Influencing factors for acute kidney injury in acute liver failure and establishment of a predictive model / 临床肝胆病杂志

ABSTRACT

Objective To investigate the predictive factors for acute kidney injury (AKI) in patients with acute liver failure (ALF), and to establish a new predictive model. Methods Clinical data were collected from 253 patients who were diagnosed with ALF in The First Affiliated Hospital of Zhengzhou University from January 2015 to October 2021, and according to the presence or absence of AKI, these patients were divided into non-AKI group with 170 patients and AKI group with 83 patients. Related clinical data and laboratory markers were collected. Non-normally distributed continuous data were expressed as M ( P 25 - P 75 ), and the Mann-Whitney U test was used for comparison between two groups; categorical data were expressed as cases (%), and the chi-square test was used for comparison between two groups. The binary logistic regression analysis was used to investigate the risk factors for AKI in ALF patients, and the receiver operating characteristic (ROC) curve was used to evaluate the performance of the indices obtained in predicting AKI in ALF patients. Results Compared with the non-AKI group, the AKI group had a significantly higher proportion of patients with hypertension, diabetes, hepatic encephalopathy, ascites, and pulmonary infection, significantly higher levels of white blood cell count (WBC), international normalized ratio (INR), C-reactive protein, procalcitonin (PCT), neutrophil-to-lymphocyte ratio, and Model for End-Stage Liver Disease (MELD) score, and significantly lower levels of platelet count, lymphocyte-to-monocyte ratio, and PNI (all P < 0.05). The multivariate logistic regression analysis showed that WBC (odds ratio [ OR ]=1.267, 95% confidence interval [ CI ] 1.124-1.428, P < 0.001), INR ( OR =1.663, 95% CI 1.205-2.293, P =0.002), PCT ( OR =1.416, 95% CI 1.137-1.764, P =0.002), and MELD score ( OR =1.098, 95% CI 1.029-1.172, P =0.005) were risk factors for the development of AKI in patients with ALF. The ROC curve analysis showed that the combination of WBC+INR+PCT+MELD had the largest area under the ROC curve (AUC) of 0.908 in predicting AKI in ALF patients, while WBC, INR, PCT, and MELD alone had an AUC of 0.776, 0.771, 0.746, and 0.780, respectively, in predicting AKI. Conclusion WBC, INR, PCT, and MELD score are independent influencing factors for AKI in patients with ALF, and the predictive model established based on these four indices has a relatively high predictive value.