Evidence map›Paper›PMID 36944961›Full record

ArticleAnnals of clinical microbiology and antimicrobials2023

Impact of inactivated COVID-19 vaccines on lung injury in B.1.617.2 (Delta) variant-infected patients.

Miao Lai, Kai Wang, Chengyuan Ding, Yi Yin, Xiaoling Lin, Chuanjun Xu, Zhiliang Hu, Zhihang Peng

Open access · goldFull text read
In one paragraph

Article in Annals of clinical microbiology and antimicrobials, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 52% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

8 authors at 3 institutions in 1 country.

Miao Lai *School of Public Health, Nanjing Medical University, 101 Longmian Ave, Nanjing, 211166, China.
Kai Wang *The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, Jiangsu, China.
Chengyuan Ding *Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
Yi YinSchool of Public Health, Nanjing Medical University, 101 Longmian Ave, Nanjing, 211166, China.
Xiaoling LinCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
Chuanjun XuDepartment of Radiology, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, 210003, China. xchuanjun@163.com.
Zhiliang HuCenter for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China. huzhiliang@njucm.edu.cn.
Zhihang PengSchool of Public Health, Nanjing Medical University, 101 Longmian Ave, Nanjing, 211166, China. zhihangpeng@njmu.edu.cn.
Nanjing Medical University · CNJiangsu Province Hospital · CNNanjing University of Chinese Medicine · CN

Funding

Nanjing medical science and technique development foundation ZKX21037National S&T Major Project Foundation of China 2018ZX10715002the National Natural Science Foundation of China 82073673the Priority Academic Program Development of Jiangsu Higher Education Institutions PAPD
6 · The paper itself

Abstract

backgroundChest computerized tomography (CT) scan is an important strategy that quantifies the severity of COVID-19 pneumonia. To what extent inactivated COVID-19 vaccines could impact the COVID-19 pneumonia on chest CT is not clear.

methodsThis study recruited 357 SARS-COV-2 B.1.617.2 (Delta) variant-infected patients admitted to the Second Hospital of Nanjing from July to August 2021. An artificial intelligence-assisted CT imaging system was used to quantify the severity of COVID-19 pneumonia. We compared the volume of infection (VOI), percentage of infection (POI) and chest CT scores among patients with different vaccination statuses.

resultsOf the 357 Delta variant-infected patients included for analysis, 105 were unvaccinated, 72 were partially vaccinated and 180 were fully vaccinated. Fully vaccination had the least lung injuries when quantified by VOI (median VOI of 222.4 cm

conclusionFully vaccination but not partially vaccination could significantly protect lung injury manifested on chest CT. Our study provides additional evidence to encourage a full course of vaccination.

Indexed as

COVID-19COVID-19 VaccinesLung InjuryArtificial IntelligenceFemaleHumansMaleSARS-CoV-2COVID-19 VaccinesArtificial intelligence (AI)Chest CTCOVID-19COVID-19 vaccinesLung injury

Identifiers

PMID36944961
PMCPMC10029781
OpenAlexW4328130879

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read23
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.