OBJECTIVES

The purpose of this study was to evaluate the predictive value of the ABIC score in the long-term prognosis of elderly patients with acute exacerbations of chronic heart failure (CHF), and to explore whether its integration with other known prognostic variables could enhance the performance of a predictive model.

METHODS

This is a retrospective cohort study of elderly patients with acute exacerbation of CHF who were hospitalized for the first time. The main clinical outcome was all-cause mortality within three years. Cox regression and Lasso regression were used to screen variables. The screened variables, along with the ABIC score, were included in the multivariate Cox regression analysis to construct a predictive nomogram model.

RESULTS

A total of 365 patients with acute exacerbation of CHF were included. During the 3-year follow-up period, 87 patients experienced all-cause death, including 53 cardiac deaths. A total of 4 variables [NT-proBNP, serum urea nitrogen (BUN), red cell distribution width-coefficient of variation (RDW-CV) and prealbumin] were screened by univariate Cox regression analysis and Lasso regression analysis. The multivariate COX regression results showed that the risk of death increased by 33% with the increase of ABIC score by 1 point. The results of ROC curve analysis show that the area under the curve (AUC) of the ABIC score is 0.685, while the AUC of the Nomograph including the ABIC score is 0.840.

CONCLUSIONS

The ABIC score is associated with long-term adverse outcomes in elderly hospitalized patients with acute exacerbation of CHF. The integration with established prognostic variables (NT-proBNP, BUN, RDW-CV, prealbumin) significantly improves model performance, highlighting the combined value of multiple organ dysfunction and cardiac biomarkers in risk stratification.