Evidence map›Paper›PMID 36600892›Full record

ArticleFrontiers in medicine2022

A diagnostic model for serious COVID-19 infection among older adults in Shanghai during the Omicron wave.

Suxia Bao, Guanzhu Lu, Yaoyue Kang, Yuanyuan Zhou, Yuhuan Wang, Lei Yan, Donglin Yin, Yujie Bao, Xiaoling Yuan, Jie Xu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-weighted citation impact, top 25% of its field
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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
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  4. Observational
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 at 2 institutions in 1 country.

Suxia BaoDepartment of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Guanzhu LuDepartment of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yaoyue KangDepartment of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yuanyuan ZhouDepartment of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yuhuan WangDepartment of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lei YanDepartment of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Donglin YinDepartment of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yujie BaoDepartment of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiaoling YuanDepartment of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jie XuDepartment of Infectious Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shanghai Ninth People's Hospital · CNShanghai Jiao Tong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The Omicron variant is characterized by striking infectivity and antibody evasion. The analysis of Omicron variant BA.2 infection risk factors is limited among geriatric individuals and understanding these risk factors would promote improvement in the public health system and reduction in mortality. Therefore, our research investigated BA.2 infection risk factors for discriminating severe/critical from mild/moderate geriatric patients. Methods: Baseline characteristics of enrolled geriatric patients (aged over 60 years) with Omicron infections were analyzed. A logistic regression analysis was conducted to evaluate factors correlated with severe/critical patients. A receiver operating characteristic (ROC) curve was constructed for predicting variables to discriminate mild/moderate patients from severe/critical patients. Results: A total of 595 geriatric patients older than 60 years were enrolled in this study. Lymphocyte subset levels were significantly decreased, and white blood cells (WBCs) and D-dimer levels were significantly increased with disease progression from a mild/moderate state to a severe/critical state. Univariate and multivariate logistic regression analyses identified a panel of WBCs, CD4 Conclusion: Some key baseline laboratory indicators change with disease development. A panel was identified for discriminating mild/moderate patients from severe/critical patients, suggesting that the panel could serve as a potential biomarker to enable physicians to provide timely medical services in clinical practice.

Indexed as

diagnose modeldiscriminategeriatric patientsserious COVID-19 infectionShanghaiShanghai diagnose model

Identifiers

PMID36600892
PMCPMC9806114
OpenAlexW4311873355

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.