A diagnostic prediction model for the early detection of heart failure following primary percutaneous coronary intervention in patients with ST-elevation myocardial infarction.

Abstract

Background: In this study, we aimed to construct a robust diagnostic model that can predict the early onset of heart failure in patients with ST-elevation myocardial infarction (STEMI) following a primary percutaneous coronary intervention (PCI). This diagnostic model can facilitate the early stratification of high-risk patients, thereby optimizing therapeutic management.

Methods: We performed a retrospective analysis of 664 patients with STEMI who underwent their inaugural PCI. We performed logistic regression along with optimal subset regression and identified important risk factors associated with the early onset of heart failure during the time of admission. Based on these determinants, we constructed a predictive model and confirmed its diagnostic precision using a receiver operating characteristic (ROC) curve.

Results: The logistic and optimal subset regression analyses revealed the following three salient risk factors crucial for the early onset of heart failure: the Killip classification, the presence of renal insufficiency, and increased troponin T levels. The constructed prognostic model exhibited excellent discriminative ability, which was indicated by an area under the curve value of 0.847. The model's 95% confidence interval following 200 Bootstrap iterations was found to be between 0.767 and 0.925. The Hosmer-Lemeshow test revealed a chi-square value of 3.553 and a p-value of 0.938. Notably, the calibration of the model remained stable even after 500 Bootstrap evaluations. Furthermore, decision curve analysis revealed a substantial net benefit of the model.

Conclusion: We have successfully constructed a diagnostic prediction model to predict the incipient stages of heart failure in patients with STEMI following primary PCI. This diagnostic model can revolutionize patient care, allowing clinicians to quickly identify and create individualized interventions for patients at a higher risk.