Virus load and virus shedding of SARS-CoV-2 and their impact onpatient outcomes

BACKGROUND Understanding a virus shedding patterns in body fluids/secretions is importantto determine the samples to be used for diagnosis and to formulate infectioncontrol measures.AIM To investigate the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)shedding patterns and its risk factors.METHODS All laboratory-confirmed coronavirus disease 2019 patients with completemedical records admitted to the Shenzhen Third People’s Hospital from January28, 2020 to March 8, 2020 were included. Among 145 patients (54.5% males;median age, 46.1 years), three (2.1%) died. The bronco-alveolar lavage fluid(BALF) had the highest virus load compared with the other samples. The viralload peaked at admission (3.3 × 108 copies) and sharply decreased 10 d afteradmission.RESULTS The viral load was associated with prolonged intensive care unit (ICU) duration.Patients in the ICU had significantly longer shedding time compared to those inthe wards (P < 0.0001). Age > 60 years [hazard ratio (HR) = 0.6;95% confidenceinterval (CI): 0.4-0.9] was an independent risk factor for SARS-CoV-2 shedding,while chloroquine (HR = 22.8;95%CI: 2.3-224.6) was a protective factor.CONCLUSION BALF had the highest SARS-CoV-2 load. Elderly patients had higher virus loads,which was associated with a prolonged ICU stay. Chloroquine was associatedwith shorter shedding duration and increased the chance of viral negativity.

Validated tool for early prediction of intensive care unit admission in COVID-19 patients

BACKGROUND The novel coronavirus disease 2019(COVID-19)pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2.AIM To develop and validate a risk stratification tool for the early prediction of intensive care unit(ICU)admission among COVID-19 patients at hospital admission.METHODS The training cohort included COVID-19 patients admitted to the Wuhan Third Hospital.We selected 13 of 65 baseline laboratory results to assess ICU admission risk,which were used to develop a risk prediction model with the random forest(RF)algorithm.A nomogram for the logistic regression model was built based on six selected variables.The predicted models were carefully calibrated,and the predictive performance was evaluated and compared with two previously published models.RESULTS There were 681 and 296 patients in the training and validation cohorts,respectively.The patients in the training cohort were older than those in the validation cohort(median age:63.0 vs 49.0 years,P<0.001),and the percentages of male gender were similar(49.6%vs 49.3%,P=0.958).The top predictors selected in the RF model were neutrophil-to-lymphocyte ratio,age,lactate dehydrogenase,C-reactive protein,creatinine,D-dimer,albumin,procalcitonin,glucose,platelet,total bilirubin,lactate and creatine kinase.The accuracy,sensitivity and specificity for the RF model were 91%,88%and 93%,respectively,higher than those for the logistic regression model.The area under the receiver operating characteristic curve of our model was much better than those of two other published methods(0.90 vs 0.82 and 0.75).Model A underestimated risk of ICU admission in patients with a predicted risk less than 30%,whereas the RF risk score demonstrated excellent ability to categorize patients into different risk strata.Our predictive model provided a larger standardized net benefit across the major high-risk range compared with model A.CONCLUSION Our model can identify ICU admission risk in COVID-19 patients at admission,who can then receive prompt care,thus improving medical resource allocation.