Clinical characteristics of patients with confirmed and asymptomatic SARS-CoV-2 infection in China.

OBJECTIVE: To examine the clinical characteristics of patients with asymptomatic novel coronavirus disease 2019 (COVID-19) and compare them with those of patients with mild disease. DESIGN: A retrospective cohort study. SETTING: Multiple medical centers in Wuhan, Hubei, China. PARTICIPANTS: A total of 3,263 patients with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infection between February 4, 2020, and April 15, 2020. MAIN OUTCOME MEASURES: Patient demographic characteristics, medical history, vital signs, and laboratory and chest computed tomography (CT) findings. RESULTS: A total of 3,173 and 90 patients with mild and moderate, and asymptomatic COVID-19, respectively, were included. A total of 575 (18.2%) symptomatic patients and 4 (4.4%) asymptomatic patients developed the severe illness. All asymptomatic patients recovered; no deaths were observed in this group. The median duration of viral shedding in asymptomatic patients was 17 (interquartile range, 9.25-25) days. Patients with higher levels of ultrasensitive C-reactive protein (odds ratio [OR] = 1.025, 95% confidence interval [CI], 1.01-1.04), lower red blood cell volume distribution width (OR = 0.68, 95% CI 0.51-0.88), lower creatine kinase Isoenzyme(0.94, 0.89-0.98) levels, or lower lesion ratio (OR = 0.01, 95% CI 0.00-0.33) at admission were more likely than their counterparts to have asymptomatic disease. CONCLUSIONS: Patients with younger ages and fewer comorbidities are more likely to be asymptomatic. Asymptomatic patients had similar laboratory characteristics and longer virus shedding time than symptomatic patients; screen and isolation during their infection were helpful to reduce the risk of SARS-CoV-2 transmission.

Elevated Fasting Blood Glucose Levels Are Associated with Worse Clinical Outcomes in COVID-19 Patients Than in Pneumonia Patients with Bacterial Infections.

AIMS: We investigate how fasting blood glucose (FBG) levels affect the clinical severity in coronavirus disease 2019 (COVID-19) patients, pneumonia patients with sole bacterial infection, and pneumonia patients with concurrent bacterial and fungal infections. METHODS: We enrolled 2761 COVID-19 patients, 1686 pneumonia patients with bacterial infections, and 2035 pneumonia patients with concurrent infections. We used multivariate logistic regression analysis to assess the associations between FBG levels and clinical severity. RESULTS: FBG levels in COVID-19 patients were significantly higher than in other pneumonia patients during hospitalisation and at discharge (all p < 0.05). Among COVID-19 patients, the odds ratios of acute respiratory distress syndrome (ARDS), respiratory failure (RF), acute hepatitis/liver failure (AH/LF), length of stay, and intensive care unit (ICU) admission were 12.80 (95% CI, 4.80-37.96), 5.72 (2.95-11.06), 2.60 (1.20-5.32), 1.42 (1.26-1.59), and 5.16 (3.26-8.17) times higher in the FBG ≥7.0 mmol/L group than in FBG < 6.1 mmol/L group, respectively. The odds ratios of RF, AH/LF, length of stay, and ICU admission were increased to a lesser extent in pneumonia patients with sole bacterial infection (3.70 [2.21-6.29]; 1.56 [1.17-2.07]; 0.98 [0.88-1.11]; 2.06 [1.26-3.36], respectively). The odds ratios of ARDS, RF, AH/LF, length of stay, and ICU admission were increased to a lesser extent in pneumonia patients with concurrent infections (3.04 [0.36-6.41]; 2.31 [1.76-3.05]; 1.21 [0.97-1.52]; 1.02 [0.93-1.13]; 1.72 [1.19-2.50], respectively). Among COVID-19 patients, the incidence rate of ICU admission on day 21 in the FBG ≥ 7.0 mmol/L group was six times higher than in the FBG < 6.1 mmol/L group (12.30% vs. 2.21%, p < 0.001). Among other pneumonia patients, the incidence rate of ICU admission on day 21 was only two times higher. CONCLUSIONS: Elevated FBG levels at admission predict subsequent clinical severity in all pneumonia patients regardless of the underlying pathogens, but COVID-19 patients are more sensitive to FBG levels, and suffer more severe clinical complications than other pneumonia patients.

High Fasting Blood Glucose Level With Unknown Prior History of Diabetes Is Associated With High Risk of Severe Adverse COVID-19 Outcome.

Background: We aimed to understand how glycaemic levels among COVID-19 patients impact their disease progression and clinical complications. Methods: We enrolled 2,366 COVID-19 patients from Huoshenshan hospital in Wuhan. We stratified the COVID-19 patients into four subgroups by current fasting blood glucose (FBG) levels and their awareness of prior diabetic status, including patients with FBG<6.1mmol/L with no history of diabetes (group 1), patients with FBG<6.1mmol/L with a history of diabetes diagnosed (group 2), patients with FBG≥6.1mmol/L with no history of diabetes (group 3) and patients with FBG≥6.1mmol/L with a history of diabetes diagnosed (group 4). A multivariate cause-specific Cox proportional hazard model was used to assess the associations between FBG levels or prior diabetic status and clinical adversities in COVID-19 patients. Results: COVID-19 patients with higher FBG and unknown diabetes in the past (group 3) are more likely to progress to the severe or critical stage than patients in other groups (severe: 38.46% vs 23.46%-30.70%; critical 7.69% vs 0.61%-3.96%). These patients also have the highest abnormal level of inflammatory parameters, complications, and clinical adversities among all four groups (all p<0.05). On day 21 of hospitalisation, group 3 had a significantly higher risk of ICU admission [14.1% (9.6%-18.6%)] than group 4 [7.0% (3.7%-10.3%)], group 2 [4.0% (0.2%-7.8%)] and group 1 [2.1% (1.4%-2.8%)], (P<0.001). Compared with group 1 who had low FBG, group 3 demonstrated 5 times higher risk of ICU admission events during hospitalisation (HR=5.38, 3.46-8.35, P<0.001), while group 4, where the patients had high FBG and prior diabetes diagnosed, also showed a significantly higher risk (HR=1.99, 1.12-3.52, P=0.019), but to a much lesser extent than in group 3. Conclusion: Our study shows that COVID-19 patients with current high FBG levels but unaware of pre-existing diabetes, or possibly new onset diabetes as a result of COVID-19 infection, have a higher risk of more severe adverse outcomes than those aware of prior diagnosis of diabetes and those with low current FBG levels.

An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19.

A high-performing interpretable model is proposed to predict the risk of deterioration in coronavirus disease 2019 (COVID-19) patients. The model was developed using a cohort of 3028 patients diagnosed with COVID-19 and exhibiting common clinical symptoms that were internally verified (AUC 0.8517, 95% CI 0.8433, 0.8601). A total of 15 high risk factors for deterioration and their approximate warning ranges were identified. This included prothrombin time (PT), prothrombin activity, lactate dehydrogenase, international normalized ratio, heart rate, body-mass index (BMI), D-dimer, creatine kinase, hematocrit, urine specific gravity, magnesium, globulin, activated partial thromboplastin time, lymphocyte count (L%), and platelet count. Four of these indicators (PT, heart rate, BMI, HCT) and comorbidities were selected for a streamlined combination of indicators to produce faster results. The resulting model showed good predictive performance (AUC 0.7941 95% CI 0.7926, 0.8151). A website for quick pre-screening online was also developed as part of the study.

The active lung microbiota landscape of COVID-19 patients through the metatranscriptome data analysis.

Introduction: With the outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the interaction between the host and SARS-CoV-2 was widely studied. However, it is unclear whether and how SARS-CoV-2 infection affects lung microflora, which contribute to COVID-19 complications. Methods: Here, we analyzed the metatranscriptomic data of bronchoalveolar lavage fluid (BALF) of 19 COVID-19 patients and 23 healthy controls from 6 independent projects and detailed the active microbiota landscape in both healthy individuals and COVID-19 patients. Results: The infection of SARS-CoV-2 could deeply change the lung microbiota, evidenced by the α-diversity, ß-diversity, and species composition analysis based on bacterial microbiota and virome. Pathogens (e.g., Klebsiella oxytoca causing pneumonia as well), immunomodulatory probiotics (e.g., lactic acid bacteria and Faecalibacterium prausnitzii, a butyrate producer), and Tobacco mosaic virus (TMV) were enriched in the COVID-19 group, suggesting a severe microbiota dysbiosis. The significant correlation between Rothia mucilaginosa, TMV, and SARS-CoV-2 revealed drastic inflammatory battles between the host, SARS-CoV-2, and other microbes in the lungs. Notably, TMV only existed in the COVID-19 group, while human respirovirus 3 (HRV 3) only existed in the healthy group. Our study provides insights into the active microbiota in the lungs of COVID-19 patients and would contribute to the understanding of the infection mechanism of SARS-CoV-2 and the treatment of the disease and complications. Conclusion: SARS-COV-2 infection deeply altered the lung microbiota of COVID-19 patients. The enrichment of several other pathogens, immunomodulatory probiotics (lactic acid or butyrate producers), and TMV in the COVID-19 group suggests a complex and active lung microbiota disorder.

DeepKG: An End-to-End Deep Learning-Based Workflow for Biomedical Knowledge Graph Extraction, Optimization and Applications.

SUMMARY: DeepKG is an end-to-end deep learning-based workflow that helps researchers automatically mine valuable knowledge in biomedical literature. Users can utilize it to establish customized knowledge graphs in specified domains, thus facilitating in-depth understanding on disease mechanisms and applications on drug repurposing and clinical research, etc. To improve the performance of DeepKG, a cascaded hybrid information extraction framework (CHIEF) is developed for training model of 3-tuple extraction, and a novel AutoML-based knowledge representation algorithm (AutoTransX) is proposed for knowledge representation and inference. The system has been deployed in dozens of hospitals and extensive experiments strongly evidence the effectiveness. In the context of 144,900 COVID-19 scholarly full-text literature, DeepKG generates a high-quality knowledge graph with 7,980 entities and 43,760 3-tuples, a candidate drug list, and relevant animal experimental studies are being carried out. To accelerate more studies, we make DeepKG publicly available and provide an online tool including the data of 3-tuples, potential drug list, question answering system, visualization platform. AVAILABILITY: Free to all users: http://covidkg.ai/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrated medical resource consumption stratification in hospitalized patients: an Auto Triage Management model based on accurate risk, cost and length of stay prediction.

Triage management plays important roles in hospitalized patients for disease severity stratification and medical burden analysis. Although progression risks have been extensively researched for numbers of diseases, other crucial indicators that reflect patients' economic and time costs have not been systematically studied. To address the problems, we developed an automatic deep learning based Auto Triage Management (ATM) Framework capable of accurately modelling patients' disease progression risk and health economic evaluation. Based on them, we can first discover the relationship between disease progression and medical system cost, find potential features that can more precisely aid patient triage in resource allocation, and allow treatment plan searching that has cured patients. Applying ATM in COVID-19, we built a joint model to predict patients' risk, the total length of stay (LoS) and cost when at-admission, and remaining LoS and cost at a given hospitalized time point, with C-index 0.930 and 0.869 for risk prediction, mean absolute error (MAE) of 5.61 and 5.90 days for total LoS prediction in internal and external validation data.

Effects of angiotensin receptor blockers and angiotensin-converting enzyme inhibitors on COVID-19.

BACKGROUND: The World Health Organization reported that 28637952 people worldwide had been infected with severe acute respiratory syndrome coronavirus 2, the causative agent of coronavirus disease 2019 (COVID-19), by September 13. AIM: The aim was to investigate whether long-term use of renin-angiotensin-aldosterone system (RAAS) inhibitors for the treatment of hypertension aggravates the performance of COVID-19 patients with hypertension. METHODS: This was a retrospective analysis of lung computed tomography (CT) data and laboratory values of COVID-19 patients with hypertension who were admitted to Huoshenshan Hospital, Wuhan, Hubei Province, between February 18 and March 31, 2020. Patients were divided into two groups. Group A included 19 people who were long-term users of RAAS inhibitors for hypertension; and group B included 28 people who were randomly selected from the database and matched with group A by age, sex, basic diseases, and long-term use of other antihypertensive drugs. All patients underwent a series of CT and laboratory tests. We compared the most severe CT images of the two groups and the laboratory examination results within 2 d of the corresponding CT images. RESULTS: The time until the most severe CT images from the onset of COVID-19 was 30.37 ± 14.25 d group A and 26.50 ± 11.97 d in group B. The difference between the two groups was not significant (t = 1.01, P = 0.32). There were no significant differences in blood laboratory values, C-reactive protein, markers of cardiac injury, liver function, or kidney function between the two groups. There was no significant difference in the appearance of the CT images between the two groups. The semiquantitative scores of each involved lobe were 11.84 ± 5.88 in group A and 10.36 ± 6.04 group B. The difference was not significantly different (t = 0.84, P = 0.41). CONCLUSION: Chest CT is an important imaging tool to monitor the characteristics of COVID-19 and the degree of lung injury. Chronic use of RAAS inhibitors is not related to the severity of COVID-19, and it does not worsen the clinical process.

Clinical Significance of an IgM and IgG Test for Diagnosis of Highly Suspected COVID-19.

Background: Nucleic acid detection and CT scanning have been reported in COVID-19 diagnosis. Here, we aimed to investigate the clinical significance of IgM and IgG testing for the diagnosis of highly suspected COVID-19. Methods: A total of 63 patients with suspected COVID-19 were observed, 57 of whom were enrolled (24 males and 33 females). The selection was based on the diagnosis and treatment protocol for COVID-19 (trial Sixth Edition) released by the National Health Commission of the People's Republic of China. Patients were divided into positive and negative groups according to the first nucleic acid results from pharyngeal swab tests. Routine blood tests were detected on the second day after each patient was hospitalized. The remaining serum samples were used for detection of novel coronavirus-specific IgM/IgG antibodies. Results: The rate of COVID-19 nucleic acid positivity was 42.10%. The positive detection rates with a combination of IgM and IgG testing for patients with COVID-19 negative and positive nucleic acid test results were 72.73 and 87.50%, respectively. Conclusions: We report a rapid, simple, and accurate detection method for patients with suspected COVID-19 and for on-site screening for close contacts within the population. IgM and IgG antibody detection can identify COVID-19 after a negative nucleic acid test. Diagnostic accuracy of COVID-19 might be improved by nucleic acid testing in patients with a history of epidemic disease or with clinical symptoms, as well as CT scans when necessary, and serum-specific IgM and IgG antibody testing after the window period.

Rapid Acquisition of High-Quality SARS-CoV-2 Genome via Amplicon-Oxford Nanopore Sequencing.

Genome sequencing has shown strong capabilities in the initial stages of the COVID-19 pandemic such as pathogen identification and virus preliminary tracing. While the rapid acquisition of SARS-CoV-2 genome from clinical specimens is limited by their low nucleic acid load and the complexity of the nucleic acid background. To address this issue, we modified and evaluated an approach by utilizing SARS-CoV-2-specific amplicon amplification and Oxford Nanopore PromethION platform. This workflow started with the throat swab of the COVID-19 patient, combined reverse transcript PCR, and multi-amplification in one-step to shorten the experiment time, then can quickly and steadily obtain high-quality SARS-CoV-2 genome within 24 h. A comprehensive evaluation of the method was conducted in 42 samples: the sequencing quality of the method was correlated well with the viral load of the samples; high-quality SARS-CoV-2 genome could be obtained stably in the samples with Ct value up to 39.14; data yielding for different Ct values were assessed and the recommended sequencing time was 8 h for samples with Ct value of less than 20; variation analysis indicated that the method can detect the existing and emerging genomic mutations as well; Illumina sequencing verified that ultra-deep sequencing can greatly improve the single read error rate of Nanopore sequencing, making it as low as 0.4/10,000 bp. In summary, high-quality SARS-CoV-2 genome can be acquired by utilizing the amplicon amplification and it is an effective method in accelerating the acquisition of genetic resources and tracking the genome diversity of SARS-CoV-2.

Elevated glucose level leads to rapid COVID-19 progression and high fatality.

OBJECTIVES: We aimed to identify high-risk factors for disease progression and fatality for coronavirus disease 2019 (COVID-19) patients. METHODS: We enrolled 2433 COVID-19 patients and used LASSO regression and multivariable cause-specific Cox proportional hazard models to identify the risk factors for disease progression and fatality. RESULTS: The median time for progression from mild-to-moderate, moderate-to-severe, severe-to-critical, and critical-to-death were 3.0 (interquartile range: 1.8-5.5), 3.0 (1.0-7.0), 3.0 (1.0-8.0), and 6.5 (4.0-16.3) days, respectively. Among 1,758 mild or moderate patients at admission, 474 (27.0%) progressed to a severe or critical stage. Age above 60 years, elevated levels of blood glucose, respiratory rate, fever, chest tightness, c-reaction protein, lactate dehydrogenase, direct bilirubin, and low albumin and lymphocyte count were significant risk factors for progression. Of 675 severe or critical patients at admission, 41 (6.1%) died. Age above 74 years, elevated levels of blood glucose, fibrinogen and creatine kinase-MB, and low plateleta count were significant risk factors for fatality. Patients with elevated blood glucose level were 58% more likely to progress and 3.22 times more likely to die of COVID-19. CONCLUSIONS: Older age, elevated glucose level, and clinical indicators related to systemic inflammatory responses and multiple organ failures, predict both the disease progression and the fatality of COVID-19 patients.

Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

Independent and combined effects of hypertension and diabetes on clinical outcomes in patients with COVID-19: A retrospective cohort study of Huoshen Mountain Hospital and Guanggu Fangcang Shelter Hospital.

It is widely recognized that hypertension is one of the major risk factor for disease severity and mortality in patients with coronavirus disease 2019 (COVID-19). However, type 2 diabetes mellitus (T2DM) and hypertension are frequent comorbid conditions, complicating the assessment of hypertension's individual contribution to the risk. The aims of this study were to evaluate the contributions of hypertension alone, T2DM alone, or their combination to the risk of death, acute respiratory distress syndrome (ARDS)/respiratory failure, and severe COVID-19 infection. Additionally, we assessed risks associated with elevated blood pressure and fasting blood glucose on the same three clinical outcomes. Multivariate logistic models were used for these analyses. Among the 3400 patients, 3327(97.9%) survived and 73(2.1%) died. Compared to patients having neither hypertension nor T2DM (n = 1392), the risk of mortality was significantly higher in patients with T2DM alone (n = 226, OR 5.26 [95% CI: 2.39-11.58]) or with T2DM in combination with hypertension (n = 507, OR 3.02, [95% CI: 1.48-6.15]). Similarly, T2DM was a risk factor for development of ARDS/respiratory failure and severe infection. Hypertension alone (n = 1275) only conferred additional risk for the development of severe infection (OR 1.22 [95% CI: 1.00-1.51]). In conclusion, neither hypertension nor elevated blood pressure was independent risk factors for death or ARDS/respiratory failure but hypertension marginally increased the risk of severe COVID-19 infection. The risk associated with hypertension is accentuated through its confounding effect on T2DM.

Foundation and Clinical Evaluation of a New Method for Detecting SARS-CoV-2 Antigen by Fluorescent Microsphere Immunochromatography.

Purpose: To develop a rapid detection reagent for SARS-CoV-2 antigen for the auxiliary diagnosis of new coronary pneumonia (COVID-19), and perform the methodological evaluation and clinical evaluation of the reagent. Method: SARS-CoV-2 N-protein test strip was created by combining fluorescent microsphere labeling technology and immunochromatographic technology, based on the principle of double antibody sandwich. Then we evaluated the analytical capability and clinical application of the strips. Result: The limit of detection of the strips for recombinant N protein was 100 ng/ml and for activated SARS -CoV-2 virus was 1 × 103 TCID50/ml. The strips also have high analytical specificity and anti-interference capability. According to the predetermined cut-off value, the specificity of the test strip in healthy controls and patients with other respiratory disease was 100.00 and 97.29%, the sensitivity in COVID-19 cases at progress stage and cured stage was 67.15 and 7.02%. The positive percentage agreement and negative percentage agreement of antigen strip to RNA test were 83.16 and 94.45%. Conclusion: SARS-CoV-2 fluorescence immunochromatographic test strip can achieve fast, sensitive and accurate detection, which can meet the clinical requirements for rapid detection of viruses on the spot.

Establishing a high sensitivity detection method for SARS-CoV-2 IgM/IgG and developing a clinical application of this method.

COVID-19 is caused by SARS-CoV-2 infection and was initially discovered in Wuhan. This outbreak quickly spread all over China and then to more than 20 other countries. SARS-CoV-2 fluorescent microsphere immunochromatographic test strips were prepared by the combination of time-resolved fluorescence immunoassay with a lateral flow assay. The analytical performance and clinical evaluation of this testing method was done and the clinical significance of the testing method was verified. The LLOD of SARS-CoV-2 antibody IgG and IgM was 0.121U/L and 0.366U/L. The specificity of IgM and IgG strips in healthy people and in patients with non-COVID-19 disease was 94%, 96.72% and 95.50%, 99.49%, respectively; and sensitivity of IgM and IgG strips for patients during treatment and follow-up was 63.02%, 37.61% and 87.28%, 90.17%, respectively. The SARS-CoV-2 antibody test strip can provide rapid, flexible and accurate testing, and is able to meet the clinical requirement for rapid on-site testing of virus. The ability to detect IgM and IgG provided a significant benefit for the detection and prediction of clinical course with COVID-19 patients.

Transcriptome-based drug repositioning for coronavirus disease 2019 (COVID-19).

The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) around the world has led to a pandemic with high morbidity and mortality. However, there are no effective drugs to prevent and treat the disease. Transcriptome-based drug repositioning, identifying new indications for old drugs, is a powerful tool for drug development. Using bronchoalveolar lavage fluid transcriptome data of COVID-19 patients, we found that the endocytosis and lysosome pathways are highly involved in the disease and that the regulation of genes involved in neutrophil degranulation was disrupted, suggesting an intense battle between SARS-CoV-2 and humans. Furthermore, we implemented a coexpression drug repositioning analysis, cogena, and identified two antiviral drugs (saquinavir and ribavirin) and several other candidate drugs (such as dinoprost, dipivefrine, dexamethasone and (-)-isoprenaline). Notably, the two antiviral drugs have also previously been identified using molecular docking methods, and ribavirin is a recommended drug in the diagnosis and treatment protocol for COVID pneumonia (trial version 5-7) published by the National Health Commission of the P.R. of China. Our study demonstrates the value of the cogena-based drug repositioning method for emerging infectious diseases, improves our understanding of SARS-CoV-2-induced disease, and provides potential drugs for the prevention and treatment of COVID-19 pneumonia.