The Associations of Lymphocyte Ratio and Neutrophil Ratio on Liver Dysfunction in COVID-19 Patients

Liver injury in COVID-19: Clinical features, potential mechanisms, risk factors and clinical treatments

The coronavirus disease 2019 (COVID-19) pandemic has been a serious threat to global health for nearly 3 years. In addition to pulmonary complications, liver injury is not uncommon in patients with novel COVID-19. Although the prevalence of liver injury varies widely among COVID-19 patients, its incidence is significantly increased in severe cases. Hence, there is an urgent need to understand liver injury caused by COVID-19. Clinical features of liver injury include detectable liver function abnormalities and liver imaging changes. Liver function tests, computed tomography scans, and ultrasound can help evaluate liver injury. Risk factors for liver injury in patients with COVID-19 include male sex, preexisting liver disease including liver transplantation and chronic liver disease, diabetes, obesity, and hypertension. To date, the mechanism of COVID-19-related liver injury is not fully understood. Its pathophysiological basis can generally be explained by systemic inflammatory response, hypoxic damage, ischemia-reperfusion injury, and drug side effects. In this review, we systematically summarize the existing literature on liver injury caused by COVID-19, including clinical features, underlying mechanisms, and potential risk factors. Finally, we discuss clinical management and provide recommendations for the care of patients with liver injury.

A prediction model for COVID-19 liver dysfunction in patients with normal hepatic biochemical parameters

Coronavirus disease 2019 (COVID-19) patients with liver dysfunction (LD) have a higher chance of developing severe and critical disease. The routine hepatic biochemical parameters ALT, AST, GGT, and TBIL have limitations in reflecting COVID-19-related LD. In this study, we performed proteomic analysis on 397 serum samples from 98 COVID-19 patients to identify new biomarkers for LD. We then established 19 simple machine learning models using proteomic measurements and clinical variables to predict LD in a development cohort of 74 COVID-19 patients with normal hepatic biochemical parameters. The model based on the biomarker ANGL3 and sex (AS) exhibited the best discrimination (time-dependent AUCs: 0.60-0.80), calibration, and net benefit in the development cohort, and the accuracy of this model was 69.0-73.8% in an independent cohort. The AS model exhibits great potential in supporting optimization of therapeutic strategies for COVID-19 patients with a high risk of LD. This model is publicly available at https://xixihospital-liufang.shinyapps.io/DynNomapp/.