Evidence map›Paper›PMID 37228665›Full record

ArticleFrontiers in cellular and infection microbiology2023

Understanding clinical characteristics influencing adverse outcomes of Omicron infection: a retrospective study with propensity score matching from a Fangcang hospital.

Yanxia Geng, Qingfang Nie, Feifei Liu, Yinghao Pei, Qiuhua Chen, Haidong Zhang, Haiqi Zhou, Jiang Zhou, Hua Jiang, Jing Xu

Abstract read
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2023. 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

10 authors.

Yanxia GengDepartment of Intensive Care Unit, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, China.
Qingfang NieDepartment of Intensive Care Unit, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, China.
Feifei LiuDepartment of Intensive Care Unit, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, China.
Yinghao PeiDepartment of Intensive Care Unit, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, China.
Qiuhua ChenDepartment of Intensive Care Unit, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, China.
Haidong ZhangDepartment of Intensive Care Unit, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, China.
Haiqi ZhouDepartment of Intensive Care Unit, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, China.
Jiang ZhouDepartment of Intensive Care Unit, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, China.
Hua JiangDepartment of Intensive Care Unit, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, China.
Jing XuDepartment of Nosocomial Infection Control, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The epidemic of coronavirus disease 2019 (COVID-19) is causing global health concerns. The aim of this study was to evaluate influence of clinical characteristics on outcomes during the Omicron outbreak. Methods: A total of 25182 hospitalized patients were enrolled, including 39 severe patients and 25143 non-severe patients. Propensity score matching (PSM) was applied to balance the baseline characteristics. Logistic regression analysis was used to assess the risk of severe disease, as well as the risk of prolonged viral shedding time (VST) and increased length of hospital stay (LOS). Results: Before PSM, patients in the severe group were older, had higher symptom scores, and had a higher proportion of comorbidities ( Conclusion: The overall condition of the Shanghai Omicron epidemic was relatively mild. Potential risk factors for fever, diarrhea, and higher symptom score can help clinicians to predict clinical outcomes in COVID-19 patients.

Indexed as

COVID-19ChinaDiarrheaHospitalsHumansPropensity ScoreRetrospective Studiesclinical outcomeCOVID-19diarrheaOmicronsymptoms

Identifiers

PMID37228665
PMCPMC10203467

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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.