Association of systemic inflammatory response index with ST segment elevation myocardial infarction and degree of coronary stenosis: a cross-sectional study.

BACKGROUND: Systemic Inflammatory Response Index (SIRI), a composite inflammatory marker encompassing neutrophils, monocytes, and lymphocytes, has been recognized as a reliable marker of systemic inflammation. This article undertakes an analysis of clinical data from ST-segment Elevation Myocardial Infarction (STEMI) patients, aiming to comprehensively assess the relationship between SIRI, STEMI, and the degree of coronary stenosis. METHODS: The study involved 1809 patients diagnosed with STEMI between the years 2020 and 2023. Univariate and multivariate logistic regression analyses were conducted to evaluate the risk factors for STEMI. Receiver operating characteristic (ROC) curves were generated to determine the predictive power of SIRI and neutrophil-to-lymphocyte ratio (NLR). Spearman correlation analysis was performed to assess the correlation between SIRI, NLR, and the Gensini score (GS). RESULTS: Multivariate logistic regression analysis showed that the SIRI was the independent risk factor for STEMI (adjusted odds ratio (OR) in the highest quartile = 24.96, 95% confidence interval (CI) = 15.32-40.66, P < 0.001). In addition, there is a high correlation between SIRI and GS (ß:28.54, 95% CI: 24.63-32.46, P < 0.001). The ROC curve analysis was performed to evaluate the predictive ability of SIRI and NLR for STEMI patients. The area under the curve (AUC) for SIRI was 0.789. The AUC for NLR was 0.754. Regarding the prediction of STEMI in different gender groups, the AUC for SIRI in the male group was 0.771. The AUC for SIRI in the female group was 0.807. Spearman correlation analysis showed that SIRI exhibited a stronger correlation with GS, while NLR was lower (SIRI: r = 0.350, P < 0.001) (NLR: r = 0.313, P < 0.001). CONCLUSION: The study reveals a strong correlation between the SIRI and STEMI as well as the degree of coronary artery stenosis. In comparison to NLR, SIRI shows potential in predicting acute myocardial infarction and the severity of coronary artery stenosis. Additionally, SIRI exhibits a stronger predictive capability for female STEMI patients compared to males.

Establishment and validation of a nomogram model containing a triglyceride-glucose index and neutrophil-to-high-density lipoprotein ratio for predicting major adverse cardiac events in patients with ST-segment elevation myocardial infarction.

OBJECTIVE: To analyze the predictive value of the triglyceride-glucose (TyG) index and neutrophil-to-high-density lipoprotein ratio (NHR) for in-hospital major adverse cardiac events (MACE) after percutaneous coronary intervention (PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI), and to establish an associated nomogram model. METHODS: In this retrospective study, we collected data from consecutive STEMI patients who underwent PCI from October 2019 to June 2023 at the Second People's Hospital of Hefei and the Second Affiliated Hospital of Anhui Medical University, as training and validation sets. Stepwise regression and multivariate logistic regression analysis were performed to screen independent risk factors, and a nomogram model was constructed and evaluated for its predictive efficacy. RESULTS: The TyG index, NHR, urea, diastolic blood pressure, hypertension, and left ventricular ejection fraction were independent risk factors for in-hospital MACE after PCI, and were used to construct the nomogram model. The C-index of the training and validation sets were 0.799 and 0.753, respectively, suggesting that the model discriminated well. Calibration and clinical decision curves also demonstrated that the nomogram model had good predictive power. CONCLUSION: In STEMI patients, increased TyG index and NHR were closely related to the occurrence of in-hospital MACE after PCI. Our constructed nomogram model has some value for predicting the occurrence of in-hospital MACE in STEMI patients.

Predictive value of atherogenic index of plasma and atherogenic index of plasma combined with low-density lipoprotein cholesterol for the risk of acute myocardial infarction.

Background and aims: Acute myocardial infarction (AMI) is a prevalent medical condition associated with significant morbidity and mortality rates. The principal underlying factor leading to myocardial infarction is atherosclerosis, with dyslipidemia being a key risk factor. Nonetheless, relying solely on a single lipid level is insufficient for accurately predicting the onset and progression of AMI. The present investigation aims to assess established clinical indicators in China, to identify practical, precise, and effective tools for predicting AMI. Methods: The study enrolled 267 patients diagnosed with acute myocardial infarction as the experimental group, while the control group consisted of 73 hospitalized patients with normal coronary angiography. The investigators collected general clinical data and relevant laboratory test results and computed the Atherogenic Index of Plasma (AIP) for each participant. Using acute myocardial infarction status as the dependent variable and controlling for confounding factors such as smoking history, fasting plasma glucose (FPG), low-density lipoprotein cholesterol (LDL-C), blood pressure at admission, and diabetes history, the researchers conducted multivariate logistic regression analysis with AIP as an independent variable. Receiver operating characteristic (ROC) curves were employed to determine the predictive value of AIP and AIP combined with LDL-C for acute myocardial infarction. Result: The results of the multivariate logistic regression analysis indicated that the AIP was an independent predictor of acute myocardial infarction. The optimal cut-off value for AIP to predict AMI was -0.06142, with a sensitivity of 81.3%, a specificity of 65.8%, and an area under the curve (AUC) of 0.801 (95% confidence interval [CI]: 0.743-0.859, P < 0.001). When AIP was combined with LDL-C, the best cut-off value for predicting acute myocardial infarction was 0.756107, with a sensitivity of 79%, a specificity of 74%, and an AUC of 0.819 (95% CI: 0.759-0.879, P < 0.001). Conclusions: The AIP is considered an autonomous determinant of risk for AMI. Utilizing the AIP index alone, as well as in conjunction with LDL-C, can serve as effective predictors of AMI.