Evidence map›Paper›PMID 33827266›Full record

ArticleAnnals of clinical biochemistry2021

A composite risk model predicts disease progression in early stages of COVID-19: A propensity score-matched cohort study.

Jianjun Xu, Yang Gao, Shaobo Hu, Suzhen Li, Weimin Wang, Yuzhe Wu, Zhe Su, Xing Zhou, Xiang Cheng, Qichang Zheng

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

Article in Annals of clinical biochemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
0.2field-weighted citation impact, top 47% 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, 2 syntheses or guidelines pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  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.

Jianjun XuDepartment of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yang GaoDepartment of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Shaobo HuDepartment of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Suzhen LiDepartment of Gastroenterology, Wuhan Asia General Hospital, Wuhan, China.
Weimin WangDepartment of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Yuzhe WuDepartment of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Zhe SuDepartment of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xing ZhouDepartment of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiang ChengDepartment of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Qichang ZhengDepartment of Hepatobiliary Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID 0000-0003-4570-0863
Union Hospital · CNWuhan General Hospital of Guangzhou · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecently, studies on COVID-19 have focused on the epidemiology of the disease and clinical characteristics of patients, as well as on the risk factors associated with mortality during hospitalization in critical COVID-19 cases. However, few research has been performed on the prediction of disease progression in particular group of patients in the early stages of COVID-19.

methodsThe study included 338 patients with COVID-19 treated at two hospitals in Wuhan, China, from December 2019 to March 2020. Predictors of the progression of COVID-19 from mild to severe stages were selected by the logistic regression analysis.

resultsCOVID-19 progression to severe and critical stages was confirmed in 78 (23.1%) patients. The average value of the neutrophil-to-lymphocyte ratio (NLR) was higher in patients in the disease progression group than in the improvement group. Multivariable logistic regression analysis revealed that elevated NLR, LDH and IL-10 were independent predictors of disease progression. The optimal cut-off value of NLR was 3.75. The values of the area under the curve, reflecting the accuracy of predicting COVID-19 progression by NLR was 0.739 (95%CI: 0.605-0.804). The risk model based on NLR, LDH and IL-10 had the highest area under the ROC curve.

conclusionsThe performed analysis demonstrates that high concentrations of NLR, LDH and IL-10 were independent risk factors for predicting disease progression in patients at the early stage of COVID-19. The risk model combined with NLR, LDH and IL-10 improved the accuracy of the prediction of disease progression in patients in the early stages of COVID-19.

Indexed as

PandemicsSARS-CoV-2AdultAgedChinaCohort StudiesCOVID-19Disease ProgressionFemaleHumansInterleukin-10L-Lactate DehydrogenaseLogistic ModelsLymphocytesMaleMiddle AgedIL10 protein, humanInterleukin-10L-Lactate DehydrogenaseCOVID-19neutrophil-to-lymphocyte ratiopredictorpropensity score matchingrisk modelSARS-CoV-2systemic immune-inflammation index

Identifiers

PMID33827266
PMCPMC8685757
OpenAlexW3151121887

What OpenQuestion holds

Textmetadata
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Registered trials

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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.