Background: Different definitions of contrast-associated acute kidney injury (CA-AKI) have different predictive effects on prognosis. However, few studies explored the relationship between these definitions and long-term prognosis in patients with congestive heart failure (CHF). Thus, we aimed to evaluate this association and compared the population attributable risks (PAR) of different CA-AKI definitions.
Methods: This study enrolled 2,207 consecutive patients with CHF undergoing coronary angiography (CAG) in Guangdong Provincial People's Hospital. Two different definitions of CA-AKI were used: CA-AKIA was defined as an increase ≥.5 mg/dl or > 25% in serum creatinine (SCr) from baseline within 72 h after CAG, and CA-AKIB was defined as an increase of ≥.3 mg/dl or > 50% in SCr from baseline within 48 h after CAG. Kaplan-Meier methods and Cox regression were applied to evaluate the association between CA-AKI with long-term mortality. Population attributable risk (PAR) of different definitions for long-term prognosis was also calculated.
Results: During the 3.8-year median follow-up (interquartile range 2.1-6), the overall long-term mortality was 24.9%, and the long-term mortality in patients with the definitions of CA-AKIA and CA-AKIB were 30.4% and 34.3%, respectively. We found that CA-AKIA (HR: 1.44, 95% CI 1.19-1.74) and CA-AKIB (HR: 1.48, 95% CI 1.21-1.80) were associated with long-term mortality. The PAR was higher for CA-AKIA (9.6% vs. 8%).
Conclusions: Our findings suggested that CA-AKI was associated with long-term mortality in patients with CHF irrespective of its definitions. The CA-AKIA was a much better definition of CA-AKI in patients with CHF due to its higher PAR. For these patients, cardiologists should pay more attention to the presence of CA-AKI, especially CA-AKIA.
Keywords: congestive heart failure (CHF); contrast-associated acute kidney injury (CA-AKI); coronary angiography (CAG); long-term all-cause mortality; population attributable risk (PAR).
Copyright © 2022 Wang, Zheng, Li, Chen, Zhou, Lun, Ying, Zhang, Mai, Liu, Zhou, Lin, Yang, Chen, Liu, Liu, Chen and Tan.