Corticosteroid treatment in severe patients with SARS-CoV-2 and chronic HBV co-infection: a retrospective multicenter study.

BACKGROUND: The impact of corticosteroids on patients with severe coronavirus disease 2019 (COVID-19)/chronic hepatitis B virus (HBV) co-infection is currently unknown. We aimed to investigate the association of corticosteroids on these patients. METHODS: This retrospective multicenter study screened 5447 confirmed COVID-19 patients hospitalized between Jan 1, 2020 to Apr 18, 2020 in seven centers in China, where the prevalence of chronic HBV infection is moderate to high. Severe patients who had chronic HBV and acute SARS-cov-2 infection were potentially eligible. The diagnosis of chronic HBV infection was based on positive testing for hepatitis B surface antigen (HBsAg) or HBV DNA during hospitalization and a medical history of chronic HBV infection. Severe patients (meeting one of following criteria: respiratory rate > 30 breaths/min; severe respiratory distress; or SpO2 ≤ 93% on room air; or oxygen index < 300 mmHg) with COVID-19/HBV co-infection were identified. The bias of confounding variables on corticosteroids effects was minimized using multivariable logistic regression model and inverse probability of treatment weighting (IPTW) based on propensity score. RESULTS: The prevalence of HBV co-infection in COVID-19 patients was 4.1%. There were 105 patients with severe COVID-19/HBV co-infections (median age 62 years, 57.1% male). Fifty-five patients received corticosteroid treatment and 50 patients did not. In the multivariable analysis, corticosteroid therapy (OR, 6.32, 95% CI 1.17-34.24, P = 0.033) was identified as an independent risk factor for 28-day mortality. With IPTW analysis, corticosteroid treatment was associated with delayed SARS-CoV-2 viral RNA clearance (OR, 2.95, 95% CI 1.63-5.32, P < 0.001), increased risk of 28-day and in-hospital mortality (OR, 4.90, 95% CI 1.68-14.28, P = 0.004; OR, 5.64, 95% CI 1.95-16.30, P = 0.001, respectively), and acute liver injury (OR, 4.50, 95% CI 2.57-7.85, P < 0.001). Methylprednisolone dose per day and cumulative dose in non-survivors were significantly higher than in survivors. CONCLUSIONS: In patients with severe COVID-19/HBV co-infection, corticosteroid treatment may be associated with increased risk of 28-day and in-hospital mortality.

Clinical characteristics of novel coronavirus pneumonia in children in Jinan

Objective: To enhance the understanding of novel coronavirus pneumonia (NCP) in children, to provide reference for the early diagnosis and treatment and to prevent misdiagnosis.

Epidemiological analysis of 26 COVID-19 cases at a designated hospital in Jinan city

Objective: To describe and analyze the epidemiological characteristics of patients with coronavirus disease 2019(COVID-19) treated at a designated hospital in Jinan from 0:00, Jan. 23 to 12:00, Feb. 5, 2020.

An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.

OBJECTIVES: Artificial intelligence (AI) has been proved to be a highly efficient tool for COVID-19 diagnosis, but the large data size and heavy label force required for algorithm development and the poor generalizability of AI algorithms, to some extent, limit the application of AI technology in clinical practice. The aim of this study is to develop an AI algorithm with high robustness using limited chest CT data for COVID-19 discrimination. METHODS: A three dimensional algorithm that combined multi-instance learning with the LSTM architecture (3DMTM) was developed for differentiating COVID-19 from community acquired pneumonia (CAP) while logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), and a three dimensional convolutional neural network set for comparison. Totally, 515 patients with or without COVID-19 between December 2019 and March 2020 from five different hospitals were recruited and divided into relatively large (150 COVID-19 and 183 CAP cases) and relatively small datasets (17 COVID-19 and 35 CAP cases) for either training or validation and another independent dataset (37 COVID-19 and 93 CAP cases) for external test. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, accuracy, F1 score, and G-mean were utilized for performance evaluation. RESULTS: In the external test cohort, the relatively large data-based 3DMTM-LD achieved an AUC of 0.956 (95% confidence interval, 95% CI, 0.929∼0.982) with 86.2% and 98.0% for its sensitivity and specificity. 3DMTM-SD got an AUC of 0.937 (95% CI, 0.909∼0.965), while the AUC of 3DCM-SD decreased dramatically to 0.714 (95% CI, 0.649∼0.780) with training data reduction. KNN-MMSD, LR-MMSD, SVM-MMSD, and 3DCM-MMSD benefited significantly from the inclusion of clinical information while models trained with relatively large dataset got slight performance improvement in COVID-19 discrimination. 3DMTM, trained with either CT or multi-modal data, presented comparably excellent performance in COVID-19 discrimination. CONCLUSIONS: The 3DMTM algorithm presented excellent robustness for COVID-19 discrimination with limited CT data. 3DMTM based on CT data performed comparably in COVID-19 discrimination with that trained with multi-modal information. Clinical information could improve the performance of KNN, LR, SVM, and 3DCM in COVID-19 discrimination, especially in the scenario with limited data for training.

Sex Differences on Clinical Characteristics, Severity, and Mortality in Adult Patients With COVID-19: A Multicentre Retrospective Study.

Background: Coronavirus disease-2019 (COVID-19) epidemic is spreading globally. Sex differences in the severity and mortality of COVID-19 emerged. This study aims to describe the impact of sex on outcomes in COVOD-19 with a special focus on the effect of estrogen. Methods: We performed a retrospective cohort study which included 413 patients (230 males and 183 females) with COVID-19 from three designated hospitals in China with a follow up time from January 31, 2020, to April 17, 2020. Women over 55 were considered as postmenopausal patients according to the previous epidemiological data from China. The interaction between age and sex on in-hospital mortality was determined through Cox regression analysis. In addition, multivariate Cox regression models were performed to explore risk factors associated with in-hospital mortality of COVID-19. Results: Age and sex had significant interaction for the in-hospital mortality (P < 0.001). Multivariate Cox regression showed that age (HR 1.041, 95% CI 1.009-1.073, P = 0.012), male sex (HR 2.033, 95% CI 1.007-2.098, P = 0.010), the interaction between age and sex (HR 1.118, 95% CI 1.003-1.232, P = 0.018), and comorbidities (HR 9.845, 95% CI 2.280-42.520, P = 0.002) were independently associated with in-hospital mortality of COVID-19 patients. In this multicentre study, female experienced a lower fatality for COVID-19 than male (4.4 vs. 10.0%, P = 0.031). Interestingly, stratification by age group revealed no difference in-hospital mortality was noted in women under 55 compared with women over 55 (3.8 vs. 5.2%, P = 0.144), as well as in women under 55 compared with the same age men (3.8 vs. 4.0%, P = 0.918). However, there was significantly difference in women over 55 with men of the same age group (5.2 vs. 21.0%, P = 0.007). Compared with male patients, female patients had higher lymphocyte (P < 0.001) and high-density lipoprotein (P < 0.001), lower high sensitive c reaction protein level (P < 0.001), and lower incidence rate of acute cardiac injury (6.6 vs. 13.5%, P = 0.022). Conclusion: Male sex is an independent risk factor for COVID-19 in-hospital mortality. Although female mortality in COVID-19 is lower than male, it might not be directly related to the effect of estrogen. Further study is warranted to identify the sex difference in COVID-19 and mechanisms involved.

A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography.

Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856-0.988) and 0.959 (95% CI 0.910-1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases.

Risk factors for severe COVID-19 in middle-aged patients without comorbidities: a multicentre retrospective study.

BACKGROUND: Information regarding characteristics and risk factors of COVID-19 amongst middle-aged (40-59 years) patients without comorbidities is scarce. METHODS: We therefore conducted this multicentre retrospective study and collected data of middle-aged COVID-19 patients without comorbidities at admission from three designated hospitals in China. RESULTS: Among 119 middle-aged patients without comorbidities, 18 (15.1%) developed into severe illness and 5 (3.9%) died in hospital. ARDS (26, 21.8%) and elevated D-dimer (36, 31.3%) were the most common complications, while other organ complications were relatively rare. Multivariable regression showed increasing odds of severe illness associated with neutrophil to lymphocyte ratio (NLR, OR, 11.238; 95% CI 1.110-1.382; p < 0.001) and D-dimer greater than 1 µg/ml (OR, 16.079; 95% CI 3.162-81.775; p = 0.001) on admission. The AUCs for the NLR, D-dimer greater than 1 µg/ml and combined NLR and D-dimer index were 0.862 (95% CI, 0.751-0.973), 0.800 (95% CI 0.684-0.915) and 0.916 (95% CI, 0.855-0.977), respectively. SOFA yielded an AUC of 0.750 (95% CI 0.602-0.987). There was significant difference in the AUC between SOFA and combined index (z = 2.574, p = 0.010). CONCLUSIONS: More attention should be paid to the monitoring and early treatment of respiratory and coagulation abnormalities in middle-aged COVID-19 patients without comorbidities. In addition, the combined NLR and D-dimer higher than 1 µg/ml index might be a potential and reliable predictor for the incidence of severe illness in this specific patient with COVID-19, which could guide clinicians on early classification and management of patients, thereby relieving the shortage of medical resource. However, it is warranted to validate the reliability of the predictor in larger sample COVID-19 patients.

Characteristics of asymptomatic patients with SARS-CoV-2 infection in Jinan, China.

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) is continuously and rapidly circulating at present. Asymptomatic patients have been proven to be contagious and thus pose a significant infection control challenge. Here we describe the characteristics of asymptomatic patients with SARS-CoV-2 infection in Jinan, Shandong province, China. A total of 47 patients with confirmed COVID-19 were recruited. Among them, 11 patients were categorized as asymptomatic cases. We found that the asymptomatic patients in Jinan were relatively young and were mainly clustered cases. The laboratory indicators and lung lesion on chest CT were mild. No special factors were found accounting for the presence or absence of symptoms. The presence of asymptomatic patients increased the difficulty of screening. It is necessary to strengthen the identification of such patients in the future.

Epidemic update of COVID-19 in Hubei Province compared with other regions in China.

AIMS & BACKGROUND: The COVID-19 outbreak spread in China and is a threat to the world. The aims of this study to help health workers better understand the epidemic of the COVID-19 and provide different control strategies toward Hubei Province and other regions in China. METHODS: A comprehensive search of the Chinese Center for Disease Control and Prevention official websites and announcements was performed between 20 Jan 2019 and 29 Feb 2020. The relevant data of the distribution of the infection on each reported day were obtained. RESULTS& FINDINGS: Up to 29 Feb 2020, 79,824 confirmed cases with the COVID-19 including 66,907 in Hubei Province and 12,377 in other administrative regions were reported. The SARS-COV-2 showed faster epidemic trends compared with the 2003-SARS-CoV. A total of 2,870 deaths have been reported nationwide among 79,824 confirmed cases, with a mortality of 3.6%. The mortality of the COVID-19 was significantly higher in Hubei Province than that in other regions(4.1% versus 0.84%). Since 1 Feb 2020 the number of discharged cases exceeded the number of the dead. By 29 Feb 2020, the number of discharged patients was 41,625, which exceeded the number of hospitalized patients, and the trend has further increased. CONCLUSIONS: The infection of the SARS-COV-2 is spreading and increasing nationwide, and Hubei Province is the main epidemic area, with higher mortality. The outbreak is now under initial control especially in other regions outside of Hubei Province. Due to the different epidemic characteristics between Hubei Province and other regions, we should focus on different prevention and control strategies.

Clinical characteristics of COVID-19 in children compared with adults in Shandong Province, China.

AIMS AND BACKGROUND: The COVID-19 outbreak spread in China and is a threat to the world. We reported on the epidemiological, clinical, laboratory, and radiological characteristics of children cases to help health workers better understand and provide timely diagnosis and treatment. METHODS: Retrospectively, two research centers' case series of 67 consecutive hospitalized cases including 53 adult and 14 children cases with COVID-19 between 23 Jan 2020 and 15 Feb 2020 from Jinan and Rizhao were enrolled in this study. Epidemiological, clinical, laboratory, and radiological characteristics of children and adults were analyzed and compared. RESULTS: Most cases in children were mild (21.4%) and conventional cases (78.6%), with mild clinical signs and symptoms, and all cases were of family clusters. Fever (35.7%) and dry cough (21.4%) were described as clinical manifestations in children cases. Dry cough and phlegm were not the most common symptoms in children compared with adults (p = 0.03). In the early stages of the disease, lymphocyte counts did not significantly decline but neutrophils count did in children compared with adults (p = 0.02). There was a lower level of CRP (p = 0.00) in children compared with adults. There were 8 (57.1%) asymptomatic cases and 6 (42.9%) symptomatic cases among the 14 children cases. The age of asymptomatic patients was younger than that of symptomatic patients (p = 0.03). Even among asymptomatic patients, 5 (62.5%) cases had lung injuries including 3 (60%) cases with bilateral involvement, which was not different compared with that of symptomatic cases (p = 0.58, p = 0.74). CONCLUSIONS: The clinical symptoms of children are mild, there is substantial lung injury even among children, but that there is less clinical disease, perhaps because of a less pronounced inflammatory response, and that the occurrence of this pattern appears to inversely correlate with age.

The different clinical characteristics of corona virus disease cases between children and their families in China - the character of children with COVID-19.

This study aims to analyze the different clinical characteristics between children and their families infected with severe acute respiratory syndrome coronavirus 2. Clinical data from nine children and their 14 families were collected, including general status, clinical, laboratory test, and imaging characteristics. All the children were detected positive result after their families onset. Three children had fever (22.2%) or cough (11.2%) symptoms and six (66.7%) children had no symptom. Among the 14 adult patients, the major symptoms included fever (57.1%), cough (35.7%), chest tightness/pain (21.4%), fatigue (21.4%) and sore throat (7.1%). Nearly 70% of the patients had normal (71.4%) or decreased (28.6%) white blood cell counts, and 50% (7/14) had lymphocytopenia. There were 10 adults (71.4%) showed abnormal imaging. The main manifestations were pulmonary consolidation (70%), nodular shadow (50%), and ground glass opacity (50%). Five discharged children were admitted again because their stool showed positive result in SARS-CoV-2 PCR. COVID-19 in children is mainly caused by family transmission, and their symptoms are mild and prognosis is better than adult. However, their PCR result in stool showed longer time than their families. Because of the mild or asymptomatic clinical process, it is difficult to recognize early for pediatrician and public health staff.

Do children need a longer time to shed SARS-CoV-2 in stool than adults?

SARS-CoV-2 can be shed in the stool of patients in the recovery phase. Children show a longer shedding time than adults. We analyzed the possible causes of this finding and recommend that a negative stool sample be included in a patient's discharge criteria.