Evidence map›Paper›PMID 34951061›Full record

ArticleJournal of clinical laboratory analysis2022

Early predictors of severe COVID-19 among hospitalized patients.

Qiongrui Zhao, Youhua Yuan, Jiangfeng Zhang, Jieren Li, Wei Li, Kunshan Guo, Yanchao Wang, Juhua Chen, Wenjuan Yan, Baoya Wang and 3 more

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

Article in Journal of clinical laboratory analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
0.4field-weighted citation impact, top 36% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Early predictors of severe COVID-19 among hospitalized patients.Journal of clinical laboratory analysis · 2022
    Article
  6. Predictive Value of Systemic Immune-Inflammation index and Neutrophil-to-Lymphocyte Ratio in Patients with Severe COVID-19.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    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

13 authors at 5 institutions in 2 countries.

Qiongrui ZhaoCentre of Clinical Research Service, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China.
Youhua YuanDepartment of Clinical Microbiology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China.ORCID https://orcid.org/0000-0002-4341-2561
Jiangfeng ZhangDepartment of Critical Care Medicine, Huaibin County People's Hospital, Huaibin, China.
Jieren LiDepartment of Critical Care Medicine, Huaibin County People's Hospital, Huaibin, China.
Wei LiDepartment of Infectious Disease, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China.
Kunshan GuoDepartment of Clinical Microbiology, Xuchang Municipal Central Hospital, Xuchang, China.
Yanchao WangDepartment of Clinical Microbiology, Hebi Infectious Disease Hospital, Hebi, China.
Juhua ChenDepartment of Clinical Microbiology, Xinyang Municipal First People's Hospital, Xinyang, China.
Wenjuan YanDepartment of Clinical Microbiology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China.
Baoya WangDepartment of Clinical Microbiology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China.
Nan JingDepartment of Clinical Microbiology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China.
Bing MaDepartment of Clinical Microbiology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China.
Qi ZhangDepartment of Clinical Microbiology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China.
Henan University · CNZhengzhou University · CNHebrew Hospital · USXinyang Central Hospital · CNXuzhou Central Hospital · CN

Funding

Henan Provincial Key Programs in Science and Technology 182102311241Henan Provincial Key Programs in Science and Technology 202102310355Joint Program of Medical Science and Technology Research of Henan Province LHGJ20190611Joint Program of Medical Science and Technology Research of Henan Province SB201903018
6 · The paper itself

Abstract

backgroundLimited research has been conducted on early laboratory biomarkers to identify patients with severe coronavirus disease (COVID-19). This study fills this gap to ensure appropriate treatment delivery and optimal resource utilization.

methodsIn this retrospective, multicentre, cohort study, 52 and 64 participants with severe and mild cases of COVID-19, respectively, were enrolled during January-March 2020. Least absolute shrinkage and selection operator and binary forward stepwise logistic regression were used to construct a predictive risk score. A prediction model was then developed and verified using data from four hospitals.

resultsOf the 50 variables assessed, eight were independent predictors of COVID-19 and used to calculate risk scores for severe COVID-19: age (odds ratio (OR = 14.01, 95% confidence interval (CI) 2.1-22.7), number of comorbidities (OR = 7.8, 95% CI 1.4-15.5), abnormal bilateral chest computed tomography images (OR = 8.5, 95% CI 4.5-10), neutrophil count (OR = 10.1, 95% CI 1.88-21.1), lactate dehydrogenase (OR = 4.6, 95% CI 1.2-19.2), C-reactive protein OR = 16.7, 95% CI 2.9-18.9), haemoglobin (OR = 16.8, 95% CI 2.4-19.1) and D-dimer levels (OR = 5.2, 95% CI 1.2-23.1). The model was effective, with an area under the receiver-operating characteristic curve of 0.944 (95% CI 0.89-0.99, p < 0.001) in the derived cohort and 0.8152 (95% CI 0.803-0.97; p < 0.001) in the validation cohort.

conclusionPredictors based on the characteristics of patients with COVID-19 at hospital admission may help predict the risk of subsequent critical illness.

Indexed as

AdultAgedAged, 80 and overBiomarkersCOVID-19Critical IllnessFemaleHospitalizationHumansMaleMiddle AgedRetrospective StudiesRisk FactorsROC CurveYoung AdultBiomarkerscohort studyCOVID-19prediction modelpredictor

Identifiers

PMID34951061
PMCPMC8841178
OpenAlexW4200167002

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.