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To establish a simple myocardial ischemia‒reperfusion injury (MIRI) manifestation grading system based on clinical manifestations and coronary angiography during primary percutaneous coronary intervention (PPCI). All STEMI patients treated with PPCI from June 2018 to November 2019 were included. According to the MIRI manifestation grade, patients were divided into four grades (I–IV). Laboratory and clinical indicators of the patients and the occurrence of major adverse cardiac events (MACEs) within one year of follow-up were analyzed. A total of 300 patients were included. The higher the MIRI manifestation grade, the lower was the high-density lipoprotein cholesterol (HDL-C); the higher were the C-reactive protein (CRP), lipoprotein(a) [LP(a)], and peak levels of high-sensitivity troponin T (hs-cTnT), creatine kinase (CK-MB), and N-terminal pro-B-type natriuretic peptide (NT-proBNP); and the higher were the proportions of right coronary artery (RCA) and multivessel lesions (P < 0.05). The left ventricular end-diastolic dimension (LVEDD) and E/e′ values of patients with higher grades were significantly increased, while the LVEF, left ventricular short-axis functional shortening (LVFS) and E/A values were significantly decreased (P < 0.05). The one-year cumulative incidence of major adverse cardiac events (MACEs) in patients with grade I–IV disease was 7.7% vs. 26.9% vs. 48.4% vs. 93.3%, respectively, P < 0.05. The higher the MIRI manifestation grade, the more obvious is the impact on diastolic and systolic function and the higher is the cumulative incidence of MACEs within one year, especially in patients with multivessel disease, low HDL-C, high CRP, high LP(a) levels, and the RCA as the infarction-related artery.

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The aim of the study was to investigate the factors influencing contrast-induced acute kidney injury (CI-AKI) after percutaneous intervention (PCI) in patients with acute coronary syndrome (ACS) with diabetes mellitus (DM). A total of 1073 patients with ACS combined with DM who underwent PCI at the Affiliated Hospital of Xuzhou Medical University were included in this study. We divided the patients into the CI-AKI and non-CI-AKI groups according to whether CI-AKI occurred or not. The patients were then randomly assigned to the training and validation sets at a proportion of 7 : 3. Based on the results of the LASSO regression and multivariate analyses, we determined that the subtypes of ACS, age, multivessel coronary artery disease, hyperuricemia, low-density lipoprotein cholesterol, triglyceride-glucose index, and estimated glomerular filtration rate were independent predictors on CI-AKI after PCI in patients with ACS combined with DM. Using the above indicators to develop the nomogram, the AUC-ROC of the training and validation sets were calculated to be 0.811 (95% confidence interval (CI): 0.766-0.844) and 0.773 (95% CI: 0.712-0.829), respectively, indicating high prediction efficiency. After verification by the Bootstrap internal verification, we found that the calibration curves showed good agreement between the nomogram predicted and observed values. And the DCA results showed that the nomogram had a high clinical application. In conclusion, we constructed and validated the nomogram to predict CI-AKI risk after PCI in patients with ACS and DM. The model can provide a scientific reference for predicting the occurrence of CI-AKI and improving the prognosis of patients.

The high incidence of readmission for patients with reduced ejection fraction heart failure (HFrEF) can seriously affect the prognosis. In this study, we aimed to build a simple predictive model to predict the risk of heart failure (HF) readmission in patients with HFrEF within one year of discharge from the hospital. This retrospective study enrolled patients with HFrEF evaluated in the Heart Failure Center of the Affiliated Hospital of Xuzhou Medical University from January 2018 to December 2020. The patients were allocated into the readmission or nonreadmission group, according to whether HF readmission occurred within 1 year of hospital discharge. Subsequently, all patients were randomly divided into training and validation sets in a 7 : 3 ratio. A nomogram was established according to the results of univariate and multivariate logistic regression analysis. Finally, the area under the receiver operating characteristic curve (AUC-ROC), calibration plot, and decision curve analysis (DCA) were used to validate the nomogram. Independent risk factors for HF readmission of patients with HFrEF within 1 year of hospital discharge were as follows: age, body mass index, systolic blood pressure, diabetes mellitus, left ventricular ejection fraction, and angiotensin receptor-neprilysin inhibitors. The AUC-ROC of the training and validation sets were 0.833 (95% confidence interval (CI): 0.793-0.866) and 0.794 (95% CI: 0.727-0.852), respectively, which have an excellent distinguishing ability. The predicted and observed values of the calibration curve also showed good consistency. DCA also confirmed that the nomogram had good clinical value. In conclusion, we constructed an accurate and straightforward nomogram model for predicting the 1-year HF readmission risk in patients with HFrEF. This nomogram can guide early clinical intervention and improve patient prognosis.