Evidence map›Paper›PMID 38779041›Full record

ArticleERJ open research2024

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

Weilong Zhang, Xiaoyan Gai, Ben Wang, Zhonghui Duan, Qingtao Zhou, Lili Dai, Changjian Yan, Chaoling Wu, Jiarun Fan, Ping Wang and 7 more

Abstract read
In one paragraph

Article in ERJ open research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Weilong ZhangDepartment of Hematology, Lymphoma Research Center, Peking University Third Hospital, Beijing, China.ORCID https://orcid.org/0009-0009-4052-200X
Xiaoyan GaiDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, and Center for Chronic Airway Diseases, Peking University Health Science Center, Peking University, Beijing, China.
Ben WangOrthopedics Department, Peking University Third Hospital, Beijing, China.
Zhonghui DuanDepartment of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Qingtao ZhouDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, and Center for Chronic Airway Diseases, Peking University Health Science Center, Peking University, Beijing, China.
Lili DaiDepartment of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Changjian YanDepartment of Hematology, Lymphoma Research Center, Peking University Third Hospital, Beijing, China.
Chaoling WuDepartment of Hematology, Lymphoma Research Center, Peking University Third Hospital, Beijing, China.
Jiarun FanDepartment of Hematology, Lymphoma Research Center, Peking University Third Hospital, Beijing, China.
Ping WangDepartment of Hematology, Lymphoma Research Center, Peking University Third Hospital, Beijing, China.
Ping YangDepartment of Hematology, Lymphoma Research Center, Peking University Third Hospital, Beijing, China.
Fang BaoDepartment of Hematology, Lymphoma Research Center, Peking University Third Hospital, Beijing, China.
Hongmei JingDepartment of Hematology, Lymphoma Research Center, Peking University Third Hospital, Beijing, China.
Chao CaiDepartment of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Chunli SongOrthopedics Department, Peking University Third Hospital, Beijing, China.
Yingmin MaDepartment of Respiratory and Critical Care Medicine, Beijing Youan Hospital, Capital Medical University, Beijing, China.
Yongchang SunDepartment of Respiratory and Critical Care Medicine, Peking University Third Hospital, and Center for Chronic Airway Diseases, Peking University Health Science Center, Peking University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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/).

Identifiers

PMID38779041
PMCPMC11111115

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.