Evidence map›Paper›PMID 32369205›Full record

ArticleJournal of medical virology2020

Individualized prediction nomograms for disease progression in mild COVID-19.

Jiaofeng Huang, Aiguo Cheng, Su Lin, Yueyong Zhu, Gongping Chen

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical virology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 4 pooled it
1.4field-weighted citation impact, top 10% 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

27 citing papers in PubMed, 4 syntheses or guidelines pooled it, 56 citations in OpenAlex.

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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

5 authors at 2 institutions in 1 country.

Jiaofeng HuangDepartment of Liver Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Aiguo ChengDepartment of Critical Care, The Third People's Hospital of Yichang, China.
Su LinDepartment of Liver Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.ORCID 0000-0001-7517-9859
Yueyong ZhuDepartment of Liver Research Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.ORCID 0000-0002-0746-4911
Gongping ChenDepartment of Respiratory, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Fujian Medical University · CNSecond Hospital of Yichang · CN

Funding

Chinese National 13th Five-Year Plan's Science and Technology Projects 2017ZX10202201Fujian Medical University Sailing Fund Project 2018QH1047Fujian Province Health Youth Research Project 2019-1-37
6 · The paper itself

Abstract

The coronavirus disease 2019 (COVID-19) has evolved into a pandemic rapidly. The majority of COVID-19 patients are with mild syndromes. This study aimed to develop models for predicting disease progression in mild cases. The risk factors for the requirement of oxygen support in mild COVID-19 were explored using multivariate logistic regression. Nomogram as visualization of the models was developed using R software. A total of 344 patients with mild COVID-19 were included in the final analysis, 45 of whom progressed and needed high-flow oxygen therapy or mechanical ventilation after admission. There were 188 (54.7%) males, and the average age of the cohort was 52.9  ± 16.8 years. When the laboratory data were not included in multivariate analysis, diabetes, coronary heart disease, T   ≥  38.5℃ and sputum were independent risk factors of progressive COVID-19 (Model 1). When the blood routine test was included the CHD, T ≥ 38.5℃ and neutrophil-to-lymphocyte ratio were found to be independent predictors (Model 2). The area under the receiver operator characteristic curve of model 2 was larger than model 1 (0.872 vs 0.849, P = .023). The negative predictive value of both models was greater than 96%, indicating they could serve as simple tools for ruling out the possibility of disease progression. In conclusion, two models comprised common symptoms (fever and sputum), underlying diseases (diabetes and coronary heart disease) and blood routine test are developed for predicting the future requirement of oxygen support in mild COVID-19 cases.

Indexed as

COVID-19Disease ProgressionFemaleHumansLymphocytesMaleMiddle AgedMultivariate AnalysisNeutrophilsPandemicsPneumonia, ViralRetrospective StudiesRisk FactorsROC CurveSARS-CoV-2COVID-19mildnomogramprogressionrisk factor

Identifiers

PMID32369205
PMCPMC7267495
OpenAlexW3023856687

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.