Abstract
The objective of the current study is to assess the usefulness of HbA1cAp ratio in predicting in-hospital major adverse cardiac events (MACEs) among acute ST-segment elevation myocardial infarction (STEMI) patients that have undergone percutaneous coronary intervention (PCI). Further, the study aims to construct a ratio nomogram for prediction with this ratio. The training cohort comprised of 511 STEMI patients who underwent emergency PCI at the Huaibei Miners' General Hospital between January 2019 and May 2023. Simultaneously, 384 patients treated with the same strategy in First People's Hospital of Hefei formed the validation cohort during the study period. LASSO regression was used to screen predictors of nonzero coefficients, multivariate logistic regression was used to analyze the independent factors of in-hospital MACE in STEMI patients after PCI, and nomogram models and validation were established. The LASSO regression analysis demonstrated that systolic blood pressure, diastolic blood pressure, D-dimer, urea, and glycosylated hemoglobin A1c (HbA1c)/apolipoprotein A1 (ApoA1) were significant predictors with nonzero coefficients. Multivariate logistic regression analysis was further conducted to identify systolic blood pressure, D-dimer, urea, and HbA1c/ApoA1 as independent factors associated with in-hospital MACE after PCI in STEMI patients. Based on these findings, a nomogram model was developed and validated, with the C-index in the training set at 0.77 (95% CI: 0.723-0.817), and the C-index in the validation set at 0.788 (95% CI: 0.734-0.841), indicating excellent discrimination accuracy. The calibration curves and clinical decision curves also demonstrated the good performance of the nomogram models. In patients with STEMI who underwent PCI, it was noted that a higher HbA1c of the ApoA1 ratio is significantly associated with in-hospital MACE. In addition, a nomogram is constructed having considered the above-mentioned risk factors to provide predictive information on in-hospital MACE occurrence in these patients. In particular, this tool is of great value to the clinical practitioners in determination of patients with a high risk.