A Novel Prediction Model for Long-Term SARS-CoV-2 RNA Shedding in Non-Severe Adult Hospitalized Patients with COVID-19: A Retrospective Cohort Study.

INTRODUCTION

Due to the lack of clear direction (evidence) on the duration of viral shedding and thus potential for transmission, this retrospective study aimed to come up with a prediction model of prolonged coronavirus disease-19 (COVID-19) transmission or infection-spreading potential.

METHODS

A total of 1211 non-severe patients with COVID-19 were retrospectively enrolled. Multivariate Cox regression was performed to identify the risk factors associated with long-term SARS-CoV-2 RNA shedding, and a prediction model was established.

RESULTS

In the training set, 796 patients were divided into the long-term (> 21 days) group (n = 116, 14.6%) and the short-term (≤ 21 days) group (n = 680, 85.4%) based on their viral shedding duration. Multivariate analysis identified that age > 50 years, comorbidity, CD4-positive T-lymphocytes count (CD4 + T cell) ≤ 410 cells/ul, C-reactive protein (CRP) > 10 mg/L, and the corticosteroid use were independent risk factors for long-term SARS-CoV-2 RNA shedding. Incorporating the five risk factors, a prediction model, named as the CCCCA score, was established, and its area under the receiver operator characteristic curve (AUROC) was 0.87 in the training set and 0.83 in the validation set, respectively. In the validation set, using a cut-off of 8 points, we found sensitivity, specificity, positive predictive value, and negative predictive value of 51.7%, 92.2%, 33.3%, and 96.2%, respectively. Long-term SARS-CoV-2 RNA shedding increased from 14/370 (3.8%) in patients with CCCCA < 8 points to 15/45 (33.3%) in patients with CCCCA ≥ 8 points.

CONCLUSION

Using the CCCCA score, clinicians can identify patients with long-term SARS-CoV-2 RNA shedding.