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

Abstract Background: Because of the lack of compelling evidence for predicting the duration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA shedding, the purpose of this retrospective study was to establish a predictive model for long-term SARS-CoV-2 RNA shedding in non-death hospitalized patients with coronavirus disease-19 (COVID-19).Methods: 97 non-death hospitalized patients with COVID-19 admitted to two hospitals in Henan province of China from February 3, 2020 to March 31, 2020 were retrospectively enrolled. Multivariate logistic regression was performed to identify the high risk factors associated with long-term SARS-CoV-2 RNA shedding and a predictive model was established and represented by a nomogram. Its performance was assessed with discrimination and calibration.Results: 97 patients were divided into the long-term (>21 days) group (n = 27, 27.8%) and the short-term (≤ 21 days) group (n = 70, 72.2%) based on their viral shedding duration. Multivariate logistic regression analysis showed that time from illness onset to diagnosis (OR 1.224, 95% CI 1.070-1.400, P = 0.003) and interstitial opacity in chest computerized tomography(CT) scan (OR 6.516, 95% CI 2.041-20.798, P = 0.002) were independent risk factors for long-term SARS-CoV-2 RNA shedding. A prediction model, which is presented with a nomogram, was established by incorporating the two risk factors. The goodness-of-fit statistics for the nomogram was not statistically significant (χ2 = 8.292; P = 0.406), and its area under the receiver operator characteristic curve was 0.834 (95% CI 0.731- 0.936; P < 0.001).Conclusion: The established model has a good predictive performance on the long-term viral RNA shedding in non-death hospitalized patients with COVID-19, but it still needs further validation by independent data set of large samples in the future.