The global pandemic of novel coronavirus pneumonia (COVID-19) has resulted in millions of deaths over the past three years. As one of the most commonly affected extra-pulmonary organs, numerous studies have reported varying degrees of liver injury in a significant proportion of patients with COVID-19, particularly in severe and critically ill patients. Early prediction of liver dysfunction in hospitalized patients would facilitate the clinical management of COVID-19 and improve clinical prognosis, but reliable and valid predictive models are still lacking.MethodsWe collected data from 286 patients with RT-PCR confirmed COVID-19 admitted to various ICUs from the case system. These patients were randomly divided into a training cohort (50%) and a validation cohort (50%). In the training cohort, we first used ROC curves to measure the predictive efficiency of each of the variables for the development of liver damage during hospitalization in patients with COVID-19, followed by LASSO regression analysis to screen the variables for predictive models and logistic regression analysis to identify relevant risk factors. A nomogram based on these variables was created following the above model. Finally, the efficiency of the prediction models in the training and validation cohorts was assessed using AUC, consistency index (C index), calibration curves and Decision Curve Analysis.ResultsOut of a total of 80 parameters for COVID-19 patients admitted to the ICUs, 10 were determined to be significantly associated with the occurrence of liver dysfunction during hospitalization. Based on these predictors, further prediction models were used to construct and develop a nomogram that was offered for practical clinical application. The C-index of the column line graphs for the training and validation cohorts was 0.956 and 0.844 respectively. in addition, the calibration curves for the model showed a high degree of agreement between the predicted and actual incidence of liver dysfunction in patients with COVID-19.ConclusionBy developing a predictive model and associated nomogram, we predicted the incidence of liver dysfunction during hospitalization in patients with COVID-19 in the ICU. The model's predictive performance was determined in both the training and validation cohorts, contributing to the clinical management of COVID-19.