Background: Since March 2020, Ireland has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To date, while several cohorts from China have been described, our understanding is limited, with no data describing the epidemiological and clinical characteristics of patients with COVID-19 in Ireland. To improve our understanding of the clinical characteristics of this emerging infection we carried out a retrospective review of patient data to examine the clinical characteristics of patients admitted for COVID-19 hospital treatment. Methods: Demographic, clinical and laboratory data on the rst 100 adult patients admitted to Mater Misericordiae University Hospital (MMUH) for in-patient COVID-19 treatment after onset of the outbreak in March 2020 was extracted from clinical and administrative records. Missing data were excluded from the analysis. Results: Fifty-eight per cent were male, 63% were Irish nationals, 29% were GMS eligible, and median age was 45 years (interquartile range [IQR] =34-64 years). Patients had symptoms for a median of ve days before diagnosis (IQR=2.5-7 days), most commonly cough (72%), fever (65%), dyspnoea (37%), fatigue (28%), myalgia (27%) and headache (24%). Of all cases, 54 had at least one pre-existing chronic illness (most commonly hypertension, diabetes mellitus or asthma). At initial assessment, the most common abnormal ndings were: C-reactive protein >7.0mg/L (74%), ferritin >247μg/L (women) or >275μg/L (men) (62%), D-dimer >0.5μg/dL (62%), chest imaging (59%), NEWS Score (modi ed) of ≥3 (55%) and heart rate >90/min (51%). Twenty-seven required supplemental oxygen, of which 17 were admitted to the intensive care unit-14 requiring ventilation. Forty received antiviral treatment (most commonly hydroxychloroquine or lopinavir/ritonavir). Four died, 17 were admitted to intensive care, and 74 were discharged home, with nine days the median hospital stay (IQR=6-11). Conclusion: Our ndings reinforce the emerging consensus of COVID-19 as an acute life-threatening disease and highlights, the importance of laboratory (ferritin, C-reactive protein, D-dimer) and radiological parameters, in addition to clinical parameters. Further cohort studies involving larger samples followed longitudinally are a priority.

The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations in a surging pandemic require difficult resource allocation decisions. The objectives of this research are: (1) to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes, and (2) to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation. The predictive models learn from historical data to help predict who will develop acute respiratory distress syndrome (ARDS), a severe outcome in COVID-19. Our results, based on data from two hospitals in Wenzhou, Zhejiang, China, identified features on initial presentation with COVID-19 that were most predictive of later development of ARDS. A mildly elevated alanine aminotransferase (ALT) (a liver enzyme), the presence of myalgias (body aches), and an elevated hemoglobin (red blood cells), in this order, are the clinical features, on presentation, that are the most predictive. The predictive models that learned from historical data of patients from these two hospitals achieved 70% to 80% accuracy in predicting severe cases.

IMPORTANCE Severe acute respiratory syndrome coronavirus 2 has caused a global outbreak of coronavirus disease 2019 (COVID-19). Severe acute respiratory syndrome coronavirus 2 binds angiotensin-converting enzyme 2 of the rennin-angiotensin system, resulting in hypokalemia. OBJECTIVE To investigate the prevalence, causes, and clinical implications of hypokalemia, including its possible association with treatment outcomes, among patients with COVID-19.

AbstractsThe clinical features and treatment of pulmonary tuberculosis patients with COVID-19 is unclear and understudied. Here, three pulmonary tuberculosis patients with COVID-19 infection were prospectively followed from hospital admission to discharge. We provide information and experience with treatment of pulmonary tuberculosis cases with confirmed COVID-19 infection.

medRxiv preprint WHAT IS ALREADY KNOWN ON THIS TOPICThere are several reports about the serum antibodies against SARS-CoV-2. However, most of them evaluate diagnostic accuracy. Only two articles report dynamics of SARS-CoV-2 viral RNA and antibodies with serial samples, but the observation periods are within 30 days. None of the studies investigate the profiles of SARS-CoV-2 viral load and antibodies in a long period. Three reports investigate profiles in respiratory samples, but there are no reports on the dynamics of the viral load in stool samples. WHAT THIS STUDY ADDSIn both sputum and stool, SARS-CoV-2 RNA persists for a long time. The anti-RBD antibodies may involve in the clearance of SARS-CoV-2 infection. After eight weeks from symptom onset, IgM were negative in many of the previously positive patients, and IgG levels remained less than 50% of the peak levels in more than 20% of the patients. In about 40% of the patients, anti-RBD IgG levels increased 4-time higher in convalescence than in acute phase. Long persistence of SARS-CoV-2 viral RNA in sputum and stool presents challenges for management of the infection. The IgM/IgG comb test is better than single IgM test as a supplement diagnostic tool. Anti-RBD may be a protective antibody, and is valuable for development of vaccines. ABSTRACT OBJECTIVETo investigate the dynamics of viral RNA, IgM, and IgG and their relationships in patients with SARS-CoV-2 pneumonia over an 8-week period. DESIGNRetrospective, observational case series. SETTING Wenzhou Sixth People's HospitalPARTICIPANTS Thirty-three patients with laboratory confirmed SARS-CoV-2 pneumonia admitted to hospital. Data were collected from MAIN OUTCOME MEASURES Throat swabs, sputum, stool, and blood samples were collected, and viral load was measured by reverse transcription PCR (RT-PCR). Specific IgM and IgG against spike protein (S), spike protein receptor binding domain (RBD), and nucleocapsid (N) were analyzed. RESULTSAt the early stages of symptom onset, SARS-CoV-2 viral load is higher in throat swabs and sputum, but lower in stool. The median (IQR) time of undetectable viral RNA in throat swab, sputum, and stool was 18.5 (13.25-22) days, 22 (18.5-27.5) days, and 17 (11.5-32) days, respectively. In sputum, 17 patients (51.5%) had undetectable viral RNA within 22 days (short persistence), and 16 (48.5%) had persistent viral RNA more than 22 days (long persistence). Three patients (9.1%) had a detectable relapse of viral RNA in sputum within two weeks of their discharge from the hospital. One patient had persistent viral RNA for 59 days or longer. The median (IQR) seroconversion time of anti-S IgM, anti-RBD IgM, and anti-N IgM was 10.5 (7.75-15.5) days, 14 (9-24) days, and 10 (7-14) days, respectively. The median (IQR) seroconversion time of anti-S IgG, anti-RBD IgG, and anti-N IgG was 10 (7.25-16.5) days, 13 (9-17) days, and 10 (7-14) days, respectively. By week 8 after symptom onset, IgM were negative in many of the previously positive patients, and IgG levels remained less than 50% of the p...

Objective: To quantify coronavirus diseases 2019 (COVID-19) pneumonia and to explore whether quantitative computer tomography (CT) could be used to assess severity on admission. Materials and methods: From January 17 to February 9, 2020, 38 hospitalized patients with COVID-19 pneumonia were consecutively enrolled in our hospitals. All clinical data and the chest CT on admission were retrospectively reviewed and analyzed. Firstly, a quantitative method based on multi-scale convolutional neural networks was used to assess the infected lung segments and this was compared with the semi-quantitative method. Secondly, the quantitative method was tested with laboratory results and the pneumonia severity index (PSI) by correlation analyses. Thirdly, both quantitative and semi-quantitative parameters between patients with different PSI were compared. Results: Thirty cases were finally enrolled: 16 (53.33%) of them were male, and the mean age was 48 years old. The interval from onset symptoms to first chest CT scan was 8 days. The proportion of ground glass opacity (GGO), consolidation and the total lesion based on the quantitative method was positively correlated with the semi-quantitative CT score (P < 0.001 for all; rs ¼ 0.88, 0.87, 0.90), CRP (P ¼ 0.0278, 0.0168, 0.0078; rs ¼ 0.40, 0.43, 0.48) and ESR (P ¼ 0.0296, 0.0408, 0.0048; rs ¼ 0.46, 0.44, 0.58), respectively, and was negatively correlated with the lymphocyte count (P ¼ 0.0222, 0.0024, 0.0068; rs ¼ À0.42, À0.53, À0.48). There was a positive correlation trend between the proportion of total infection and the pneumonia severity index (P ¼ 0.0994; rs ¼ 0.30) and a tendency that patients with severe COVID-19 pneumonia had higher percentage of consolidation and total infection (P ¼ 0.0903, 0.0989). Conclusions: Quantitative CT may have potential in assessing the severity of COVID-19 pneumonia on admission.

A newly identified coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the infectious coronavirus disease 2019 (COVID-19), emerged in December 2019 in Wuhan, Hubei Province, China, and now poses a major threat to global public health. Previous studies have observed highly variable alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels in patients with COVID-19. However, circulating levels of the cholangiocyte injury biomarker gamma-glutamyltransferase (GGT) have yet to be reported in the existing COVID-19 case studies. Herein, we describe the relationship between GGT levels and clinical and biochemical characteristics of patients with COVID-19. Our study is a retrospective case series of 98 consecutive hospitalized patients with confirmed COVID-19 at Wenzhou Central Hospital in Wenzhou, China, from January 17 to February 5, 2020. Clinical data were collected using a standardized case report form. Diagnosis of COVID-19 was assessed by symptomatology, reverse-transcription polymerase chain reaction (RT-PCR), and computed tomography scan. The medical records of patients were analyzed by the research team. Of the 98 patients evaluated, elevated GGT levels were observed in 32.7%; increased C-reactive protein (CRP) and elevated ALT and AST levels were observed in 22.5%, 13.3%, and 20.4%, respectively; and elevated alkaline phosphatase (ALP) and triglycerides (TGs) were found in 2% and 21.4%, respectively. Initially, in the 82 patients without chronic liver disease and alcohol history, age older than 40 years (P = 0.027); male sex (P = 0.0145); elevated CRP (P = 0.0366), ALT (P < 0.0001), and ALP (P = 0.0003); and increased TGs (P = 0.0002) were found to be associated with elevated GGT levels. Elevated GGT (P = 0.0086) and CRP (P = 0.0162) levels had a longer length of hospital stay. Conclusion: A sizable number of patients with COVID-19 infection have elevated serum GGT levels. This elevation supports involvement of the liver in persons with COVID-19. (Hepatology Communications 2020;0:1-7). C oronavirus disease 2019 (COVID-19), an infectious disease characterized by fever and pneumonia, is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Deep-sequencing analysis from lower respiratory tract samples indicated that this pathogen is a novel coronavirus. (1) COVID-19 may progress rapidly to acute respiratory distress syndrome with considerable

Little is known regarding why a subset of COVID-19 patients exhibited prolonged positivity of SARS-CoV-2 infection. Here, we found that patients with long viral RNA course (LC) exhibited prolonged high-level IgG antibodies and higher regulatory T (Treg) cell counts compared to those with short viral RNA course (SC) in terms of viral load. Longitudinal proteomics and metabolomics analyses of the patient sera uncovered that prolonged viral RNA shedding was associated with inhibition of the liver X receptor/retinoid X receptor (LXR/RXR) pathway, substantial suppression of diverse metabolites, activation of the complement system, suppressed cell migration, and enhanced viral replication. Furthermore, a ten-molecule learning model was established which could potentially predict viral RNA shedding period. In summary, this study uncovered enhanced inflammation and suppressed adaptive immunity in COVID-19 patients with prolonged viral RNA shedding, and proposed a multi-omic classifier for viral RNA shedding prediction.