Evidence map›Paper›PMID 38351902›Full record

ArticleIJID regions2024

Lung abnormalities on computed tomography of Vietnamese patients with COVID-19 and the association with medical variables.

Cong Dien Trinh, Van Nam Le, Van Nguyen Bang Le, Ngoc Thach Pham, Van Duyet Le

Open access · goldAbstract read
In one paragraph

Article in IJID regions, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 20% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  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

5 authors at 3 institutions in 1 country.

Cong Dien TrinhDepartments of Infectious Disease, Military Hospital 103, Hanoi, Vietnam.
Van Nam LeDepartments of Infectious Disease, Military Hospital 103, Hanoi, Vietnam.
Van Nguyen Bang LeLuong The Vinh High School, Hanoi, Vietnam.
Ngoc Thach PhamMicobiology and Molecular Biology Department, National Hospital for Tropical Diseases, Hanoi, Vietnam.
Van Duyet LeMicobiology and Molecular Biology Department, National Hospital for Tropical Diseases, Hanoi, Vietnam.
108 Military Central Hospital · VNVietnam Military Medical University · VNHospital for Tropical Diseases · VN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Patients with COVID-19 may experience a lung injury without presenting clinical symptoms. Early detection of lung injury in patients with COVID-19 is required to enhance prediction and prevent severe progression. Methods: Lung lesions in patients with COVID-19 were defined using the Fleischner Society terminology. Chest computed tomography lesions and their correlation with demographic characteristics and medical variables were identified. Results: Patients with mild and moderate COVID-19 had up to 45% lung injuries, whereas critical patients had 55%. However, patients with mild and moderate COVID-19 typically had low-level lung injuries. Ground-glass (68.1%), consolidation (48.8%), opacity (36.3%), and nodular (6.9%) lung lesions were the most prevalent in patients with COVID-19. Patients with COVID-19 infected with the Delta variant had worse lung injury than those infected with the Alpha and Omicron. People vaccinated with ≥2 doses showed a lower risk of lung injury than those vaccinated with <1 dose. Patients <18 years old were less likely to have a lung injury than patients >18 years old. The treatment outcomes were unaffected by the severity of the lung injury. Conclusion: Patients with mild COVID-19 had a similar risk of lung injury as patients with severe COVID-19. Thus, using chest computed tomography to detect lung injury can enhance the treatment outcomes and reduce the patient's risk of pulmonary complications.

Indexed as

COVID-19CT scanLung CT abnormalityLung injuryLung involvement

Identifiers

PMID38351902
PMCPMC10862005
OpenAlexW4391027018

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.