Evidence map›Paper›PMID 33318316›Full record

ArticleAging2020

Predicting severe or critical symptoms in hospitalized patients with COVID-19 from Yichang, China.

Xin Chen, Feng Peng, Xiaoni Zhou, Jiang Zhu, Xin Chen, Yingying Gong, Wang Shupeng, Wenquan Niu

Open access · hybridAbstract read
In one paragraph

Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.1field-weighted citation impact, top 44% 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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Observational
  2. 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

8 authors at 4 institutions in 1 country.

Xin ChenDepartment of Cardiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Feng PengDepartment of Cardiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Xiaoni ZhouDepartment of Pulmonary Disease, The Third People's Hospital of Yichang, Yichang, Hubei, China.
Jiang ZhuDepartment of Cardiology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Xin ChenDepartment of Cardiology Nursing Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Yingying GongDepartment of Cardiology Nursing Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Wang ShupengDepartment of Cardiology Nursing Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Wenquan NiuInstitute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China.
First Affiliated Hospital of Fujian Medical University · CNFujian Medical University · CNChina-Japan Friendship Hospital · CNSecond Hospital of Yichang · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWe aimed to identify potential risk factors for severe or critical coronavirus disease 2019 (COVID-19) and establish a prediction model based on significant factors.

methodsA total of 370 patients with COVID-19 were consecutively enrolled at The Third People's Hospital of Yichang from January to March 2020. COVID-19 was diagnosed according to the COVID-19 diagnosis and treatment plan released by the National Health and Health Committee of China. Effect-size estimates are summarized as odds ratio (OR) and 95% confidence interval (CI).

results326 patients were diagnosed with mild or ordinary COVID-19, and 44 with severe or critical COVID-19. After propensity score matching and statistical adjustment, eight factors were significantly associated with severe or critical COVID-19 (

conclusionsOur findings provide evidence for the significant contribution of five independent factors to the risk of severe or critical COVID-19, and their prediction was reinforced in a nomogram model.

Indexed as

COVID-19AgedBiomarkersChinaCritical IllnessDelayed DiagnosisFemaleHumansLeukocyte CountL-Lactate DehydrogenaseMaleMiddle AgedNomogramsRisk FactorsSARS-CoV-2Serum AlbuminBiomarkersL-Lactate DehydrogenaseSerum AlbuminCOVID-19nomogrampredictionrisksevere or critical symptoms

Identifiers

PMID33318316
PMCPMC7880337
OpenAlexW3110771438

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.