Background: Coronary atherosclerosis is a systemic chronic inflammatory disease with variable occurrence and progression. Some laboratory parameters, such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and C-reactive protein (CRP) level, are used to evaluate the degree of inflammation and the severity of coronary artery disease (CAD). The neutrophil*platelet/lymphocyte is a novel systemic immune-inflammation index (SII), and its relationship with the development and severity of CAD is unclear. Objective: To investigate the association between the SII and the severity of CAD. Methods: Three-hundred and ninety-five patients who underwent coronary angiography were enrolled; among whom, 285 patients were included in the CAD group and 110 patients were included in the non-CAD group according to the WHO guidelines. Patients with CAD were further divided according to the Gensini score into the severe coronary stenosis group and the mild coronary stenosis group. The SII was calculated using the following formula: neutrophil*platelet/lymphocyte. Results: When the cutoff value of the SII was set at 439.44, the predictive power of CAD was the highest, with a sensitivity and specificity of 64.6 and 68.2%, respectively. When the cutoff value of the SII was set at 652.83, the predictive power of severe coronary stenosis was the highest, with a sensitivity and specificity of 71.0 and 86.0%, respectively. The area under the curve of the SII in predicting severe coronary stenosis was greater than that of the NLR, PLR and CRP level. Conclusion: The SII is an independent risk factor for the occurrence and severity of CAD.

Aim: Reducing the high morbidity and mortality of ST-segment elevation myocardial infarction (STEMI) and improving patient prognosis remains a major global challenge. This study aimed to explore whether dynamic fluctuations in biomarkers are valuable predictors of prognosis in patients with STEMI. Methods: This study included 216 patients with STEMI. Blood routine tests were performed on admission, 12 h after percutaneous coronary intervention (PCI), and at discharge. Systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), and pan-immune-inflammation-value (PIV) serum immune-inflammatory markers were calculated. The Cox proportional hazard model was used to assess the factors independently associated with the prognosis of STEMI. The optimal cutoff values for the inflammatory markers were calculated. Results: Eighty-five (39.35%) of the 216 patients had major adverse cardiovascular events (MACEs) during the 1-year follow-up. Most were male (81.18%) with a median age of 64 years (interquartile, 55– 69.5). Killip class ≥ II on admission (hazard ratio [HR], 1.859; 95% CI, 1.169– 2.957; P = 0.009), total stent length (HR, 1.016; 95% CI, 1.003– 1.029; P = 0.019), values of SIRI at 12 h after PCI (HR, 1.079; 95% CI, 1.050– 1.108; P < 0.001), and the Gensini score (HR, 1.014; 95% CI, 1.007– 1.022; P < 0.001) were independently associated with an increased risk of MACEs. Compared with SII, SIRI and PIV calculated at various time points and dynamically fluctuating changes, SIRI (cutoff value, 4.15; 95% CI, 0.701– 0.819; P < 0.001) and PIV (cutoff value, 622.71; 95% CI, 0.674– 0.796; P < 0.001) at 12 h after PCI showed the best efficacy for the prognosis of STEMI. Conclusion: Our study provides relevant evidence to the notion that SIRI or PIV at 12 h after PCI may be more accurate and economical predictors of long-term adverse prognosis in patients with STEMI.

Background: No-reflow occurring after primary percutaneous coronary intervention (PCI) in patients with ST-segment elevation myocardial infarction (STEMI) can increase the incidence of major adverse cardiovascular events (MACE). The present study aimed to construct a nomogram prediction model that can be quickly referred to before surgery to predict the risk for no-reflow after PCI in STEMI patients, and to further explore its prognostic utility in this patient population.Methods: Research subjects included 443 STEMI patients who underwent primary PCI between February 2018 and February 2021. Rapidly available clinical data obtained from emergency admissions were collected. Independent risk factors for no-reflow were analyzed using a multivariate logistic regression model. Subsequently, a nomogram for no-reflow was constructed and verified using bootstrap resampling. A receiver operating characteristic (ROC) curve was plotted to evaluate the discrimination ability of the nomogram model and a calibration curve was used to assess the concentricity between the model probability curve and ideal curve. Finally, the clinical utility of the model was evaluated using decision curve analysis.Results: The incidence of no-reflow was 18% among patients with STEMI. Killip class ≥2 on admission, pre-operative D-dimer and fibrinogen levels, and systemic immune–inflammation index (SII) were independent risk factors for no-reflow. A simple and quickly accessible prediction nomogram for no-reflow after PCI was developed. This nomogram demonstrated good discrimination, with an area under the ROC curve of 0.716. This nomogram was further validated using bootstrapping with 1,000 repetitions; the C-index of the bootstrap model was 0.706. Decision curve analysis revealed that this model demonstrated good fit and calibration and positive net benefits. Kaplan–Meier survival curve analysis revealed that patients with higher model scores were at a higher risk of MACE. Multivariate Cox regression analysis revealed that higher model score(s) was an independent predictor of MACE (hazard ratio 2.062; P = 0.004).Conclusions: A nomogram prediction model that can be quickly referred to before surgery to predict the risk for no-reflow after PCI in STEMI patients was constructed. This novel nomogram may be useful in identifying STEMI patients at higher risk for no-reflow and may predict prognosis in this patient population.

Aim: The high morbidity and mortality associated with ST-segment elevation myocardial infarction (STEMI) are an urgent concern. This study aimed to investigate the ratio of lymphocyte count to C-reactive protein ratio (LCR) in multiple measurements in the perioperative period, exploring dynamic changes as the best predictor of major adverse cardiovascular events (MACE) in STEMI patients. Methods: We enrolled 205 STEMI patients, conducting blood counts at admission, 24 hours post-percutaneous coronary intervention (PCI), and at discharge. Cox proportional risk models evaluated factors independently associated with STEMI prognosis. The receiver operating characteristic (ROC) curve and the De-Long test determined the best predictor. Kaplan–Meier analysis assessed the prognostic value of LCR for STEMI patients. Statistical differences and correlations between LCR at 24 hours post-PCI and cardiovascular disease risk factors were also analyzed. Results: Gensini score (HR, 1.015; 95% CI, 1.007– 1.022; P < 0.001), total stent length (HR, 1.015; 95% CI, 1.002– 1.029; P=0.025), lipoprotein (a) (HR, 1.001; 95% CI, 1.000– 1.002; P=0.043), LCR at admission (HR, 0.995; 95% CI, 0.989– 1.000; P=0.002), and LCR at 24 hours post-PCI (HR, 0.587; 95% CI, 0.486– 0.708; P < 0.001) were independent risk factors for long-term STEMI prognosis after PCI. LCR at admission (cut-off value, 2.252; 95% CI, 0.040– 0.768; P < 0.001) and LCR at 24 hours post-PCI (cut-off value, 2.252; 95% CI, 0.831– 0.924; P < 0.001) effectively predicted MACEs occurrence, with the latter exhibiting a superior predictive effect (P< 0.001). Kaplan-Meier analysis revealed that patients with LCR at admission ≤ 50.29 and LCR at 24 hours post-PCI ≤ 2.25 had significantly higher risks of developing MACEs (Log-rank P < 0.0001). Conclusion: LCR at 24 hours post-PCI may be a superior marker for long-term MACE prediction in STEMI patients, serving as the best predictor for distant MACE occurrence.