Background: A patient's infectivity is determined by the presence of the virus in different body fluids, secretions, and excreta. The persistence and clearance of viral RNA from different specimens of patients with 2019 novel coronavirus disease (COVID-19) remain unclear. This study analyzed the clearance time and factors influencing 2019 novel coronavirus (2019-nCoV) RNA in different samples from patients with COVID-19, providing further evidence to improve the management of patients during convalescence. Methods: The clinical data and laboratory test results of convalescent patients with COVID-19 who were admitted to from January 20, 2020 to February 10, 2020 were collected retrospectively. The reverse transcription polymerase chain reaction (RT-PCR) results for patients' oropharyngeal swab, stool, urine, and serum samples were collected and analyzed. Convalescent patients refer to recovered non-febrile patients without respiratory symptoms who had two successive (minimum 24 h sampling interval) negative RT-PCR results for viral RNA from oropharyngeal swabs. The effects of cluster of differentiation 4 (CD4)+ T lymphocytes, inflammatory indicators, and glucocorticoid treatment on viral nucleic acid clearance were analyzed. Results: In the 292 confirmed cases, 66 patients recovered after treatment and were included in our study. In total, 28 (42.4%) women and 38 men (57.6%) with a median age of 44.0 (34.0-62.0) years were analyzed. After in-hospital treatment, patients' inflammatory indicators decreased with improved clinical condition. The median time from the onset of symptoms to first negative RT-PCR results for oropharyngeal swabs in convalescent patients was 9.5 (6.0-11.0) days. By February 10, 2020, 11 convalescent patients (16.7%) still tested positive for viral RNA from stool specimens and the other 55 patients' stool specimens were negative for 2019-nCoV following a median duration of 11.0 (9.0-16.0) days after symptom onset. Among these 55 patients, 43 had a longer duration until stool specimens were negative for viral RNA than for throat swabs, with a median delay of 2.0 (1.0-4.0) days. Results for only four (6.9%) urine samples were positive for viral nucleic acid out of 58 cases; viral RNA was still present in three patients' urine specimens after throat swabs were negative. Using a multiple linear regression model (F = 2.669, P = 0.044, and adjusted R 2 = 0.122), the analysis showed that the CD4+ T lymphocyte count may help predict the duration of viral RNA detection in patients' stools (t = À2.699, P = 0.010). The duration of viral RNA detection from oropharyngeal swabs and fecal samples in the glucocorticoid treatment group was longer than that in the nonglucocorticoid treatment group (15 days vs. 8.0 days, respectively; t = 2.550, P = 0.013) and the duration of viral RNA detection in fecal samples in the glucocorticoid treatment group was longer than that in the non-glucocorticoid treatment group (20 days vs. 11 days, respectively; t = 4.631, P < 0.001). There was no statistically significant dif...

The study aimed to explore the influencing factors on critical coronavirus disease 2019 (COVID-19) patients’ prognosis and to construct a nomogram model to predict the mortality risk. We retrospectively analyzed the demographic data and corresponding laboratory biomarkers of 102 critical COVID-19 patients with a residence time ≥ 24 h and divided patients into survival and death groups according to their prognosis. Multiple logistic regression analysis was performed to assess risk factors for critical COVID-19 patients and a nomogram was constructed based on the screened risk factors. Logistic regression analysis showed that advanced age, high peripheral white blood cell count (WBC), low lymphocyte count (L), low platelet count (PLT), and high-sensitivity C-reactive protein (hs-CRP) were associated with critical COVID-19 patients mortality risk (p < 0.05) and these were integrated into the nomogram model. Nomogram analysis showed that the total factor score ranged from 179 to 270 while the corresponding mortality risk ranged from 0.05 to 0.95. Findings from this study suggest advanced age, high WBC, high hs-CRP, low L, and low PLT are risk factors for death in critical COVID-19 patients. The Nomogram model is helpful for timely intervention to reduce mortality in critical COVID-19 patients.

ObjectivesTotal Health Risks in Vascular Events‐calculation score (THRIVE‐c) is an easy use and patient‐specific outcome predictive score by computing the logistic equation with patients’ continuous variables. We validated its performance in Chinese ischemic stroke patients receiving intravenous thrombolysis (IVT) therapy.Materials and MethodsWe used data from the Thrombolysis Implementation and Monitor of Acute Ischemic Stroke in China (TIMS‐China) registry to validate the THRIVE‐c score in patients receiving IVT therapy. We evaluated the score performance using area under the receiver operating characteristic curve (AUC). Receiver operator characteristic curve (ROC) was used to compare THRIVE‐c score performance with other scores in predicting clinical outcome and symptomatic intracranial hemorrhage (SICH). Calibration was assessed by Pearson correlation coefficient and Hosmer–Lemeshow test.ResultsAmong the 1,128 patients receiving IVT therapy included in this study, AUC of the THRIVE‐c score for 3‐month SICH, poor functional outcome, and mortality rate was 0.70 (95% CI: 0.63–0.76), 0.75 (95% CI: 0.73–0.78) and 0.81 (95% CI: 0.77–0.85), respectively. The increased THRIVE‐c score was associated with higher risk of developing SICH, poor functional outcome, or mortality in patients with acute ischemic stroke at 3 months after thrombolysis. The performance of the THRIVE‐c score was similar to or superior to other predictive scores (THRIVE score, SEDAN score, DRAGON score, HIAT2 score).ConclusionsThe THRIVE‐c score reliably predicts the risks of 3‐month SICH, poor functional outcome, and mortality after IVT therapy in Chinese patients with ischemic stroke.

To discuss influencing factors on critical COVID-19 patient’s prognosis, construct a basic model and predict their mortality risks. Retrospectively analyzed the general condition and respective laboratory biomarkers of critical patients with duration≥24 h from Feb. 10th, 2020 to Mar. 30th, 2020 to separate them into a survival group and death group based on their clinical features. Multiple logistic regression analysis was performed to assess risk factors for critical COVID-19 patient’s and a nomogram was constructed based on screened risk factors. A receiver operating curve (ROC) was created to evaluate the accuracy of the nomogram. Multi-factor Logistic recovery analysis results show: Age, Peripheral blood leucocyte count,Lymphocyte percentage, Thrombocyte count and Hyper C-reactive protein are single danger factors of critical COVID-19 patient’s mortality risk (p＜0.05). ROC curve indicates Nomogram predictive model AUC is 0.958 (95%CI: 0.923-0.993), which has high predictive value. Findings from this study suggest advanced age, high peripheral blood leucocyte count, high hyper C-reactive protein, low lymphocyte percentage and low thrombocyte count are risk factors of critical COVID-19 patient’s death.The Nomogram model is helpful for timely intervention to reduce the incidence of critical COVID-19 patients.