ABSTRACT
Background: Patients with pre-existing cirrhosis are considered at increased risk of severe coronavirus disease 2019 (COVID-19) but the clinical course in these patients has not yet been reported. This study aimed to provide a detailed report of the clinical characteristics and outcomes among COVID-19 patients with pre-existing cirrhosis. Methods: In this retrospective, multicenter cohort study, we consecutively included all adult inpatients with laboratory-confirmed COVID-19 and pre-existing cirrhosis that had been discharged or had died by 24 March 2020 from 16 designated hospitals in China. Demographic, clinical, laboratory and radiographic findings on admission, treatment, complications during hospitalization and clinical outcomes were collected and compared between survivors and non-survivors. Findings: Twenty-one patients were included in this study, of whom 16 were cured and 5 died in hospital. Seventeen patients had compensated cirrhosis and hepatitis B virus infection was the most common etiology. Lymphocyte and platelet counts were lower, and direct bilirubin levels were higher in patients who died than those who survived (p= 0.040, 0.032, and 0.006, respectively). Acute respiratory distress syndrome and secondary infection were both the most frequently observed complications. Only one patient developed acute on chronic liver failure. Of the 5 non-survivors, all patients developed acute respiratory distress syndrome and 2 patients progressed to multiple organ dysfunction syndrome. Interpretation: Lower lymphocyte and platelet counts, and higher direct bilirubin level might represent poor prognostic indicators in SARS-CoV-2-infected patients with pre-existing cirrhosis.
Subject(s)
Fibrosis , Coinfection , Multiple Organ Failure , Respiratory Distress Syndrome , End Stage Liver Disease , Severe Acute Respiratory Syndrome , COVID-19 , Hepatitis BABSTRACT
Abstract Objectives To develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection. Design Cross-sectional Setting Multicenter Participants A total of 52 patients with laboratory-confirmed SARS-CoV-2 infection and their initial CT images were enrolled from 5 designated hospitals in Ankang, Lishui, Zhenjiang, Lanzhou, and Linxia between January 23, 2020 and February 8, 2020. As of February 20, patients remained in hospital or with non-findings in CT were excluded. Therefore, 31 patients with 72 lesion segments were included in the final analysis. Intervention CT radiomics models based on logistic regression (LR) and random forest (RF) were developed on features extracted from pneumonia lesions in training and inter-validation datasets. The predictive performance was further evaluated in test dataset on lung lobe- and patients-level. Main outcomes Short-term hospital stay ([≤]10 days) and long-term hospital stay (>10 days). Results The CT radiomics models based on 6 second-order features were effective in discriminating short- and long-term hospital stay in patients with pneumonia associated with SARS-CoV-2 infection, with areas under the curves of 0.97 (95%CI 0.83-1.0) and 0.92 (95%CI 0.67-1.0) by LR and RF, respectively, in the test dataset. The LR model showed a sensitivity and specificity of 1.0 and 0.89, and the RF model showed similar performance with sensitivity and specificity of 0.75 and 1.0 in test dataset. Conclusions The machine learning-based CT radiomics models showed feasibility and accuracy for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection.