PTH Predicts the in-Hospital MACE After Primary Percutaneous Coronary Intervention for Acute ST-Segment Elevation Myocardial Infarction.

To investigate the correlation between serum parathyroid hormone (PTH) levels and in-hospital major adverse cardiovascular events (MACE) in patients with acute ST-segment elevation myocardial infarction (STEMI) after primary percutaneous coronary intervention (PCI), and establish a risk prediction model based on parameters such as PTH for in-hospital MACE. This observational retrospective study consecutively enrolled 340 patients who underwent primary PCI for STEMI between January 2016 and December 2020, divided into a MACE group (n=92) and a control group (n=248). The least absolute shrinkage and selection operator (LASSO) and logistic regression analyses were used to determine the risk factors for MACE after primary PCI. The rms package in R-studio statistical software was used to construct a nomogram, to detect the line chart C-index, and to draw a calibration curve. The decision curve analysis (DCA) method was used to evaluate the clinical application value and net benefit. Correlation analysis revealed that PTH level positively correlated with the occurrence of in-hospital MACE. Receiver operating characteristic curve analyses revealed that PTH had a good predictive value for in-hospital MACE. Multivariate logistic regression analysis indicated that Killip class II-IV, and FBG were independently associated with in-hospital MACE after primary PCI. A nomogram model was constructed using the above parameters. The model C-index was 0.894 and the calibration curve indicated that the model was well calibrated. The DCA curve suggested that the nomogram model was better than TIMI score model in terms of net clinical benefit. Serum PTH levels in patients with STEMI are associated with in-hospital MACE after primary PCI, and the nomogram risk prediction model based on PTH demonstrated good predictive ability with obvious clinical practical value.

Wu ZF ，Su WT ，Chen S ，Xu BD ，Zong GJ ，Fang CM ，Huang Z ，Hu XJ ，Wu GY ，Ma XL ... -  《Therapeutics and Clinical Risk Management》

Construction and evaluation of nomogram model for individualized prediction of risk of major adverse cardiovascular events during hospitalization after percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction.

Emergency percutaneous coronary intervention (PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI) helps to reduce the occurrence of major adverse cardiovascular events (MACEs) such as death, cardiogenic shock, and malignant arrhythmia, but in-hospital MACEs may still occur after emergency PCI, and their mortality is significantly increased once they occur. The aim of this study was to investigate the risk factors associated with MACE during hospitalization after PCI in STEMI patients, construct a nomogram prediction model and evaluate its effectiveness. A retrospective analysis of 466 STEMI patients admitted to our hospital from January 2018 to June 2022. According to the occurrence of MACE during hospitalization, they were divided into MACE group (n = 127) and non-MACE group (n = 339), and the clinical data of the two groups were compared; least absolute shrinkage and selection operator (LASSO) regression was used to screen out the predictors with non-zero coefficients, and multivariate Logistic regression was used to analyze STEMI Independent risk factors for in-hospital MACE in patients after emergency PCI; a nomogram model for predicting the risk of in-hospital MACE in STEMI patients after PCI was constructed based on predictive factors, and the C-index was used to evaluate the predictive performance of the prediction model; the Bootstrap method was used to repeat sampling 1,000 Internal validation was carried out for the second time, the Hosmer-Lemeshow test was used to evaluate the model fit, and the calibration curve was drawn to evaluate the calibration degree of the model. Receiver operating characteristic (ROC) curves were drawn to evaluate the efficacy of the nomogram model and thrombolysis in myocardial infarction (TIMI) score in predicting in-hospital MACE in STEMI patients after acute PCI. The results of LASSO regression showed that systolic blood pressure, diastolic blood pressure, Killip grade II-IV, urea nitrogen and left ventricular ejection fraction (LVEF), IABP, NT-ProBNP were important predictors with non-zero coefficients, and multivariate logistic regression analysis was performed to analyze that Killip grade II-IV, urea nitrogen, LVEF, and NT-ProBNP were independent factors for in-hospital MACE after PCI in STEMI patients; a nomogram model for predicting the risk of in-hospital MACE after PCI in STEMI patients was constructed with the above independent predictors, with a C-index of 0.826 (95% CI: 0.785-0.868) having a good predictive power; the results of H-L goodness of fit test showed χ2 = 1.3328, P = 0.25, the model calibration curve was close to the ideal model, and the internal validation C-index was 0.818; clinical decision analysis also showed that the nomogram model had a good clinical efficacy, especially when the threshold probability was 0.1-0.99, the nomogram model could bring clinical net benefits to patients. The nomogram model predicted a greater AUC (0.826) than the TIMI score (0.696) for in-hospital MACE after PCI in STEMI patients. Urea nitrogen, Killip class II-IV, LVEF, and NT-ProBNP are independent factors for in-hospital MACE after PCI in STEMI patients, and nomogram models constructed based on the above factors have high predictive efficacy and feasibility.

Fang C ，Chen Z ，Zhang J ，Jin X ，Yang M ... -  《Frontiers in Cardiovascular Medicine》

Predictive value of triglyceride glucose index combined with neutrophil-to-lymphocyte ratio for major adverse cardiac events after PCI for acute ST-segment elevation myocardial infarction.

Wang L ，Wang Y ，Wang W ，Wang Z ... -  《Scientific Reports》

In-hospital major adverse cardiovascular events after primary percutaneous coronary intervention in patients with acute ST-segment elevation myocardial infarction: a retrospective study under the China chest pain center (standard center) treatment system.

Patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI) are at high risk of major adverse cardiovascular events (MACE) despite timely treatment. This study aimed to investigate the independent predictors and their predictive value of in-hospital MACE after primary PCI in patients with acute STEMI under the China chest pain center (standard center) treatment system. We performed a single-center, retrospective study of 151 patients with acute STEMI undergoing primary PCI. All patients were treated under the China chest pain center (standard center) treatment system. The data collected included general data, vital signs, auxiliary examination results, data related to interventional therapy, and various treatment delays. The primary endpoint was the in-hospital MACE defined as the composite of all-cause death, stroke, nonfatal recurrent myocardial infarction, new-onset heart failure, and malignant arrhythmias. In-hospital MACE occurred in 71 of 151 patients with acute STEMI undergoing primary PCI. Logistic regression analysis showed that age, cardiac troponin I (cTnI), serum creatinine (sCr), multivessel coronary artery disease, and Killip class III/IV were risk factors for in-hospital MACE, whereas estimated glomerular filtration rate (eGFR), left ventricular ejection fraction (LVEF), systolic blood pressure (SBP), diastolic blood pressure (DBP), were protective factors, with eGFR, LVEF, cTnI, SBP, and Killip class III/IV being independent predictors of in-hospital MACE. The prediction model had good discrimination with an area under the curve = 0. 778 (95%CI: 0.690-0.865). Good calibration and clinical utility were observed through the calibration and decision curves, respectively. Our data suggest that eGFR, LVEF, cTnI, SBP, and Killip class III/IV independently predict in-hospital MACE after primary PCI in patients with acute STEMI, and the prediction model constructed based on the above factors could be useful for individual risk assessment and early management guidance.

Huang L ，Zhang J ，Huang Q ，Cui R ，Chen J ... -  《BMC Cardiovascular Disorders》

A nomogram risk prediction model for no-reflow after primary percutaneous coronary intervention based on rapidly accessible patient data among patients with ST-segment elevation myocardial infarction and its relationship with prognosis.

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. 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. 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). 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.

Liu Y ，Ye T ，Chen K ，Wu G ，Xia Y ，Wang X ，Zong G ... -  《-》