Evidence map›Paper›PMID 36309538›Full record

ArticleScientific reports2022

A complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19: a retrospective study.

Yiting Wang, Xuewen Li, Jiancheng Xu, Qi Zhou

Abstract read
In one paragraph

Article in Scientific reports, 2022. 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
–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

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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.

  1. Article
  2. Review
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

4 authors.

Yiting WangDepartment of Laboratory Medicine, First Hospital of Jilin University, 1 Xinmin Street, Changchun, 130021, China.ORCID 0000-0001-5977-5407
Xuewen LiDepartment of Laboratory Medicine, First Hospital of Jilin University, 1 Xinmin Street, Changchun, 130021, China.ORCID 0000-0001-9880-571X
Jiancheng XuDepartment of Laboratory Medicine, First Hospital of Jilin University, 1 Xinmin Street, Changchun, 130021, China.ORCID 0000-0001-8796-271X
Qi ZhouDepartment of Pediatrics, First Hospital of Jilin University, 1 Xinmin Street, Changchun, 130021, China. zhou_qi@jlu.edu.cn.ORCID 0000-0002-5084-8697

Funding

Department of Science and Technology of Jilin Province (Jilin Province Science and Technology Department) 20190304110YYDepartment of Science and Technology of Jilin Province (Jilin Province Science and Technology Department) 20200404171YY
6 · The paper itself

Abstract

Many resource-limited countries need an efficient and convenient method to assess disease progression in patients with coronavirus disease 2019 (COVID-19). This study developed and validated a complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19. We collected the clinical data and laboratory test results of 86 patients with moderate COVID-19. These data were categorized into two subgroups depending on the laboratory test time. Univariate logistic regression and covariance diagnosis were used to screen for independent factors, and multifactorial logistic regression was used for model building. Data from 38 patients at another hospital were collected for external verification of the model. Basophils (OR 6.372; 95% CI 3.284-12.363), mean corpuscular volume (OR 1.244; 95% CI 1.088-1.422), red blood cell distribution width (OR 2.585; 95% CI 1.261-5.297), and platelet distribution width (OR 1.559; 95% CI 1.154-2.108) could be combined to predict recovery of patients with moderate COVID-19. The ROC curve showed that the model has good discrimination. The calibration curve showed that the model was well-fitted. The DCA showed that the model is clinically useful. Small increases in the above parameters within the normal range suggest an improvement in patients with moderate COVID-19.

Indexed as

COVID-19HumansLeukocyte CountPrognosisRetrospective StudiesROC CurveSARS-CoV-2

Identifiers

PMID36309538
PMCPMC9617603

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