backscattering, and homogeneous with low attenuation. Macrophages were defined as signal-rich, distinct, or confluent punctate regions that exceed the intensity of background speckle noise via visual estimation [9]. Stent malapposition was defined as the distance from the endoluminal surface of the stent strut to the lumen contour is greater than the sum of the metal and polymer thickness [15]. Microvessels were defined as tubular structures with a diameter of <200 μm. The inter-observer and intra-observer variabilities for observing ISNA were k = 0.854 and k = 0.827, respectively, for LRP were k = 0.813 and k = 0.845, respectively, and for calcified neointima were k = 0.897 and k = 0.861, respectively. Lipid length and lipid arc were measured on the longitudinal reconstructed view and the cross-sectional image, respectively. Lipid index was defined as the product of mean lipid arc multiplied by lipid length. The typical OCT images are shown in Figure 2.

Fig. 2.

Representative images of optical coherence tomography findings in patients presenting with in-stent restenosis. a Lipid-rich plaque. b Neointimal calcification. c Intra-intimal microvessels (arrows). d White thrombus. e Red thrombus. f Stent malapposition.

Fig. 2.

Representative images of optical coherence tomography findings in patients presenting with in-stent restenosis. a Lipid-rich plaque. b Neointimal calcification. c Intra-intimal microvessels (arrows). d White thrombus. e Red thrombus. f Stent malapposition.

Close modal

Statistical Analysis

Continuous variables were presented as mean ± standard deviation (SD) for normally distributed data. These data were compared using independent samples t test when it satisfied homogeneity of variance as well. When the data did not meet normal distribution, these data were expressed as median (25th–75th percentiles) and compared using the Mann-Whitney U test between the two groups. Categorical variables are summarized as numbers (percentages) and compared using the χ2 test or Fisher’s exact test when any expected frequency of the contingency table was <5. In univariate logistic regression analysis, age, sex, hypertension, diabetes mellitus, dyslipidemia, smoking, stroke, chronic kidney disease, previous MI, triglyceride, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, uric acid, estimated glomerular filtration rate, white blood cell count, fasting blood glucose, homogeneous neointima, heterogeneous neointima, in-stent neoatherosclerosis, lipid-rich plaque, TCFA, calcified neointima, peri-strut microvessels, intra-intima microvessels, neointimal rupture, red thrombus, white thrombus, cholesterol crystal, stent malapposition, diameter stenosis (DS), area stenosis (AS), minimal lumen diameter (MLD), minimal lumen area (MLA), and past medicines use were included. While variables included in the multivariate logistic regression analysis were based on clinical importance, statistical results (p value of < 0.1 in the univariable analysis). Statistical significance was set at a two-sided p value of <0.05. All statistical analyses were performed using SPSS version 26.0 software (IBM, Armonk, NY, USA).

Clinical Characteristics and Angiographic Findings

A total of 216 patients with 216 lesions were finally included. Baseline clinical and angiographic characteristics are shown in Tables 1 and 2. The ratio of males was higher in the non-focal group than those in the focal group (84.2% vs. 69.0%, p = 0.013). The white blood cell count and high-sensitivity troponin T were significantly higher in the non-focal group (7.8 ± 3.0 vs. 6.6 ± 2.1, p = 0.007; 15.69 [9.38–55.48] vs. 10.82 [7.66–22.76], p = 0.020, respectively). There were no significant statistical differences among the other baseline and angiographic data.

Table 1.

Baseline characteristics

VariablesOverall (N = 216)Focal (N = 58)Non-focal (N = 158)p value
Age, M (IQR), years 63.4±10.5 65.3±9.2 62.7±10.8 0.070 
Male, n (%) 173 (80.1) 40 (69.0) 133 (84.2) 0.013 
Coronary risk factors 
 Hypertension, n (%) 132 (61.1) 39 (67.2) 93 (58.9) 0.263 
 Diabetes, n (%) 55 (25.5) 15 (25.9) 40 (25.3) 0.935 
 Dyslipidemia, n (%) 58 (26.9) 21 (36.2) 37 (23.4) 0.060 
 Smoking, n (%) 91 (42.1) 19 (32.8) 72 (45.6) 0.091 
 Atrial fibrillation, n (%) 3 (1.4) 0 (0) 3 (1.9) 0.566 
 Stoke, n (%) 11 (5.1) 4 (6.9) 7 (4.4) 0.491 
 CKD, n (%) 52 (24.1) 16 (27.6) 36 (22.8) 0.464 
Laboratory data 
 TG, mmol/L 2.02±1.11 2.07±1.15 2.01±1.04 0.833 
 TC, mmol/L 4.10±1.48 3.90±1.28 4.18±1.54 0.233 
 HDL-C, mmol/L 1.10±0.31 1.10±0.30 1.09±0.31 0.849 
 LDL-C, mmol/L 2.31±0.88 2.62±0.72 2.20±0.94 0.063 
 FBG, mmol/L 7.2±2.5 6.9±2.4 7.4±2.9 0.329 
 Uric acid, μmol/L 390.2±110.5 381.6±100.3 393.4±114.1 0.488 
 eGFR, mL/min/1.73 m2 79.6±26.2 77.9±23.9 80.3±27.1 0.550 
 WBC, 109/L 7.5±2.9 6.6±2.1 7.8±3.0 0.007 
 Hs-TnT, ng/L, M (IQR) 15 (8.96–47.61) 10.82 (7.66–22.76) 15.69 (9.38–55.48) 0.020 
 Stents time, days, M (IQR) 912.5 (365.0, 2,007.5) 730.0 (365.0, 1,916.3) 1,095.0 (365.0, 2,190.0) 0.221 
ISR clinical presentation 0.679 
 Silent ischemia, n (%) 36 (16.7) 13 (22.4) 23 (14.6)  
 Stable angina pectoris, n (%) 58 (26.9) 16 27.6) 42 (26.6)  
 Unstable angina pectoris, n (%) 82 (38.0) 19 (32.8) 63 (39.9)  
 NSTEMI, n (%) 26 (12.0) 7 (12.1) 19 (12.0)  
 STEMI, n (%) 14 (6.5) 3 (5.2) 11 (7.0)  
Past medicines use 
 Aspirin, n (%) 200 (92.6) 54 (93.1) 146 (92.4) 1,000 
 P2Y12 inhibitor, n (%) 189 (87.5) 52 (89.7) 137 (86.7) 0.562 
 Statin, n (%) 191 (88.4) 55 (94.8) 136 (86.1) 0.075 
VariablesOverall (N = 216)Focal (N = 58)Non-focal (N = 158)p value
Age, M (IQR), years 63.4±10.5 65.3±9.2 62.7±10.8 0.070 
Male, n (%) 173 (80.1) 40 (69.0) 133 (84.2) 0.013 
Coronary risk factors 
 Hypertension, n (%) 132 (61.1) 39 (67.2) 93 (58.9) 0.263 
 Diabetes, n (%) 55 (25.5) 15 (25.9) 40 (25.3) 0.935 
 Dyslipidemia, n (%) 58 (26.9) 21 (36.2) 37 (23.4) 0.060 
 Smoking, n (%) 91 (42.1) 19 (32.8) 72 (45.6) 0.091 
 Atrial fibrillation, n (%) 3 (1.4) 0 (0) 3 (1.9) 0.566 
 Stoke, n (%) 11 (5.1) 4 (6.9) 7 (4.4) 0.491 
 CKD, n (%) 52 (24.1) 16 (27.6) 36 (22.8) 0.464 
Laboratory data 
 TG, mmol/L 2.02±1.11 2.07±1.15 2.01±1.04 0.833 
 TC, mmol/L 4.10±1.48 3.90±1.28 4.18±1.54 0.233 
 HDL-C, mmol/L 1.10±0.31 1.10±0.30 1.09±0.31 0.849 
 LDL-C, mmol/L 2.31±0.88 2.62±0.72 2.20±0.94 0.063 
 FBG, mmol/L 7.2±2.5 6.9±2.4 7.4±2.9 0.329 
 Uric acid, μmol/L 390.2±110.5 381.6±100.3 393.4±114.1 0.488 
 eGFR, mL/min/1.73 m2 79.6±26.2 77.9±23.9 80.3±27.1 0.550 
 WBC, 109/L 7.5±2.9 6.6±2.1 7.8±3.0 0.007 
 Hs-TnT, ng/L, M (IQR) 15 (8.96–47.61) 10.82 (7.66–22.76) 15.69 (9.38–55.48) 0.020 
 Stents time, days, M (IQR) 912.5 (365.0, 2,007.5) 730.0 (365.0, 1,916.3) 1,095.0 (365.0, 2,190.0) 0.221 
ISR clinical presentation 0.679 
 Silent ischemia, n (%) 36 (16.7) 13 (22.4) 23 (14.6)  
 Stable angina pectoris, n (%) 58 (26.9) 16 27.6) 42 (26.6)  
 Unstable angina pectoris, n (%) 82 (38.0) 19 (32.8) 63 (39.9)  
 NSTEMI, n (%) 26 (12.0) 7 (12.1) 19 (12.0)  
 STEMI, n (%) 14 (6.5) 3 (5.2) 11 (7.0)  
Past medicines use 
 Aspirin, n (%) 200 (92.6) 54 (93.1) 146 (92.4) 1,000 
 P2Y12 inhibitor, n (%) 189 (87.5) 52 (89.7) 137 (86.7) 0.562 
 Statin, n (%) 191 (88.4) 55 (94.8) 136 (86.1) 0.075 

M±SD, mean ± standard deviation; M (IQR), median (interquartile range); CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; FBG, fasting blood glucose; TG, triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol C; HDL-C, high-density lipoprotein cholesterol C; WBC, white blood cell count; Hs-TnT, hypersensitive cardiac troponin T; NSTEMI, non-ST-elevation myocardial infarction; STEMI, ST-elevation myocardial infarction; NT-proBNP, N-terminal pro-B-type natriuretic peptide; BMS, bare-metal stent; DES, drug-eluting stent.

Table 2.

Angiography characteristics

VariablesOverall (N = 216)Focal (N = 58)Non-focal (N = 158)p value
Target vessel 0.426 
 LAD, n (%) 138 (63.9) 37 (63.8) 101 (63.9)  
 LCX, n (%) 18 (8.3) 7 (12.1) 11 (7.0)  
 RCA, n (%) 60 (27.8) 14 (24.1) 46 (29.1)  
Underlying stent type 0.077 
 BMS, n (%) 73 (33.8) 13 (22.4) 60 (38.0)  
 First-generation DES, n (%) 70 (32.4) 26 (44.8) 44 (27.8)  
 New-generation DES, n (%) 57 (26.4) 15 (25.9) 42 (26.6)  
 Unknown 16 (7.4) 4 (6.9) 12 (7.6)  
Restenosis morphology 
 Focal, n (%) 58 (26.9) 58 (100) 0 (0)  
 Diffuse, n (%) 53 (24.5) 0 (0) 53 (33.5)  
 Proliferative, n (%) 51 (23.6) 0 (0) 51 (32.3)  
 Occlusive, n (%) 54 (25.0) 0 (0) 54 (32.4)  
 RVD (proximal), M±SD, mm 3.1±0.5 3.0±0.4 3.2±0.5 0.812 
 RVD (distal), M±SD, mm 2.4±0.5 2.4±0.4 2.4±0.5 0.982 
 DS, %, M±SD 58.9±6.9 57.9±6.3 59.2±7.6 0.553 
 MLD, M±SD, mm 1.44±0.29 1.42±0.26 1.45±0.33 0.528 
VariablesOverall (N = 216)Focal (N = 58)Non-focal (N = 158)p value
Target vessel 0.426 
 LAD, n (%) 138 (63.9) 37 (63.8) 101 (63.9)  
 LCX, n (%) 18 (8.3) 7 (12.1) 11 (7.0)  
 RCA, n (%) 60 (27.8) 14 (24.1) 46 (29.1)  
Underlying stent type 0.077 
 BMS, n (%) 73 (33.8) 13 (22.4) 60 (38.0)  
 First-generation DES, n (%) 70 (32.4) 26 (44.8) 44 (27.8)  
 New-generation DES, n (%) 57 (26.4) 15 (25.9) 42 (26.6)  
 Unknown 16 (7.4) 4 (6.9) 12 (7.6)  
Restenosis morphology 
 Focal, n (%) 58 (26.9) 58 (100) 0 (0)  
 Diffuse, n (%) 53 (24.5) 0 (0) 53 (33.5)  
 Proliferative, n (%) 51 (23.6) 0 (0) 51 (32.3)  
 Occlusive, n (%) 54 (25.0) 0 (0) 54 (32.4)  
 RVD (proximal), M±SD, mm 3.1±0.5 3.0±0.4 3.2±0.5 0.812 
 RVD (distal), M±SD, mm 2.4±0.5 2.4±0.4 2.4±0.5 0.982 
 DS, %, M±SD 58.9±6.9 57.9±6.3 59.2±7.6 0.553 
 MLD, M±SD, mm 1.44±0.29 1.42±0.26 1.45±0.33 0.528 

M±SD, mean ± standard deviation; LAD, left anterior descending artery; LCX, left circumflex artery; RCA, right coronary artery; DS, diameter stenosis; RVD, reference vessel diameter; MLD, minimal lumen diameter; BMS, bare-metal stent; DES, drug-eluting stent.

OCT Analysis of ISR Lesions

The OCT findings were shown in Table 3. The prevalence of lipid-rich plaque was higher in the focal group than in the non-focal ones (65.5% vs. 42.4%, p = 0.003). While both red thrombus and white thrombus were more common in the non-focal group (27.8% vs. 12.1%, p = 0.016 and 41.1% vs. 24.1%, p = 0.021, respectively). Stent malapposition was more observed in non-focal group (22.2% vs. 8.6%, p = 0.023). In addition, compared with the focal group, the degree of AS was commonly higher in the non-focal group (81.0 ± 7.7 vs. 74.7 ± 8.2, p = 0.045). The minimal lumen area was smaller in the non-focal group (1.1 ± 0.4 vs. 1.8 ± 0.7, p = 0.036). The Fibrous cap thickness is thinner in the focal group (102 ± 33 vs. 118 ± 46, p = 0.048).

Table 3.

OCT findings in different groups

VariablesOverall (N = 216)Focal (N = 58)Non-focal (N = 158)p value
Qualitative assessment 
 Heterogeneous neointima 116 (53.7) 27 (46.6) 89 (56.3) 0.202 
 Homogeneous neointima 100 (46.3) 31 (53.4) 69 (43.7) 0.202 
 ISNA 118 (54.6) 26 (44.8) 92 (58.2) 0.080 
 Lipid-rich plaque 105 (48.6) 38 (65.5) 67 (42.4) 0.003 
 TCFA 57 (26.4) 11 (19.0) 46 (29.1) 0.060 
 Neointimal calcification 72 (33.3) 15 (25.9) 57 (36.1) 0.158 
 Peri-strut microvessels 78 (36.1) 25 (43.1) 53 (33.5) 0.195 
 Intra-intimal microvessels 86 (39.8) 21 (36.2) 65 (41.1) 0.512 
 Red thrombus 51 (23.6) 7 (12.1) 44 (27.8) 0.016 
 White thrombus 79 (36.6) 14 (24.1) 65 (41.1) 0.021 
 Macrophage 41 (19) 7 (12.1) 34 (21.5) 0.117 
 Stent malapposition 40 (18.5) 5 (8.6) 35 (22.2) 0.023 
 Cholesterol crystal 36 (16.7) 8 (13.8) 28 (17.7) 0.492 
Quantitative assessment 
 RVA (proximal) 8.1±2.3 7.5±2.2 8.4±2.3 0.015 
 RVA (distal) 5.0±2.1 4.9±2.1 5.1±2.1 0.486 
 AS, % 79.5±7.9 74.7±8.2 81.0±7.7 0.045 
 MLA, mm2 1.3±0.5 1.8±0.7 1.1±0.4 0.036 
 Mean lipid arc, ° 170.8±55.3 172.4±57.4 168.5±52.9 0.308 
 Lipid length, mm 8.3±3.0 6.9±2.9 8.8±3.1 0.178 
 Lipid index1 1,386±784 1,204±603 1,482.8±824 0.053 
 Fibrous cap thickness, μm 113±41 102±33 118±46 0.048 
 Mean stent diameter, mm 2.8±0.5 2.7±0.5 2.8±0.5 0.907 
 Mean stent area, mm2 6.7±2.4 6.3±2.0 6.9±2.5 0.284 
VariablesOverall (N = 216)Focal (N = 58)Non-focal (N = 158)p value
Qualitative assessment 
 Heterogeneous neointima 116 (53.7) 27 (46.6) 89 (56.3) 0.202 
 Homogeneous neointima 100 (46.3) 31 (53.4) 69 (43.7) 0.202 
 ISNA 118 (54.6) 26 (44.8) 92 (58.2) 0.080 
 Lipid-rich plaque 105 (48.6) 38 (65.5) 67 (42.4) 0.003 
 TCFA 57 (26.4) 11 (19.0) 46 (29.1) 0.060 
 Neointimal calcification 72 (33.3) 15 (25.9) 57 (36.1) 0.158 
 Peri-strut microvessels 78 (36.1) 25 (43.1) 53 (33.5) 0.195 
 Intra-intimal microvessels 86 (39.8) 21 (36.2) 65 (41.1) 0.512 
 Red thrombus 51 (23.6) 7 (12.1) 44 (27.8) 0.016 
 White thrombus 79 (36.6) 14 (24.1) 65 (41.1) 0.021 
 Macrophage 41 (19) 7 (12.1) 34 (21.5) 0.117 
 Stent malapposition 40 (18.5) 5 (8.6) 35 (22.2) 0.023 
 Cholesterol crystal 36 (16.7) 8 (13.8) 28 (17.7) 0.492 
Quantitative assessment 
 RVA (proximal) 8.1±2.3 7.5±2.2 8.4±2.3 0.015 
 RVA (distal) 5.0±2.1 4.9±2.1 5.1±2.1 0.486 
 AS, % 79.5±7.9 74.7±8.2 81.0±7.7 0.045 
 MLA, mm2 1.3±0.5 1.8±0.7 1.1±0.4 0.036 
 Mean lipid arc, ° 170.8±55.3 172.4±57.4 168.5±52.9 0.308 
 Lipid length, mm 8.3±3.0 6.9±2.9 8.8±3.1 0.178 
 Lipid index1 1,386±784 1,204±603 1,482.8±824 0.053 
 Fibrous cap thickness, μm 113±41 102±33 118±46 0.048 
 Mean stent diameter, mm 2.8±0.5 2.7±0.5 2.8±0.5 0.907 
 Mean stent area, mm2 6.7±2.4 6.3±2.0 6.9±2.5 0.284 

ISNA, in-stent neoatherosclerosis; MLA, minimal lumen area; AS, area stenosis; RVA, reference vessel area; TCFA, thin-cap fibroatheroma.

1Lipid index = lipid length multiplied with mean lipid arc.

Predictors of Focal Restenosis

Based on the clinical importance and statistical results in univariate logistic regression analysis, age, sex, dyslipidemia, diabetes mellitus, smoking, stroke, CKD, triglyceride, total cholesterol, LDL-C, FBG, eGFR, white blood cell count, ISNA, TCFA, neointimal rupture, red thrombus, white thrombus, stent malapposition, AS, MLA, mean lipid arc, lipid length, and fibrous cap thickness were finally included in the multivariate regression logistic analysis (online suppl. Table S1; for all online suppl. material, see https://doi.org/10.1159/000542165). In the multivariable logistic regression analysis, LDL-C (odds ratio [OR]: 3.341, 95% confidence interval [CI]: 1.714–9.784, p = 0.046) was positively independently associated with focal restenosis. While white blood cell count (OR: 0.814, 95% CI: 0.657–0.913, p = 0.047) and stent malapposition (OR: 0.228, 95% CI: 0.057–0.896, p = 0.037) were independently associated with non-focal restenosis (shown in Fig. 3).

Fig. 3.

Multivariable logistic regression analysis for focal restenosis.

Fig. 3.

Multivariable logistic regression analysis for focal restenosis.

Close modal

The main findings are as follows: (1) white blood cell count was higher in patients with non-focal ISR; (2) the ratio of lipid-rich plaque was higher in patients with focal ISR while the prevalence of red thrombus, white thrombus and stent malapposition was more common in patients with non-focal restenosis; (3) multivariate logistic regression analysis shows that white blood cell count and stent malapposition were correlated with non-focal restenosis, LDL-C was associated with focal restenosis.

White blood cell count is closely related to the inflammatory state in the body, and its increase usually indicates active inflammation. Previous studies showed that inflammatory response had been significantly associated with the occurrence of ISR [16, 17]. Specifically, inflammatory markers such as high-sensitivity C-reactive protein and interleukins are involved in the development of in-stent restenosis and the type of drug-eluting stent can also affect the degree of inflammatory response, thereby affecting the occurrence of restenosis [18, 19]. Li et al. [20] found that the elevation of monocytes was independently associated with ISR. Furthermore, Niccoli and colleagues [21]investigated patients with ISR who underwent OCT. They found that inflammatory biomarkers were associated with different aspects of neointimal tissue. In their study, high-sensitivity C-reactive protein seemed to have a role in neointimal tissue shape, while eosinophil cationic protein was related to a neointimal burden. Nevertheless, the CANTOS trial [22] suggested that interleukin-1β inhibition significantly reduced recurrent cardiovascular events in patients with a previous history of myocardial infarction compared with placebo. The reduction was independent of lipid-lowering therapy. In our investigation, the white blood cell count is obviously higher in patients with non-focal type than those with focal type. The multivariate logistic regression analysis shows white blood cell count is independently associated with non-focal ISR. In fact, we wanted to investigate the relationship between focal and non-focal restenosis. Unfortunately, a lot of patients missed hypersensitive C-reactive protein during collecting the baseline data. According to previous studies, patients with non-focal ISR had worse prognosis than those with focal ISR. Therefore, suppressing the inflammatory response may be a helpful way to improve the prognosis of patients with ISR.

Stent malapposition refers to stent struts not apposed to the vessel wall, which leaving a gap occupied by blood between the struts and the vessel wall. Previous meta-analysis indicated that late and very late stent thrombosis (ST) was associated with late stent malapposition. Compared with bare-metal stents, the phenomenon of ST was more common in drug-eluted stents [23]. In our study, we found that the non-focal type ISR was more observed in patients with stent malapposition, which is in line with previous study. But there was no statistic difference among the stent types, which may be the reason why the sample size in our study was relatively small. Furthermore, the space left by stent malapposition may alter the focal hemodynamics around the vessel wall and potentially lead to vessel wall injury, accelerating the development of non-focal restenosis. Hence, it is of great importance to identify stent malapposition to avoid lesion progressing.

There is a difference in the prognosis among the different pattern of restenosis [4‒6], previous studies have investigated the relative risk factors of non-focal restenosis [24, 25]. Lee et al. [24] analyzed 217 restenotic lesions after DES implantation, the found paclitaxel-eluting stent (PES) was independently associated with non-focal restenosis and focal restenosis was commonly observed in the new generation DES. Corbett and his colleagues [25] also found that PES was the independent risk factor of non-focal restenosis. In our study, focal restenosis occurs more in the first-generation drug-eluting stents while non-focal restenosis occurs more in bare-metal stents. There could be two main reasons. A few patients cannot provide the medical records during the previous PCI. Therefore, it relied on an experienced interventional cardiologist to determine the underlying type of stents. In addition, the sample size of the different stents type was relatively small, which might be the other reason why our results are inconsistent with previous studies.

In our study, we found that the concentration of LDL-C was seemly higher in patients with focal ISR than those with non-focal type and multivariable logistic regression analysis showed LDL-C was associated with focal ISR. Plenty of studies have shown that LDL-C was correlated with lipid plaque [26‒28]. Lee et al. [28]. Retrospectively analyzed 212 DES-treated patients with ISR, they found that the concentration of LDL-C was higher in patients with neoatherosclerosis than those without during follow-up. In fact, reducing LDL-C is beneficial for patients with atherosclerotic cardiovascular disease (ASCVD) [29]. The HUYGENS study [26] demonstrated that patients with a non–ST-segment elevation myocardial infarction who treated with evolocumab for reducing LDL-C had more stable plaque than those with placebo. Moreover, the minimum fibrous cap thickness was thinner and the maximum lipid arc was smaller in the evolocumab group than in the placebo group. Therefore, intensive lipid-lowering therapy can help reverse the lipid plaque phenotype in patients with coronary artery disease. Furthermore, we also found that the prevalence of lipid-rich plaque was higher in focal ISR, which was similar to the previous study [30]. In a retrospective study, Ino et al. [30] found that lipidic plaque was associated with late stent edge ISR after everolimus-eluting stent implantation. In conclusion, patients with high LDL-C are prone to have lipid-rich plaque and seem to be focal ISR.

This study has important implications in the management of patients with ISR. According to previous studies, patients with non-focal ISR had a worse prognosis. Our research indicates white blood cell count and stent malapposition are correlated with non-focal ISR. It is vital that prevention of stent malapposition and suppressing the inflammatory response may be beneficial to the patients with ISR. Meanwhile, reducing LDL-C is of great necessity for all the patients with coronary artery disease.

This study has several limitations. First, this study was a retrospective single-center study and the sample size was relatively small. A future large-scale study is warranted. Second, most patients lacked OCT images when stents were first implanted. Therefore, the influence of lesion characteristics at the time of stent implantation on ISR cannot be investigated further. Third, all these patients were not followed up for long periods, so it is not clear whether there is a difference in prognostic value between focal restenosis and non-focal restenosis. A prospective study should be designed to identify the prognostic impact between the two groups in the future.

After adjusting confounding characteristics of clinical baselines, angiographic findings, and OCT data in patients with ISR, low-density lipoprotein cholesterol C was independently associated with focal restenosis. White blood cell count and stent malapposition were correlated with non-focal restenosis.

The written informed consent was obtained from participants to participate in the study. This study protocol was reviewed and approved by the Institutional Ethics Committee of the Affiliated Hospital of Zunyi Medical University, Approval No. ZMU (2023)1-153.

The authors have no conflicts of interest to declare.

This research was supported by grants from the National Natural Science Foundation of China (82200290) and the Science and Technology Program of the Guizhou Province (LC [2021] 026).

Bei Shi and Guanxue Xu designed the study. Youcheng Shen, Changpei Liu, and Zhijiang Liu completed the data collecting and analyzing. Wei Zhang, Jidong Rong, Ning Gu, Changyin Shen, Panke Chen, Xi Wang, Shuangya Yang, Chancui Deng, and Qianhang Xia participated in part of patients’ demographics collection. Yi Deng and Shuai Ma completed the analysis of coronary angiographic. Chancui Deng and Zhijiang Liu participated in the analysis of OCT images. Bei Shi, Guanxue Xu, and Youcheng Shen wrote and revised the manuscript with contributions upon all listed authors. All authors reviewed and approved the final manuscript.

Additional Information

Youcheng Shen, Changpei Liu, and Zhijiang Liu have contributed equally to this work.Guanxue Xu and Bei Shi take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.

The datasets used and/or analyzed during the present study are available from the corresponding author upon reasonable request. The data that support the findings of this study are not publicly available due to their containing information that could compromise the privacy of research participants but are available from corresponding author.

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