Objective To establish a prediction model for heart failure (HF) after percutaneous coronary intervention (PCI) in elderly patients with acute myocardial infarction (AMI).
Methods A total of 326 elderly AMI patients in the First Hospital Affiliated to Harbin Medical University from January 2019 to December 2022 were retrospectively enrolled and divided into AMI-HF group (n=89) and non-AMI-HF group (n=237) based on the occurrence of HF at 6 months after surgery. Clinical materials of all the patients were collected through the electronic medical record system. Multivariate Logistic regression analysis was used to screen the risk factors of AMI complicated with HF and to construct a prediction model. The performance of the prediction model was evaluated by the receiver operating characteristic (ROC) curve.
Results Univariate analysis showed that compared with the non-AMI-HF group, the AMI-HF group had higher proportions of patients aged over 75 years, diabetics, and higher levels of Gensini score, cardiac troponin I (cTnI), N-terminal pro-brain natriuretic peptide (NT-proBNP), blood uric acid, and high-sensitivity C-reactive protein (hs-CRP), while the left ventricular ejection fraction (LVEF) and the proportion of grade 3 of Thrombolysis in Myocardial Infarction (TIMI) blood flow were lower in the AMI-HF group, and the differences were statistically significant (P < 0.05). Multivariate Logistic regression analysis revealed that age, Gensini score, NT-proBNP, and hs-CRP were independent risk factors for HF after PCI in elderly AMI patients (P < 0.05), while TIMI grade was a protective factor (P < 0.05). After calculating the C-Index based on the screened risk factors, the ROC curve was used to evaluate the predictive ability of the C-Index for HF, showing an area under the curve (AUC) of 0.878 (95%CI, 0.834 to 0.921), with a sensitivity of 86.52%, a specificity of 75.95%, and an accuracy of 78.83%.
Conclusion Age, Gensini score, NT-proBNP and hs-CRP are independent risk factors for HF after PCI in elderly AMI patients, while TIMI grade is a protective factor. The model constructed based on these factors can predict the risk of HF, thereby assisting in the early identification of high-risk HF populations and facilitating individualized interventions.
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