A robust web-based tool to predict viral shedding in patients with Omicron SARS-CoV-2 variants

Autor: Weilong Zhang, Xiaoyan Gai, Ben Wang, Zhonghui Duan, Qingtao Zhou, Lili Dai, Changjian Yan, Chaoling Wu, Jiarun Fan, Ping Wang, Ping Yang, Fang Bao, Hongmei Jing, Chao Cai, Chunli Song, Yingmin Ma, Yongchang Sun
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: ERJ Open Research, Vol 10, Iss 3 (2024)
Druh dokumentu: article
ISSN: 2312-0541
23120541
DOI: 10.1183/23120541.00939-2023
Popis: Background Data on viral kinetics and variants affecting the duration of viral shedding were limited. Our objective was to determine viral shedding in distinct severe acute respiratory syndrome coronavirus 2 variants, including Omicron BA.4/5 and BF.7, and to identify the relevant influencing factors. Methods We carried out a longitudinal cohort study at Beijing Xiaotangshan Fangcang shelter hospital from May to June 2022 (Omicron BA.4/5) and from November to December 2022 (Omicron BF.7). Nucleocapsid protein (N) and open reading frame (ORF) genes were considered as the target genes of the reverse transcription PCR. The daily results of cycle threshold (CT), including lowest ORF1ab-CT values for days 1–3 post-hospitalisation and lowest N-CT values for days 1–3 post-hospitalisation (CT3minN) and demographic and clinical characteristics were collected. Results 1433 patients with coronavirus disease 2019 (COVID-19) were recruited from the Fangcang shelter hospital, in which 278 patients were diagnosed with Omicron BA.4/5 and 1155 patients with Omicron BF.7. Patients with BF.7 infection showed a longer duration of viral shedding. The duration of viral shedding was associated with the variants age, alcohol use, the severity of COVID-19 and CT3minN. Moreover, the nomogram had excellent accuracy in predicting viral shedding. Conclusions Our results indicated that patients with Omicron BF.7 had a longer period of contagiousness than those with BA.4/5. The duration of viral shedding was affected by a variety of factors and the nomogram may become an applicable clinical instrument to predict viral shedding. Furthermore, we developed a new COVID-19 viral shedding predicting model that can accurately predict the duration of viral shedding for COVID-19, and created a user-friendly website to apply this prediction model (https://puh3.shinyapps.io/CVSP_Model/).
Databáze: Directory of Open Access Journals