Systemic immune inflammation index is associated with in-stent neoatherosclerosis and plaque vulnerability: An optical coherence tomography study.

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.

Machine Learning Constructed Based on Patient Plaque and Clinical Features for Predicting Stent Malapposition: A Retrospective Study.

BACKGROUND: Stent malapposition (SM) following percutaneous coronary intervention (PCI) for myocardial infarction continues to present significant clinical challenges. In recent years, machine learning (ML) models have demonstrated potential in disease risk stratification and predictive modeling. HYPOTHESIS: ML models based on optical coherence tomography (OCT) imaging, laboratory tests, and clinical characteristics can predict the occurrence of SM. METHODS: We studied 337 patients from the Affiliated Hospital of Zunyi Medical University, China, who had PCI and coronary OCT from May to October 2023. We employed nested cross-validation to partition patients into training and test sets. We developed five ML models: XGBoost, LR, RF, SVM, and NB based on calcification features. Performance was assessed using ROC curves. Lasso regression selected features from 46 clinical and 21 OCT imaging features, which were optimized with the five ML algorithms. RESULTS: In the prediction model based on calcification features, the XGBoost model and SVM model exhibited higher AUC values. Lasso regression identified five key features from clinical and imaging data. After incorporating selected features into the model for optimization, the AUC values of all algorithmic models showed significant improvements. The XGBoost model demonstrated the highest calibration accuracy. SHAP values revealed that the top five ranked features influencing the XGBoost model were calcification length, age, coronary dissection, lipid angle, and troponin. CONCLUSION: ML models developed using plaque imaging features and clinical characteristics can predict the occurrence of SM. ML models based on clinical and imaging features exhibited better performance.

Non-target lesion progression: Unveiling critical predictors and outcomes in patients with in-stent restenosis.

BACKGROUND: Percutaneous coronary intervention (PCI) has become the primary treatment for coronary artery disease. However, while PCI effectively addresses severe stenosis or occlusive lesions in target vessels, the progression of non-target vessel plaque remains a critical determinant of long-term patient prognosis. AIMS: The purpose of this study was to investigate the impact of non-target vascular plaque progression on prognosis after PCI for ISR. METHODS: This study included 195 patients diagnosed with ISR and multivessel disease who underwent successful PCI with drug-eluting stent (DES) placement, along with intraoperative optical coherence tomography (OCT) assessment of the culprit stent. Subsequent rechecked coronary angiography categorized eligible patients into non-target lesion progression (N-TLP) and no-N-TLP groups. We evaluated the baseline morphological characteristics of N-TLP by OCT and investigated the relationship between N-TLP, non-culprit vessel-related major adverse cardiovascular events (NCV-MACE), and pan-vascular disease-related clinical events (PVD-CE) incidence. RESULTS: Multivariate logistic regression analysis revealed that diabetes mellitus (OR 3.616, 95% CI: 1.735-7.537; P = 0.001), uric acid level (OR 1.005, 95% CI: 1.001-1.009; P = 0.006), in-stent neoatherosclerosis (ISNA) (OR 1.334, 95% CI: 1.114-1.985; P = 0.047) and heterogeneous neointima morphology (OR 2.48, 95% CI: 1.18-5.43; P = 0.019) were independent predictors for N-TLP. Furthermore, N-TLP was associated with a high incidence of NCV-MACE (19.4% vs 6.9%, P = 0.009) and PVD-CE (83.9% [95% CI: 79.7%-88.3%] vs 93.1% [95% CI: 88.4%-98.0%], P = 0.038) after PCI in ISR patients. CONCLUSION: Diabetes, uric acid levels, ISNA, and heterogeneous neointima are predictive factors for subsequent rapid plaque progression, with N-TLP exacerbating the incidence of NCV-MACE and PVD-CE after PCI.

Morphological characteristics of in-stent restenosis with different degrees of area stenosis: an optical coherence tomography study.

The morphological characteristics of in-stent restenosis (ISR) in relation to varying degrees of area stenosis have not been comprehensively examined. This study aimed to explore the tissue characteristics of patients experiencing ISR with different degrees of area stenosis through the utilization of optical coherence tomography (OCT). In total, 230 patients with ISR who underwent OCT were divided into the following three groups: area stenosis (AS) < 70% (n = 26); 70-80% (n = 119) and AS ≥ 80% (n = 85). Among the 230 patients, the clinical presentation as stable angina was 61.5% in AS < 70%, followed by 47.2% in 70% < AS ≤ 80%, and 31.8% in AS ≥ 80% (P = 0.010). The OCT findings showed that heterogeneous neointima, ISNA, LRP, neointima rupture, TCFA-like pattern, macrophage infiltration, red and white thrombus was more common with AS increased. Ordinal logistic regression analysis showed that higher AS was associated with previous dyslipidemia (odds ratio [OR], 4.754; 95% confidence interval [CI], 1.419-15.927, P = 0.011), neointimal rupture (OR: 3.640; 95% CI, 1.169-11.325, P = 0.026), red thrombus (OR: 4.482; 95% CI, 1.269-15.816, P = 0.020) and white thrombus (OR: 5.259; 95% CI, 1.660-16.659, P = 0.005). Patients with higher degrees of area stenosis in the context of ISR exhibited a greater number of discernible morphological characteristics as identified through OCT analysis. Furthermore, previous dyslipidemia, neointimal rupture, white thrombus and red thrombus were highly associated with and the progression of ISR lesions.

Association Between Serum Uric Acid Levels and Neoatherosclerosis.

Neoatherosclerosis is a major cause of stent failure after percutaneous coronary intervention. Metabolism such as hyperuricemia is associated with in-stent restenosis (ISR). However, the association between serum uric acid (sUA) levels and in-stent neoatherosclerosis (ISNA) has never been validated.A total of 216 patients with 220 ISR lesions who had undergone optical coherence tomography (OCT) of culprit stents were included in this study. According to their sUA levels, eligible patients were divided into two groups [normal-sUA group: sUA < 7 mg/dL, n = 126, and high-sUA group: sUA ≥ 7 mg/dL, n = 90]. OCT findings were analyzed and compared between the normal- and high-sUA groups.The incidence of ISNA (63.0% versus 43.0%, P = 0.004) was significantly higher in the high-sUA group than in the normal-sUA group. Lipid plaques (66.3% versus 43.0%, P < 0.001) and thin-cap fibroatheroma (38.0% versus 18.0%, P = 0.001) were observed more frequently in the restenotic tissue structure in patients in the high-sUA group than in those in the normal-sUA group. Meanwhile, univariate (OR: 1.208, 95% CI: 1.037-1.407; P = 0.015) and multivariate (OR: 1.254, 95% CI: 1.048-1.501; P = 0.013) logistic regression analyses indicated that sUA levels were an independent risk factor for ISNA after adjusting for relevant risk factors.The high-sUA levels were an independent risk factor for the occurrence of neoatherosclerosis in patients with ISR via OCT.