Evidence map›Paper›PMID 34220307›Full record

SynthesisInternational journal of medical sciences2021

Multi-biomarker is an early-stage predictor for progression of Coronavirus disease 2019 (COVID-19) infection.

Zheng Zhou, Ying Li, Yuanhui Ma, Heng Zhang, Yunfeng Deng, Zuobin Zhu

Abstract readMeta-Analysis
In one paragraph

Synthesis in International journal of medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

6 authors.

Zheng ZhouKatharine Hsu International Research Institute of Infectious Disease, Shandong Provincial Public Health Clinical Center, Shandong University, Jinan 250013, China.
Ying LiMedical Technology School of Xuzhou Medical University, Xuzhou 221004, China.
Yuanhui MaDepartment of Pathology, Shandong Provincial Public Health Clinical Center, Shandong University, Jinan 250013, China.
Heng ZhangDepartment of Labor, Jining Psychiatric Hospital, Jining 272051, China.
Yunfeng DengKatharine Hsu International Research Institute of Infectious Disease, Shandong Provincial Public Health Clinical Center, Shandong University, Jinan 250013, China.
Zuobin ZhuDepartment of Genetics, Xuzhou Medical University, Xuzhou 221004, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19) has spread widely in the communities in many countries. Although most of the mild patients could be cured by their body's ability to self-heal, many patients quickly progressed to severe disease and had to undergo treatment in the intensive care unit (ICU). Thus, it is very important to effectively predict which patients with mild disease are more likely to progress to severe disease. A total of 72 patients hospitalized with COVID-19 in Shandong Provincial Public Health Clinical Center and 1141 patients included in the published papers were enrolled in this study. We determined that the combination of interleukin-6 (IL-6), Neutrophil (NEUT), and Natural Killer (NK) cells had the highest prediction accuracy (with 75% sensitivity and 95% specificity) for progression of COVID-19 infection. A binomial regression equation that accounted for a multiple risk score for the combination of IL-6, NEUT, and NK was also established. The multiple risk score is a good indicator for early stratification of mild patients into risk categories, which is very important for adjusting the treatment plan and preventing death.

Indexed as

AgedBiomarkersBlood Cell CountComorbidityCOVID-19Disease ProgressionHumansInterleukin-6Killer Cells, NaturalMiddle AgedNeutrophilsRetrospective StudiesBiomarkersIL6 protein, humanInterleukin-6COVID-19IL-6Multi-biomarkerpredictorsprogression

Identifiers

PMID34220307
PMCPMC8241766

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.