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

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

doi:10.3389/fmed.2022.1002188

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

Front Med (Lausanne). 2023 Feb 1:9:1002188. doi: 10.3389/fmed.2022.1002188. eCollection 2022.

Authors

Anni Li¹, Chao Wang², An Cui¹, Lingyu Zhou², Wei Hu¹, Senlin Ma², Dian Zhang², Hong Huang³, Mingquan Chen^{1

2}

Affiliations

¹ 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.
² Department of Emergency Medicine, Huashan Hospital, Fudan University, Shanghai, China.
³ Information Center, Huashan Hospital, Fudan University, Shanghai, China.

Abstract

Introduction: 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.

Methods: 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.

Results: 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 (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.

Conclusion: The risk model can help identify and take proper management in high-risk individuals toward the containment of the pandemic in the community.

Keywords: COVID-19; SARS-CoV-2; non-critical; re-positive; recurring; risk score.