This study aims to analyze the correlation between Systemic Inflammatory Response Index (SIRI) and the severity of coronary artery stenosis in patients with coronary heart disease (CHD). It also aims to assess the predictive value of SIRI for the severity of coronary artery stenosis. A total of 2990 patients who underwent coronary angiography were included in this study. The Gensini score was used to estimate the severity of coronary vascular lesions. The predictive ability of SIRI for CHD was evaluated using receiver operating characteristic (ROC) curves. Binary multivariate logistic regression analysis was used to predict the likelihood of CHD based on the SIRI index. The results showed that people with higher SIRI were more likely to have CHD (P < 0.001). After controlling for other risk factors, the highest quartile had a significantly higher incidence of coronary artery disease compared to the lowest quartile (odds ratio [OR] 2.25, 95% confidence interval [CI] 1.73-3.92, P < 0.001). Furthermore, the Gensini score was significantly higher in the fourth quartile group (T4) compared to the first (T1) and second (T2) quartile groups (P < 0.001). Additionally, the SIRI was significantly higher in the group with severe coronary artery lesions compared to the mild and moderate groups (P < 0.001). The SIRI also showed a higher predictive ability for the extent of coronary lesions under the ROC curve compared to other commonly used markers, including platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-lymphocyte ratio (NLR) (P < 0.001). Therefore, SIRI positively correlates with coronary artery stenosis in CHD patients, serving as an effective early screening marker for assessing stenosis severity.

Introduction: Coronary artery disease (CAD) remains a significant global health challenge, with percutaneous coronary intervention (PCI) being a primary revascularization method. In-stent restenosis (ISR) post-PCI, although reduced, continues to impact patient outcomes. Inflammation and platelet activation play key roles in ISR development, emphasizing the need for accurate risk assessment tools. The systemic inflammation aggregation index (AISI) has shown promise in predicting adverse outcomes in various conditions but has not been studied in relation to ISR. Methods: A retrospective observational study included 1712 patients post-drug-eluting stent (DES) implantation. Data collected encompassed demographics, medical history, medication use, laboratory parameters, and angiographic details. AISI, calculated from specific blood cell counts, was evaluated alongside other variables using machine learning models, including random forest, Xgboost, elastic networks, logistic regression, and multilayer perceptron. The optimal model was selected based on performance metrics and further interpreted using variable importance analysis and the SHAP method. Results: Our study revealed that ISR occurred in 25.8% of patients, with a range of demographic and clinical factors influencing the risk of its development. The random forest model emerged as the most adept in predicting ISR, and AISI featured prominently among the top variables affecting ISR prediction. Notably, higher AISI values were positively correlated with an elevated probability of ISR occurrence. Comparative evaluation and visual analysis of model performance, the random forest model demonstrates high reliability in predicting ISR, with specific metrics including an AUC of 0.9569, accuracy of 0.911, sensitivity of 0.855, PPV of 0.81, and NPV of 0.948. Conclusion: AISI demonstrated itself as a significant independent risk factor for ISR following DES implantation, with an escalation in AISI levels indicating a heightened risk of ISR occurrence.

Objective: In-stent restenosis (ISR) is a fatal complication of percutaneous coronary intervention (PCI). An early predictive model with the medical history of patients, angiographic characteristics, inflammatory indicators and blood biochemical index is urgently needed to predict ISR events. We aim to establish a risk prediction model for ISR in CAD patients undergoing PCI. Methods: A total of 477 CAD patients who underwent PCI with DES (drug-eluting stents) between January 2017 and December 2020 were retrospectively enrolled. And the preoperative factors were compared between the non-ISR and ISR groups. The least absolute shrinkage and selection operator (LASSO) and multi-factor logistic regression were used for statistical analysis. The prediction model was evaluated using receiver operator characteristic (ROC) analysis, the Hosmer–Lemeshow 2 statistic, and the calibration curve. Results: In this study, 94 patients developed ISR after PCI. Univariate analysis showed that post-PCI ISR was associated with the underlying disease (COPD), higher Gensini score (GS score), higher LDL-C, higher neutrophil/lymphocyte ratio, and higher remnant cholesterol (RC). The multi-factor logistic regression analysis suggested that remnant cholesterol (odds ratio [OR] = 2.09, 95% confidence interval [CI] [1.40– 3.11], P < 0.001), GS score (OR = 1.01, 95% CI [1.00, 1.02], P = 0.002), medical history of COPD (OR = 4.56, 95% CI [1.98, 10.40], P < 0.001), and monocyte (OR = 1.30, 95% CI [1.04, 1.70], P < 0.001) were independent risk factors for ISR. A nomogram was generated and displayed favorable fitting (Hosmer-Lemeshow test P = 0.609), discrimination (area under ROC curve was 0.847), and clinical usefulness by decision curve analysis. Conclusion: Patients with certain preoperative characteristics, such as a history of COPD, higher GS scores, higher levels of RC, and monocytes, who undergo PCI may have a higher risk of developing ISR. The predictive nomogram, based on the above predictors, can be used to help identify patients who are at a higher risk of ISR early on, with a view to provide post-PCI health management for patients.

Three-vessel coronary artery disease (TVD) is a severe subtype of coronary heart disease, strongly associated with inflammation and metabolic dysfunction. The C-reactive protein-triglyceride glucose index (CTI), an integrated measure of inflammation and metabolism, may serve as a critical predictor of TVD risk. This study evaluates the predictive utility of CTI for TVD. This single-center retrospective study compared clinical characteristics, laboratory indices, and inflammation- and metabolism-related markers between TVD and non-TVD groups. Feature selection using the Boruta algorithm and LASSO regression identified key predictors for constructing a TVD risk model. SHAP analysis provided model interpretability and evaluated the relative importance of predictive factors. Predefined subgroup analyses by sex and diabetes status were conducted using multivariable logistic regression, with interaction terms tested to assess the consistency of CTI's predictive effect across subgroups. Traditional risk factors, including gender, age, hypertension, diabetes, and smoking, were significantly more prevalent in the TVD group. The CTI index was markedly elevated in this group and emerged as a critical predictor. Boruta and LASSO identified CTI, age, and gender as primary predictors. SHAP analysis confirmed the prominent role of CTI, with age and gender also contributing significantly to risk prediction. CTI demonstrated the strongest positive impact on TVD risk assessment. Subgroup analyses showed CTI was significantly associated with TVD in men and diabetic patients, with no significant interaction by sex or diabetes, indicating a generally stable predictive effect. CTI is a reliable predictor of TVD with immediate value for improving current risk assessment and patient stratification. Beyond its present clinical relevance, CTI also holds potential for future applications in early screening and long-term management.

Objective: This study aims to investigate their correlation and predictive utility for in-stent restenosis (ISR) in patients with acute coronary syndrome (ACS) following percutaneous coronary intervention (PCI). Methods: We collected medical records of 668 patients who underwent PCI treatment from January 2022 to December 2022. Based on follow-up results (ISR defined as luminal narrowing ≥ 50% on angiography), all participants were divided into ISR and non-ISR groups. The XGBoost machine learning (ML) model was employed to identify the optimal predictive variables from a set of 31 variables. Discriminatory ability was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), while calibration and performance of the prediction models were assessed using the Hosmer-Lemeshow (HL) test and calibration plots. Clinical utility of each model was evaluated using decision curve analysis (DCA). Results: In the XGBoost importance ranking of predictive factors, LMR and RC ranked first and fourth, respectively. The AUC of the entire XGBoost ML model was 0.8098, whereas the model using traditional stepwise backward regression, comprising five predictive factors, had an AUC of 0.706. The XGBoost model showed superior predictive performance with a higher AUC, indicating better discrimination and predictive accuracy for ISR compared to traditional methods. Conclusion: LMR and RC are identified as cost-effective and reliable biomarkers for predicting ISR risk in ACS patients following drug-eluting stent (DES) implantation. LMR and RC represent cost-effective and reliable biomarkers for predicting ISR risk in ACS patients following drug-eluting stent implantation. Enhances the accuracy and clinical utility of ISR prediction models, offering clinicians a robust tool for risk stratification and personalized patient management.