Development and Validation of a dynamic online nomogram predicting acute kidney injury in critically ill patients with cirrhosis

Background: This study aimed to develop a tool for predicting the occurrence of acute kidney injury (AKI) in critically ill patients with cirrhosis. Methods: Eligible patients with cirrhosis were identified from the Medical Information Mart for Intensive Care database. Demographic data, laboratory examinations, and interventions were obtained. After splitting the population into training and validation cohorts, the least absolute shrinkage and selection operator regression model was used to select factors and construct the dynamic online nomogram. Calibration and discrimination were used to assess nomogram performance, and clinical utility was evaluated by decision curve analysis (DCA). Results: A total of 1282 patients were included in the analysis, and 773 developed AKI. The mean arterial pressure, urine volume, white blood cell count, total bilirubin level, and Glasgow Coma Score were identified as predictors of AKI. The developed model had a good ability to differentiate AKI from non-AKI, with AUCs of 0.796 and 0.782 in the training and validation cohorts, respectively. Moreover, the nomogram model showed good calibration. DCA showed that the nomogram had a superior overall net benefit within wide and practical ranges of threshold probabilities. Conclusions: The dynamic online nomogram can be an easy-to-use tool for predicting the individualized risk of AKI in critically ill patients with cirrhosis.