Abstract
BACKGROUND: Clinical decision-making is enhanced by the development of a mathematical model for prognosis prediction. Screening criteria associated with viral shedding time and developing a prediction model facilitate clinical decision-making and are, thus, of great medical value. METHODS: This study comprised 631 patients who were hospitalized with mild COVID-19 from a single center and 30 independent variables included. The data set was randomly divided into the training set (80%) and the validation set (20%). The outcome variable included viral shedding time and whether the viral shedding time >14 days, LASSO was used to screen the influencing factors. RESULTS: There were 321 males and 310 females among the 631 cases, with an average age of 62.1 years; the median viral shedding time was 12 days, and 68.8% of patients experienced viral shedding within 14 days, with fever (50.9%) and cough (44.2%) being the most common clinical manifestations. Using LASSO with viral shedding time as the outcome variable, the model with lambda as 0.1592 (λ = 0.1592) and 13 variables (eg the time from diagnosis to admission, constipation, cough, hs-CRP, IL-8, IL-1β, etc.) was more accurate. Factors were screened by LASSO and multivariable logistic regression with whether the viral shedding time >14 days as the outcome variable, five variables, including the time from diagnosis to admission, CD4 cell count, Ct value of ORF1ab, constipation, and IL-8, were included, and a nomogram was drawn; after model validation, the consistency index was 0.888, the AUC was 0.847, the sensitivity was 0.744, and the specificity was 0.830. CONCLUSION: A clinical model developed after LASSO regression was used to identify the factors that influence the viral shedding time. The predicted performance of the model was good, and it was useful for the allocation of medical resources.