Liver Chemistries in COVID-19 Patients with Survival or Death: A Meta-Analysis

Background and AimsAlthough abnormal liver chemistries are linked to higher risk of death related to coronavirus disease (COVID-19), liver manifestations may be diverse and even confused. Thus, we performed a meta-analysis of published liver manifestations and described the liver damage in COVID-19 patients with death or survival. MethodsWe searched PubMed, Google Scholar, medRxiv, bioRxiv, Cochrane Library, Embase, and three Chinese electronic databases through April 22, 2020. We analyzed pooled data on liver chemistries stratified by the main clinical outcome of COVID-19 using a fixed or random-effects model. ResultsIn the meta-analysis of 18 studies, which included a total of 2,862 patients, the pooled mean alanine aminotransferase (ALT) was 30.9 IU/L in the COVID-19 patients with death and 26.3 IU/L in the COVID-19 patients discharged alive (p < 0.0001). The pooled mean aspartate aminotransferase (AST) level was 45.3 IU/L in the COVID-19 patients with death while 30.1 IU/L in the patients discharged alive (p < 0.0001). Compared with the discharged alive cases, the dead cases tended to have lower albumin levels but longer prothrombin time, and international standardized ratio. ConclusionsIn this meta-analysis, according to the main clinical outcome of COVID-19, we comprehensively described three patterns of liver impairment related to COVID-19, hepatocellular injury, cholestasis, and hepatocellular disfunction. Patients died from COVID-19 tend to have different liver chemistries from those are discharged alive. Close monitoring of liver chemistries provides an early warning against COVID-19 related death. Lay SummaryAbnormal liver chemistries are linked to higher risk of death related to coronavirus disease (COVID-19). We performed a meta-analysis of 18 studies that included a total of 2,862 patients with COVID-19. We noted that patients died from COVID-19 tend to have different liver chemistries from those are discharged alive and close monitoring of liver chemistries provides early warning against COVID-19 related death.

Liver Chemistries in Patients with Severe or Non-Severe COVID-19: a Meta-Analysis

Background and AimsCumulating observations have indicated that patients with coronavirus disease (COVID-19) undergo different patterns of liver impairment. We performed a meta-analysis of published liver manifestations and described the liver damage in COVID-19. MethodsWe searched PubMed, Google Scholar, Embase, Cochrane Library, medRxiv, bioRxiv, and three Chinese electronic databases through April 18, 2020, in accordance with the Preferred Reporting Items for Meta-Analyses. We analyzed pooled data on liver chemistries stratified by COVID-19 severity using a fixed or random-effects model. ResultsIn the meta-analysis of 37 studies, which included a total of 6,235 patients, the pooled mean alanine aminotransferase (ALT) was 36.4 IU/L in the severe COVID-19 cases and 27.8 IU/L in the non-severe cases (95% confidence interval [CI]: - 9.4 to - 5.1, p < 0.0001). The pooled mean aspartate aminotransferase (AST) was 46.8 IU/L in the severe cases and 30.4 IU/L in the non-severe cases (95% CI: - 15.1 to - 10.4, p < 0.0001). Furthermore, regardless of disease severity, the AST level is often higher than the ALT level. Compared with the non-severe cases, the severe cases tended to have higher {gamma}-glutamyltransferase levels but lower albumin levels. ConclusionsIn this meta-analysis, we comprehensively described three patterns of liver impairment related to COVID-19, namely hepatocellular injury, cholestasis, and hepatocellular disfunction, according to COVID-19 severity. Patients with abnormal liver test results are at higher risk of progression to severe disease. Close monitoring of liver chemistries provides an early warning against disease progression. Lay SummaryData on abnormal liver chemistries related to coronavirus disease (COVID-19) are cumulating but are potentially confusing. We performed a meta-analysis of 37 studies that included a total of 6,235 patients with COVID-19. We noted that patients with abnormal liver test results are at higher risk of progression to severe disease and close monitoring of liver chemistries provides early warning against disease progression.

Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT Scan Images

PurposeCOVID-19 has become global threaten. CT acts as an important method of diagnosis. However, human-based interpretation of CT imaging is time consuming. More than that, substantial inter-observer-variation cannot be ignored. We aim at developing a diagnostic tool for artificial intelligence (AI)-based classification of CT images for recognizing COVID-19 and other common infectious diseases of the lung. Experimental DesignIn this study, images were retrospectively collected and prospectively analyzed using machine learning. CT scan images of the lung that show or do not show COVID-19 were used to train and validate a classification framework based on convolutional neural network. Five conditions including COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, pulmonary tuberculosis, and normal lung were evaluated. Training and validation set of images were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were collected from Zhongshan Hospital Xiamen University and the fifth Hospital of Wuhan. ResultsAccuracy, sensitivity, and specificity of the AI framework were reported. For test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, and pulmonary tuberculosis were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, accuracy, sensitivity, specificity, PPV, and NPV of recognizing COVID-19 were 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively. ConclusionsThe performance of the proposed AI framework has excellent performance of recognizing COVID-19 and other common infectious diseases of the lung, which also has balanced sensitivity and specificity.

Predictors and scoring system for sustained complete response to conventional interferon-alpha therapy on chronic hepatitis B / 中华肝脏病杂志

<p><b>OBJECTIVE</b>To establish a predictive scoring system which may serve for the prediction of sustained response to conventional interferon-alpha (IFN-alpha) treatment on chronic hepatitis B.</p><p><b>METHODS</b>A total of 474 IFN-alpha treated hepatitis B virus e antigen (HBeAg)-positive patients were enrolled in the present study. The patients' baseline characteristics, such as age, gender, aminotransferases, activity grading (G) of intrahepatic inflammation, score (S) of liver fibrosis, hepatitis B virus (HBV) DNA and genotype were evaluated; therapy duration and response of each patient at the 24th wk after cessation of IFN-alpha treatment were also recorded. A predictive scoring system for a sustained complete response (CR) to IFN-alpha therapy was established based on genetic algorithm. About 10% of the patients were randomly drawn out as the test set. Responses to IFN-alpha therapy were divided into CR, partial response (PR) and non-response (NR). The mixed set of PR and NR was recorded as PR + NR.</p><p><b>RESULTS</b>For the scoring system, the sensitivity and specificity were 78.8% and 80.6%, respectively.</p><p><b>CONCLUSION</b>This SCR scoring system has satisfying prediction efficiency and is easily employed in clinical practice. With this scoring system, practitioners can propose individualized decisions that have an integrated foundation on both evidence-based medicine and personal characteristics.</p>