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

Frontiers in Medicine

Medicine

Lingyu Zhou¹  Chao Wang¹  Senlin Ma¹  Dian Zhang¹  Hong Huang²  Anni Li³  An Cui³  Wei Hu³  Mingquan Chen⁴ 

[1] Department of Emergency Medicine, Huashan Hospital, Fudan University, Shanghai, China;Information Center, Huashan Hospital, Fudan University, Shanghai, China;Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Department of Infectious Diseases, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China;Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Department of Infectious Diseases, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China;Department of Emergency Medicine, Huashan Hospital, Fudan University, Shanghai, China;

关键词: risk score;    SARS-CoV-2;    non-critical;    recurring;    COVID-19;    re-positive;   

DOI  :  10.3389/fmed.2022.1002188

 received in 2022-07-24, accepted in 2022-12-28,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionRecurrent 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.MethodsWe 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.ResultsThe 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 (https://anananan1.shinyapps.io/DynNomapp2/). 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.ConclusionThe risk model can help identify and take proper management in high-risk individuals toward the containment of the pandemic in the community.

【 授权许可】

Unknown   
Copyright © 2023 Li, Wang, Cui, Zhou, Hu, Ma, Zhang, Huang and Chen.