Establishment and validation of a clinical nomogram model based on serum YKL-40 to predict major adverse cardiovascular events during hospitalization in patients with acute ST-segment elevation myocardial infarction.

Objective: This study aimed to investigate the predictive value of a clinical nomogram model based on serum YKL-40 for major adverse cardiovascular events (MACE) during hospitalization in patients with acute ST-segment elevation myocardial infarction (STEMI). Methods: In this study, 295 STEMI patients from October 2020 to March 2023 in the Second People's Hospital of Hefei were randomly divided into a training group (n = 206) and a validation group (n = 89). Machine learning random forest model was used to select important variables and multivariate logistic regression was included to analyze the influencing factors of in-hospital MACE in STEMI patients; a nomogram model was constructed and the discrimination, calibration, and clinical effectiveness of the model were verified. Results: According to the results of random forest and multivariate analysis, we identified serum YKL-40, albumin, blood glucose, hemoglobin, LVEF, and uric acid as independent predictors of in-hospital MACE in STEMI patients. Using the above parameters to establish a nomogram, the model C-index was 0.843 (95% CI: 0.79-0.897) in the training group; the model C-index was 0.863 (95% CI: 0.789-0.936) in the validation group, with good predictive power; the AUC (0.843) in the training group was greater than the TIMI risk score (0.648), p < 0.05; and the AUC (0.863) in the validation group was greater than the TIMI risk score (0.795). The calibration curve showed good predictive values and observed values of the nomogram; the DCA results showed that the graph had a high clinical application value. Conclusion: In conclusion, we constructed and validated a nomogram based on serum YKL-40 to predict the risk of in-hospital MACE in STEMI patients. This model can provide a scientific reference for predicting the occurrence of in-hospital MACE and improving the prognosis of 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.

Association of CHA2DS2-VASC Score with in-Hospital Cardiovascular Adverse Events in Patients with Acute ST-Segment Elevation Myocardial Infarction.

Background: Acute ST-elevation myocardial infarction (STEMI) is a common clinical critical illness, and accurate, reliable, simple, and easy-to-remember tools are needed in clinical practice to quickly identify the risk of this condition in STEMI patients. This study investigates the predictive value of the admission CHA2DS2-VASc score for in-hospital MACE in STEMI patients. Methods: A total of 210 STEMI patients who visited the Chest Pain Center of the Second People's Hospital of Hefei from December 2019 to December 2021 were retrospectively analyzed. They were divided into MACE and non-MACE groups. The receiver operating characteristic curve (ROC) was used to assess the predictive value of the CHA2DS2-VASc score for MACE events during hospitalization. Results: The CHA2DS2-VASc score was higher in the MACE group than in the non-MACE group (P < 0.05), and multivariate logistic regression analysis showed that the CHA2DS2-VASc score was an independent risk factor for MACE events during hospitalization in STEMI patients (OR = 1.391, 95%CI 1.044-1.853, P=0.024); ROC curve analysis showed that the area under the curve (AUC) of the CHA2DS2-VASc score was 0.744, the sensitivity was 0.64, the specificity was 0.694, and the optimal cutoff value was 3.5 in predicting the risk of MACE events during hospitalization in STEMI patients. There were no significant differences between the GRACE score (0.744 VS.0.827) and TIMI score (0.744VS.0.745) (P > 0.05). Conclusion: The CHA2DS2-VASc score can successfully predict the occurrence of in-hospital MACE events in STEMI patients.

The Value of Serum YKL-40 and TNF-α in the Diagnosis of Acute ST-Segment Elevation Myocardial Infarction.

Background: Acute ST-segment elevation myocardial infarction (STEMI) is a serious cardiovascular disease that poses a great threat to the life and health of patients. Therefore, early diagnosis is important for STEMI patient treatment and prognosis. The purpose of this study was to investigate the value of serum YKL-40 and TNF-α in the diagnosis of STEMI. Methods: From October 2020 to February 2022, 120 patients with STEMI were admitted to the Chest Pain Center of the Second People's Hospital of Hefei, and 81 patients with negative coronary angiography were selected as the control group. Serum YKL-40 and TNF-α concentrations were measured by sandwich ELISA. Pearson correlation was used to analyze the correlation between serum YKL-40, TNF-α, and serum troponin I (cTnI) in STEMI patients; multivariate logistic regression analysis was used to screen independent risk factors for STEMI. Three diagnostic models were constructed: cTnI univariate model (model A), combined serum YKL-40 and TNF-α model other than cTnI (model B), and combined cTnI and serum YKL-40 and TNF-α model (model C). We assessed the clinical usefulness of the diagnostic model by comparing AUC with decision curve analysis (DCA). Results: Serum YKL-40 and TNF-α in the STEMI group were significantly higher than those in the control group (P < 0.001). On Pearson correlation analysis, there was a significant positive correlation between serum YKL-40, TNF-α, and cTnI levels in STEMI patients. Multivariate logistic regression analysis showed that serum YKL-40 and TNF-α were independent risk factors for the development of STEMI. The results of ROC analysis showed that the area under the curve (AUC) of serum YKL-40 for predicting the occurrence of STEMI was 0.704. The AUC of serum TNF-α for predicting the occurrence of STEMI was 0.852. The AUC of cTnI as a traditional model, model A, for predicting the occurrence of STEMI was 0.875. Model B predicted STEMI with an AUC of 0.851. The addition of serum YKL-40 and serum TNF-α to the traditional diagnostic model composed of cTnI constituted a new diagnostic model; that is, the AUC of model C for predicting the occurrence of STEMI was 0.930. Model C had a better net benefit between a threshold probability of 70-95% for DCA. Conclusion: In this study, we demonstrate the utility of serum YKL-40 and TNF-α as diagnostic markers for STEMI and the clinical utility of diagnostic models by combining serum YKL-40 and TNF-α with cTnI.

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.

Background: 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. Methods: 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. Results: 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. Conclusion: 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.