ABSTRACT
Introduction: Abnormal liver biochemistry is not a rare finding in the context of SARS-CoV-2 infection, regardless of patients having pre-existing chronic disease or not. Content: This review examines the current body of knowledge on the relationship between COVID-19 and liver injury, which is frequently found in this setting. Summary: Although the pathogenesis of liver injury is not fully understood, it has been suggested to be the result of a combination of multiple factors. These include direct injury caused by the virus, immune system hyperactivation, ischemic and drug-induced injury. The prognostic valor of these alterations is also the subject of intense research. Due to their potential impact, these alterations require proper management and treatment, especially in patients with chronic liver disease or liver transplant recipients. Outlook: Some aspects associated with liver injury during COVID-19, especially in severe presentations, are not well understood. Studies assessing the clinical impact of COVID-19 on the healthy or diseased liver may help adjust treatment and immunization guidelines to the profile of the patient.
ABSTRACT
Introduction: Abnormal liver biochemistry is not a rare finding in the context of SARS-CoV-2 infection, regardless of patients having pre-existing chronic disease or not. Content: This review examines the current body of knowledge on the relationship between COVID-19 and liver injury, which is frequently found in this setting. Summary: Although the pathogenesis of liver injury is not fully understood, it has been suggested to be the result of a combination of multiple factors. These include direct injury caused by the virus, immune system hyperactivation, ischemic and drug-induced injury. The prognostic valor of these alterations is also the subject of intense research. Due to their potential impact, these alterations require proper management and treatment, especially in patients with chronic liver disease or liver transplant recipients. Outlook: Some aspects associated with liver injury during COVID-19, especially in severe presentations, are not well understood. Studies assessing the clinical impact of COVID-19 on the healthy or diseased liver may help adjust treatment and immunization guidelines to the profile of the patient.
Subject(s)
Betacoronavirus , Coronavirus Infections/cerebrospinal fluid , Encephalitis/cerebrospinal fluid , Interleukin-1beta/cerebrospinal fluid , Interleukin-6/cerebrospinal fluid , Peptidyl-Dipeptidase A/cerebrospinal fluid , Pneumonia, Viral/cerebrospinal fluid , Adult , Angiotensin-Converting Enzyme 2 , Betacoronavirus/isolation & purification , Biomarkers/cerebrospinal fluid , COVID-19 , Coronavirus Infections/complications , Coronavirus Infections/diagnosis , Encephalitis/diagnosis , Encephalitis/etiology , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/complications , Pneumonia, Viral/diagnosis , SARS-CoV-2ABSTRACT
BACKGROUND: COVID-19 causes high mortality and long hospitalization periods. The aim of this study was to search for new early prognostic strategies accessible to most health care centers. METHODS: Laboratory results, demographic and clinical data from 500 patients with positive SARS-CoV-2 infection were included in our study. The data set was split into training and test set prior to generating different multivariate models considering the occurrence of death as the response variable. A final computational method called the BGM score was obtained by combining the previous models and is available as an interactive web application. RESULTS: The logistic regression model comprising age, creatinine (CREA), D-dimer (DD), C-reactive protein (CRP), platelet count (PLT), and troponin I (TNI) showed a sensitivity of 47.3%, a specificity of 98.7%, a kappa of 0.56, and a balanced accuracy of 0.73. The CART classification tree yielded TNI, age, DD, and CRP as the most potent early predictors of mortality (sensitivity = 68.4%, specificity = 92.5%, kappa = 0.61, and balanced accuracy = 0.80). The artificial neural network including age, CREA, DD, CRP, PLT, and TNI yielded a sensitivity of 66.7%, a specificity of 92.3%, a kappa of 0.54, and a balanced accuracy of 0.79. Finally, the BGM score surpassed the prediction accuracy performance of the independent multivariate models, yielding a sensitivity of 73.7%, a specificity of 96.5%, a kappa of 0.74, and a balanced accuracy of 0.85. CONCLUSIONS: The BGM score may support clinicians in managing COVID-19 patients and providing focused interventions to those with an increased risk of mortality.