Construction and validation of a personalized risk prediction model for in-hospital mortality in patients with acute myocardial infarction undergoing percutaneous coronary intervention.

Although emergency Percutaneous Coronary Intervention (PCI) has been shown to reduce mortality in patients with Acute Myocardial Infarction (AMI), the risk of in-hospital death remains high. In this study, the authors aimed to identify risk factors associated with in-hospital mortality in AMI patients who underwent PCI, develop a nomogram prediction model, and evaluate its effectiveness. The authors retrospectively analyzed data from 1260 patients who underwent emergency PCI at Dongyang People's Hospital between June 1, 2013, and December 31, 2021. Patients were divided into two groups based on in-hospital mortality: the death group (n = 61) and the survival group (n = 1199). Clinical data between the two groups were compared. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select non-zero coefficients of predictive factors. Multivariable logistic regression analysis was then performed to identify independent risk factors for in-hospital mortality in AMI patients after emergency PCI. A nomogram model for predicting the risk of in-hospital mortality in AMI patients after PCI was constructed, and its predictive performance was evaluated using the c-index. Internal validation was performed using the bootstrap method with 1000 resamples. The Hosmer-Lemeshow test was used to assess the goodness of fit, and a calibration curve was plotted to evaluate the model's calibration. LASSO regression identified d-dimer, B-type natriuretic peptide, white blood cell count, heart rate, aspartate aminotransferase, systolic blood pressure, and the presence of postoperative respiratory failure as important predictive factors for in-hospital mortality in AMI patients after PCI. Multivariable logistic regression analysis showed that d-dimer, B-type natriuretic peptide, white blood cell count, systolic blood pressure, and the presence of postoperative respiratory failure were independent factors for in-hospital mortality. A nomogram model for predicting the risk of in-hospital mortality in AMI patients after PCI was constructed using these independent predictive factors. The Hosmer-Lemeshow test yielded a Chi-Square value of 9.43 (p = 0.331), indicating a good fit for the model, and the calibration curve closely approximated the ideal model. The c-index for internal validation was 0.700 (0.560‒0.834), further confirming the predictive performance of the model. Clinical decision analysis demonstrated that the nomogram model had good clinical utility, with an area under the ROC curve of 0.944 (95 % CI 0.903‒0.963), indicating excellent discriminative ability. This study identified B-type natriuretic peptide, white blood cell count, systolic blood pressure, d-dimer, and the presence of respiratory failure as independent factors for in-hospital mortality in AMI patients undergoing emergency PCI. The nomogram model based on these factors showed high predictive accuracy and feasibility.

Xu BZ ，Wang B ，Chen JP ，Xu JG ，Wu XY ... -  《-》

Risk analysis and risk prediction of in-hospital heart failure in patients with acute myocardial infarction after emergency intervention surgery.

Percutaneous coronary intervention (PCI) can rapidly open the culprit vessels of acute myocardial infarction (AMI) and save ischemic myocardium, but it is often accompanied by a variety of complications, including heart failure (HF).Please check if the article title is presented correctlyYes, it is presented correctly OBJECTIVE: We aimed to (i) analyze the possible risk factors affecting the occurrence of in-hospital HF after emergency PCI in patients with AMI through clinical data and (ii) establish a personalized risk prediction model for the occurrence of HF after emergency PCI in patients with AMI.Please check if the author names and affiliations are captured correctlyYes, they are captured correctly METHODS: Clinical data of 676 AMI patients who consecutively underwent emergency PCI between January 2020 and October 2023 at the First Affiliated Hospital of Guangxi University of Chinese Medicine were collected. Based on whether in-hospital HF occurred after PCI, the study subjects were divided into the HF group (91 cases) and the non-HF group (585 cases). Independent risk factors were screened using univariate and multivariate logistic regression. A nomogram model of the risk of HF was drawn using R, and the discriminative power was evaluated by calculating the area under the ROC curve and drawing the calibration curve and decision curve. In this study, the incidence of in-hospital HF events in AMI patients after emergency PCI was 13.46%. The analysis showed that age, troponin levels, D-dimer levels, left ventricular ejection fraction (LVEF), and Gensini score were independent predictors of the occurrence of in-hospital HF in AMI patients after emergency PCI (P < 0.05). The AUC of the nomogram model were 0.87 (95% CI: 0. 82-0.91) and 0.85 (95% CI: 0. 76-0.93) in the training and validation sets, respectively. The Hosmer-Lemeshow goodness-of-fit test in the training set suggested that the difference between predicted and actual risks of the predictive model was not statistically significant (χ2 = 5.8185, P = 0.6676), and this was confirmed by the Hosmer-Lemeshow goodness-of-fit test in the validation set (χ2 = 9.4774, P = 0.3036). The predictive model for the risk of in-hospital HF in AMI patients after emergency PCI includes age, troponin levels, D-dimer levels, LVEF, and Gensini score. It has a good differentiation ability and good accuracy, it can be used to intuitively and independently screen high-risk populations, and it has high predictive value for the occurrence of HF after PCI in AMI patients, so it can be used to assist clinicians in early screening, in identifying patients at high risk of postoperative HF, and in the implementation of targeted intervention therapy.Please check if "Strengths and limitations of this study" was captured and presented correctlyYes, it was captured and presented correctly.

Song H ，Qin WB ，Yang FF ，Tang WZ ，He GX ... -  《BMC Cardiovascular Disorders》

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》

Evaluation of a nomogram model for predicting in-hospital mortality risk in patients with acute ST-elevation myocardial infarction and acute heart failure post-PCI.

Yu F ，Xu Y ，Peng J 《-》

[Predictive value of hemoglobin to serum creatinine ratio combined with serum uric acid for in-hospital mortality after emergency percutaneous coronary intervention in patients with acute myocardial infarction].

Bao F ，Yang C ，Zhou G 《-》