Abstract
BACKGROUND: Acute kidney injury (AKI) is a severe and fatal complication of acute heart failure (AHF). Existing studies on AKI following AHF in the Chinese population have scarce insights available from the emergency department (ED). This study aimed to investigate the predictive factors of patients with AHF complicated with AKI in a Chinese ED cohort, and to establish a risk prediction model. METHODS: Hospitalized patients diagnosed with AHF in the ED from December 2016 to September 2023 were included. The overall dataset were divided into the training set and the testing set at a 7:3 ratio. Univariate and multivariate logistic regression analyses were performed to identify the risk factors for AKI in patients with AHF in the training set, leading to the development of a risk prediction model. The performance of the model was further assessed. RESULTS: A total of 789 patients with AHF were enrolled, with an AKI incidence of 29.7%. The mortality rates of the AKI and non-AKI groups were 23.1% and 7.6%, respectively. Logistic regression analysis showed that the levels of white blood cell (OR=2.368; 95%CI: 1.502-3.733, P<0.001), albumin (OR=2.669; 95%CI: 1.601-4.451, P<0.001), serum creatinine (OR=3.221; 95%CI: 1.935-5.363, P<0.001), and hemoglobin (OR=2.009; 95%CI: 1.259-3.205, P=0.003), maximum 24-h furosemide dosage (OR=2.196; 95%CI: 1.346-3.582, P=0.002), the use of non-invasive ventilation (OR=2.419; 95%CI: 1.454-4.024, P=0.001), and diabetes mellitus (OR=3.192; 95%CI: 2.014-5.059, P<0.001) were independent risk factors for AKI after AHF. These factors were subsequently incorporated into a risk prediction model. The area under the receiver operating characteristic (AUROC) curve for the predictive model was 0.815 (95%CI: 0.776-0.854) and 0.802 (95%CI: 0.776-0.854) in the training set and the testing set, respectively. CONCLUSION: This risk prediction model might assist physician to predict AKI following AHF effectively in the emergency setting.