AUTHOR=Li Anni , Wang Chao , Cui An , Zhou Lingyu , Hu Wei , Ma Senlin , Zhang Dian , Huang Hong , Chen Mingquan 

TITLE=Clinical risk score for early prediction of recurring SARS-CoV-2 positivity in non-critical patients

JOURNAL=Frontiers in Medicine

VOLUME=Volume 9 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2022.1002188

DOI=10.3389/fmed.2022.1002188

ISSN=2296-858X

ABSTRACT=Recurrent positive results in quantitative reverse transcriptase-PCR (qRT-PCR) tests have been commonly observed in COVID-19 patients. We aimed to construct and validate a reliable risk stratification tool for early predictions of non-critical COVID-19 survivors’ risk of getting tested re-positive within 30 days. We enrolled and retrospectively analyzed the demographic data and clinical characters of 23145 laboratory-confirmed cases with non-critical COVID-19. Participants were followed for 30 days and randomly allocated either to a training (60%) or a validation (40%) cohort. Multivariate logistic regression models were employed to identify possible risk factors with the SARS-CoV-2 recurrent positivity, then incorporated into the nomogram. The study showed that overall proportion of re-positive cases within 30 days of the last negative test was 24.1%. In the training cohort, significantly contributing variables associated with the 30-day re-positivity were clinical type, COVID-19 vaccination status, myalgia, headache, admission time, and first negative conversion, which were integrated to build a nomogram and subsequently translate these scores into an online publicly available risk calculator (https://anananan1.shinyapps.io/DynNomapp2/). The AUC in the training cohort was 0.719 (95%CI, 0.712-0.727) with a sensitivity of 66.52% (95%CI, 65.73-67.30), and a specificity of 67.74% (95%CI, 66.97,68.52). A significant AUC of 0.716 (95%CI, 0.706-0.725) was obtained for the validation cohort with a sensitivity of 62.29% (95%CI, 61.30-63.28), and a specificity of 71.26% (95%CI, 70.34-72.18). The calibration curve exhibited a good coherence between the actual observation and predicted outcomes. The risk model can help identify and take proper management in high-risk individuals toward the containment of the pandemic in the community.