Evidence map›Paper›PMID 34305939›Full record

ArticleFrontiers in immunology2021

Comparative Characterization and Risk Stratification of Asymptomatic and Presymptomatic Patients With COVID-19.

Lei Shi, Rong Ding, Tingting Zhang, Wei Wu, Ziyu Wang, Xiuzhi Jia, Kening Li, Yuan Liang, Jie Li, Mengyan Zhu and 13 more

Abstract readComparative Study
In one paragraph

Article in Frontiers in immunology, 2021. 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

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

2 citing papers in PubMed.

  1. Pattern recognition in SARS cases: insights fromFrontiers in artificial intelligence · 2025
    Article
  2. 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

23 authors.

Lei ShiAffiliated First People's Hospital of Kunshan, Gusu College of Nanjing Medical University, Suzhou, China.
Rong DingCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Tingting ZhangCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Wei WuCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Ziyu WangCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Xiuzhi JiaCentral Lab of Biomedical Research Center, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Kening LiCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Yuan LiangCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Jie LiCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Mengyan ZhuCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Bin HuangCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Lingxiang WuCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Min WuCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Jing ChenDepartment of Breast Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.
Chaochen WangDepartment of Breast Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.
Bin HuangThe Comprehensive Cancer Center of Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School & Clinical Cancer Institute of Nanjing University, Nanjing, China.
Caidong LiuDepartment of Laboratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Hongbing ShenCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Qianghu WangCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Xinyi XiaCOVID-19 Research Center, Institute of Laboratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing Clinical College of Southern Medical University, Nanjing, China.
Pengping LiCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Sali LyuCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Ying XiaoCentral Lab of Biomedical Research Center, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The identification of asymptomatic, non-severe presymptomatic, and severe presymptomatic coronavirus disease 2019 (COVID-19) in patients may help optimize risk-stratified clinical management and improve prognosis. This single-center case series from Wuhan Huoshenshan Hospital, China, included 2,980 patients with COVID-19 who were hospitalized between February 4, 2020 and April 10, 2020. Patients were diagnosed as asymptomatic (n = 39), presymptomatic (n = 34), and symptomatic (n = 2,907) upon admission. This study provided an overview of asymptomatic, presymptomatic, and symptomatic COVID-19 patients, including detection, demographics, clinical characteristics, and outcomes. Upon admission, there was no significant difference in clinical symptoms and CT image between asymptomatic and presymptomatic patients for diagnosis reference. The mean area under the receiver operating characteristic curve (AUC) of the differential diagnosis model to discriminate presymptomatic patients from asymptomatic patients was 0.89 (95% CI, 0.81-0.98). Importantly, the severe and non-severe presymptomatic patients can be further stratified (AUC = 0.82). In conclusion, the two-step risk-stratification model based on 10 laboratory indicators can distinguish among asymptomatic, severe presymptomatic, and non-severe presymptomatic COVID-19 patients on admission. Moreover, single-cell data analyses revealed that the CD8+T cell exhaustion correlated to the progression of COVID-19.

Indexed as

Asymptomatic InfectionsAgedCD8-Positive T-LymphocytesChinaCOVID-19Diagnosis, DifferentialDisease ProgressionFemaleHumansMaleMiddle AgedModels, StatisticalPrognosisRisk AssessmentSARS-CoV-2asymptomatic patientsCOVID-19presymptomatic patientsrisk stratificationT cell exhaustion

Identifiers

PMID34305939
PMCPMC8301218

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.