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=9

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=<sec><title>Introduction</title><p>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.</p></sec><sec><title>Methods</title><p>We enrolled and retrospectively analyzed the demographic data and clinical characters of 23,145 laboratory-confirmed cases with non-critical COVID-19. Participants were followed for 30 days and randomly allocated to either 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 and then incorporated into the nomogram.</p></sec><sec><title>Results</title><p>The study showed that the 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 (<ext-link ext-link-type="uri" xlink:href="https://anananan1.shinyapps.io/DynNomapp2/" xmlns:xlink="http://www.w3.org/1999/xlink">https://anananan1.shinyapps.io/DynNomapp2/</ext-link>). The AUC in the training cohort was 0.719 [95% confidence interval (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.</p></sec><sec><title>Conclusion</title><p>The risk model can help identify and take proper management in high-risk individuals toward the containment of the pandemic in the community.</p></sec>