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Liver injury in patients with COVID-19: clinical profiles, CT findings, the correlation of the severity with liver injury.

ABSTRACT: BACKGROUND AND AIMS:Liver injury is found in some of patients with COVID-19. Liver injury of COVID-19 patients based on severity grading and abdominal radiological signs have not been reported until now. The aim of our study is to determine clinical profiles of the patients based on severity grading, describe abdominal radiological signs, and investigate the correlations of the severity with clinical profiles and radiological signs. METHODS:This retrospective cohort study included 115 patients with COVID-19 from Jan 2020 to Feb 2020. Medical records of the patients were collected and CT images were reviewed. RESULTS:Common clinical manifestations of patients with COVID-19 were fever (68.70%), cough (56.52%), fatigue (31.30%); some of them had gastrointestinal symptoms (diarrhea, 12.17%; nausea or vomiting 7.83%; inappetence, 7.83%). Abnormal liver function was observed in some of patients with COVID-19. Significant differences in the levels of AST, albumin,CRP were observed among different groups classified by the severity. Common findings of upper abdominal CT scan were liver hypodensity (26.09%) and pericholecystic fat stranding (21.27%); liver hypodensity was more frequently found in critical cases (58.82%). The severity of COVID-19 correlated with semi-quantitative CT score of pulmonary lesions, CT-quantified liver/spleen attenuation ratio in patients with COVID-19. CONCLUSIONS:Some of the patients with COVID-19 displayed liver damage revealed by liver functional tests and upper abdominal CT imaging, and the severity of COVID-19 patients correlated with some of liver functional tests and CT signs; thus, it will allow an earlier identification of high-risk patients for early effective intervention.

SUBMITTER: Lei P

PROVIDER: S-EPMC7472678 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Liver injury in patients with COVID-19: clinical profiles, CT findings, the correlation of the severity with liver injury.

Lei Ping P Zhang Lan L Han Ping P Zheng Chuansheng C Tong Qiaoxia Q Shang Haitao H Yang Fan F Hu Yu Y Li Xin X Song Yuhu Y

Hepatology international 20200904 5

<h4>Background and aims</h4>Liver injury is found in some of patients with COVID-19. Liver injury of COVID-19 patients based on severity grading and abdominal radiological signs have not been reported until now. The aim of our study is to determine clinical profiles of the patients based on severity grading, describe abdominal radiological signs, and investigate the correlations of the severity with clinical profiles and radiological signs.<h4>Methods</h4>This retrospective cohort study included ...[more]

PMID: 32886333

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Project description:ObjectivesCT findings of COVID-19 look similar to other atypical and viral (non-COVID-19) pneumonia diseases. This study proposes a clinical computer-aided diagnosis (CAD) system using CT features to automatically discriminate COVID-19 from non-COVID-19 pneumonia patients.MethodsOverall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited. Twenty radiological features were extracted from CT images to evaluate the pattern, location, and distribution of lesions of patients in both groups. All significant CT features were fed in five classifiers namely decision tree, K-nearest neighbor, naïve Bayes, support vector machine, and ensemble to evaluate the best performing CAD system in classifying COVID-19 and non-COVID-19 cases.ResultsLocation and distribution pattern of involvement, number of the lesion, ground-glass opacity (GGO) and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features to classify COVID-19 from non-COVID-19 groups. Our proposed CAD system obtained the sensitivity, specificity, and accuracy of 0.965, 93.54%, 90.32%, and 91.94%, respectively, using ensemble (COVIDiag) classifier.ConclusionsThis study proposed a COVIDiag model obtained promising results using CT radiological routine features. It can be considered an adjunct tool by the radiologists during the current COVID-19 pandemic to make an accurate diagnosis.Key points• Location and distribution of involvement, number of lesions, GGO and crazy-paving, consolidation, reticular, bronchial wall thickening, nodule, air bronchogram, cavity, pleural effusion, pleural thickening, and lymphadenopathy are the significant features between COVID-19 from non-COVID-19 groups. • The proposed CAD system, COVIDiag, could diagnose COVID-19 pneumonia cases with an AUC of 0.965 (sensitivity = 93.54%; specificity = 90.32%; and accuracy = 91.94%). • The AUC, sensitivity, specificity, and accuracy obtained by radiologist diagnosis are 0.879, 87.10%, 88.71%, and 87.90%, respectively.

Dataset Information

Liver injury in patients with COVID-19: clinical profiles, CT findings, the correlation of the severity with liver injury.

Publications

Liver injury in patients with COVID-19: clinical profiles, CT findings, the correlation of the severity with liver injury.

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