Evidence map›Paper›PMID 32779860›Full record

ArticleInternational journal of laboratory hematology2020

Clinical and hematological characteristics of 88 patients with COVID-19.

Hongmei Zhang, Xiaocui Cao, Man Kong, Xiaoli Mao, Lifeng Huang, Panwen He, Shiyao Pan, Jin Li, Zhongxin Lu

Abstract readComparative Study
In one paragraph

Article in International journal of laboratory hematology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 3 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

22 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
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  6. Review
  7. CBC Differences between Survived and Deceased COVID-19 Patients: A Cohort Study.Medical journal of the Islamic Republic of Iran · 2023
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  18. COVID-19, leprosy, and neutrophils.PLoS neglected tropical diseases · 2021
    Article
  19. Article
  20. Clinical and hematological characteristics of 88 patients with COVID-19.International journal of laboratory hematology · 2020
    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

9 authors.

Hongmei ZhangDepartment of Medical Laboratory, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiaocui CaoDepartment of Medical Laboratory, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Man KongDepartment of Medical Laboratory, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiaoli MaoDepartment of Medical Laboratory, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Lifeng HuangDepartment of Medical Laboratory, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Panwen HeDepartment of Medical Laboratory, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Shiyao PanHematology Application and Research Department, Shenzhen Mindray Bio-Medical Electronic Co., Ltd, Shenzhen, China.
Jin LiHematology Application and Research Department, Shenzhen Mindray Bio-Medical Electronic Co., Ltd, Shenzhen, China.
Zhongxin LuDepartment of Medical Laboratory, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID https://orcid.org/0000-0002-3365-0881

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTo retrospectively analyze epidemiological, clinical and hematological characteristics of COVID-19 patients.

methodsThe demographic, symptoms, and physiological parameters of 88 patients were collected and analyzed. The performance of complete blood count (CBC) indexes for monitoring and predicting the severity of COVID-19 in patients was evaluated by analyzing and comparing CBC results among different COVID-19 patient groups.

resultsWhite blood cells (WBCs), the neutrophil percentage (Neu%), absolute neutrophil count (Neu#), and neutrophil-to-lymphocyte ratio (NLR) were significantly higher in the critical group than in the other three groups (P < .05), while the lymphocyte percentage (Lym%), monocyte percentage (Mon%), lymphocyte count (Lym#), and lymphocyte-to-monocyte ratio (LMR) were significantly lower in the critical group than in the other three groups (P < .05). WBCs, the Neu%, Neu#, NLR, and neutrophil-to-monocyte ratio (NMR) were significantly higher in the severe group than in the mild and moderate groups (P < .05), while the Lym% was significantly lower in the severe group than in the mild and moderate groups (P < .05). The Mon%, Lym#, and LMR were significantly lower in the severe group than in the moderate group (P < .05). Using receiver operating characteristic (ROC) curve analysis to differentiate severe and nonsevere patients, the areas under the curve (AUCs) for the NLR, Neu%, and Lym% were 0.733, 0.732, and 0.730, respectively. When differentiating critical patients from noncritical patients, the AUCs for the NLR, Neu%, and Lym% were 0.832, 0.831, and 0.831.

conclusionsThe NLR is valuable for differentiating and predicting patients who will become critical within 4 weeks after the onset of COVID-19.

Indexed as

BetacoronavirusPandemicsAdultAgedAged, 80 and overArea Under CurveBlood Cell CountComorbidityCoronavirus InfectionsCOVID-19Diabetes MellitusFemaleHumansHypertensionMaleMiddle AgedBC-6800plusclinical characteristicsCOVID-19hematologyneutrophil-to-lymphocyte rationovel coronavirus (SARS-Cov-2)

Identifiers

PMID32779860
PMCPMC7405137

What OpenQuestion holds

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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.