Background Because there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19. Methods In this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance. Results The training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846–.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790–.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit. Conclusions Our nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease.

Highlights People with positive SARS-CoV-2 RNA of respiratory tract specimen are infectious source of COVID-19. SARS-CoV-2 RNA of respiratory tract specimen may be persistent or recurrent positive during the course. Dynamic surveillance of SARS-CoV-2 RNA should be performed for infectivity assessment. AbstractThe ongoing outbreak of COVID-19 that began in Wuhan, China, has constituted a Public Health Emergency of International Concern, with cases confirmed in multiple countries. Currently patients are the main source of infection. We report a confirmed case of COVID-19 whose oropharyngeal swab test of SARS-CoV-2 RNA turned positive in convalescence. This case highlights the J o u r n a l P r e -p r o o f 2 importance of dynamic surveillance of SARS-CoV-2 RNA for infectivity assessment.

Purpose: The purpose of this study was to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ribonucleic acid (RNA) in urine and blood specimens, and anal and oropharyngeal swabs from patients with confirmed SARS-CoV-2 infection, and correlated positive results with clinical findings. Methods: Patients with confirmed SARS-CoV-2 infections were included in this study. Patients' demographic and clinical data were recorded. Quantitative real-time polymerase chain reaction was used to detect SARS-CoV-2 RNA in urine and blood specimens, and anal and oropharyngeal swabs. The study is registered at ClinicalTrials.gov (No. NCT04279782, 19 February, 2020). Results: SARS-CoV-2 RNA was present in all four specimen types, though not all specimen types were positive simultaneously. The presence of viral RNA was not necessarily predictive of clinical symptoms, for example, the presence of viral RNA in the urine did not necessarily predict urinary tract symptoms. Conclusions: SARS-CoV-2 can infect multiple systems, including the urinary tract. Testing different specimen types may be useful for monitoring disease changes and progression, and for establishing a prognosis. K E Y W O R D S prognosis, real-time polymerase chain reaction, SARS-CoV-2, specimens, urine

Older age; higher LDH, CRP, RDW, DBIL, BUN; lower ALB on admission correlated with higher odds of severe COVID-19. An effective prognostic nomogram composed of 7 features could allow early identification of patients at risk of exacerbation to severe COVID-19. Abstract BackgroundDue to no reliable risk stratification tool for severe corona virus disease 2019 patients at admission, we aimed to construct an effective model for early identifying cases at high risk of progression to severe COVID-19. MethodsIn this retrospective three-centers study, 372 non-severe COVID-19 patients during hospitalization were followed for more than 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and patients who kept non-severe state were assigned to the severe and non-severe group, respectively. Based on baseline data of the two groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluate its performance. ResultsThe train cohort consisted of 189 patients, while the two independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.35%) patients developed severe COVID-19. We found that old age, and higher serum lactate dehydrogenase, C-reactive protein, the coefficient of variation of red blood cell distribution width, blood urea nitrogen, direct bilirubin, lower albumin, are associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the train cohort (AUC 0.912 [95% CI 0.846-0.978], sensitivity 85.71%, specificity 87.58%); in validation cohort (0.853 [0.790-0.916], 77.5%, 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analysis indicated that nomogram conferred high clinical net benefit. ConclusionOur nomogram could help clinicians to early identify patients who will exacerbate to severe COVID-19, which will enable better centralized management and early treatment of severe patients.

We tested samples collected from nine patients diagnosed with coronavirus disease 2019 (COVID-19). The virus was found in urine, blood, anal swabs and oropharyngeal swabs. It is the first time for SARS-CoV-2 found in urine, though no urinary irritation was found.