Evidence map›Paper›PMID 33045571›Full record

ArticleInternational immunopharmacology2020

Novel serological biomarkers for inflammation in predicting disease severity in patients with COVID-19.

Guohui Xue, Xing Gan, Zhiqiang Wu, Dan Xie, Yan Xiong, Lin Hua, Bing Zhou, Nanjin Zhou, Jie Xiang, Junming Li

Abstract read
In one paragraph

Article in International immunopharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 10 pooled it
–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

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

64 citing papers in PubMed, 10 syntheses or guidelines pooled it.

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  6. Clinical significance of prognostic nutrition index in hospitalized patients with COVID-19: Results from single-center experience with systematic review and meta-analysis.Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition · 2021
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  10. Hemostatic System (Fibrinogen Level, D-Dimer, and FDP) in Severe and Non-Severe Patients With COVID-19: A Systematic Review and Meta-Analysis.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
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4 more citing papers are in PubMed but not listed here.

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.

Guohui XueDepartment of Clinical Laboratory, The First Affiliated Hospital of Nanchang University, Nanchang 330000, China.
Xing GanDepartment of Clinical Laboratory, Wuhan Jinyintan Hospital, Wuhan 430000, China.
Zhiqiang WuDepartment of Clinical Laboratory, Wuhan Jinyintan Hospital, Wuhan 430000, China.
Dan XieDepartment of Infectious Diseases, Wuhan Jinyintan Hospital, Wuhan 430000, China.
Yan XiongDepartment of Clinical Laboratory, Wuhan Jinyintan Hospital, Wuhan 430000, China.
Lin HuaDepartment of Clinical Laboratory, Jiujiang First People's Hospital, Jiujiang 332000, China.
Bing ZhouDepartment of Pathology, Jiujiang First People's Hospital, Jiujiang 332000, China.
Nanjin ZhouBasic Medical College, Nanchang University, Nanchang 330000, China.
Jie XiangDepartment of Clinical Laboratory, Wuhan Jinyintan Hospital, Wuhan 430000, China. Electronic address: 641931012@qq.com.
Junming LiDepartment of Clinical Laboratory, The First Affiliated Hospital of Nanchang University, Nanchang 330000, China. Electronic address: lisir361@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients with severe coronavirus disease 2019 (COVID-19) develop acute respiratory distress and multi-system organ failure and are associated with poor prognosis and high mortality. Thus, there is an urgent need to identify early diagnostic and prognostic biomarkers to determine the risk of developing serious illness.

methodsWe retrospectively analyzed 114 patients with COVID-19 at the Jinyintan Hospital, Wuhan based on their clinical and laboratory data. Patients were categorized into severe and mild to moderate disease groups. We analyzed the potential of serological inflammation indicators in predicting the severity of COVID-19 in patients using univariate and multivariate logistic regression, receiver operating characteristic curves, and nomogram analysis. The Spearman method was used to understand the correlation between the serological biomarkers and duration of hospital stay.

resultsPatients with severe disease had reduced neutrophils and lymphocytes; severe coagulation dysfunction; altered content of biochemical factors (such as urea, lactate dehydrogenase); elevated high sensitivity C-reactive protein levels, neutrophil-lymphocyte, platelet-lymphocyte, and derived neutrophil-lymphocyte ratios, high sensitivity C-reactive protein-prealbumin ratio (HsCPAR), systemic immune-inflammation index, and high sensitivity C-reactive protein-albumin ratio (HsCAR); and low lymphocyte-monocyte ratio, prognostic nutritional index (PNI), and albumin-to-fibrinogen ratio. PNI, HsCAR, and HsCPAR correlated with the risk of severe disease. The nomogram combining the three parameters showed good discrimination with a C-index of 0.873 and reliable calibration. Moreover, HsCAR and HsCPAR correlated with duration of hospital stay.

conclusionTaken together, PNI, HsCAR, and HsCPAR may serve as accurate biomarkers for the prediction of disease severity in patients with COVID-19 upon admission/hospitalization.

Indexed as

Length of StaySARS-CoV-2Severity of Illness IndexAgedBiomarkersCOVID-19C-Reactive ProteinFemaleHumansInflammationLogistic ModelsMaleMiddle AgedRetrospective StudiesSerum AlbuminBiomarkersC-Reactive ProteinSerum AlbuminCOVID-19High sensitivity C-reactive protein-albumin ratioHigh sensitivity C-reactive protein-prealbumin ratioNomogramPrognostic nutritional indexSeverity

Identifiers

PMID33045571
PMCPMC7532789

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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.