Evidence map›Paper›PMID 38435896›Full record

ArticleCureus2024

Lung Involvement Patterns in COVID-19: CT Scan Insights and Prognostic Implications From a Tertiary Care Center in Southern India.

Suhasini Balasubramaniam, Bharathi Priya Raju, Sowmya Perumpallipatty Kumarasamy, Swaminathan Ramasubramanian, Amitesh Krishna Srinivasan, Ishwar Gopinath, Kamakshi Shanmugam, Aravind S Kumar, Varun Visakan Sivasakthi, Srinidhi Srinivasan

Open access · diamondAbstract read
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.3field-weighted citation impact, top 13% 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

8 citing papers in PubMed, 6 citations in OpenAlex.

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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

10 authors at 2 institutions in 2 countries.

Suhasini BalasubramaniamRadiodiagnosis, Government Stanley Medical College and Hospital, Chennai, IND.
Bharathi Priya RajuRadiodiagnosis, Government Stanley Medical College and Hospital, Chennai, IND.
Sowmya Perumpallipatty KumarasamyRadiodiagnosis, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, IND.
Swaminathan RamasubramanianRadiodiagnosis, Government Medical College, Omandurar Government Estate, Chennai, IND.
Amitesh Krishna SrinivasanInternal Medicine, Government Medical College, Omandurar Government Estate, Chennai, IND.
Ishwar GopinathRadiodiagnosis, Government Medical College, Omandurar Government Estate, Chennai, IND.
Kamakshi ShanmugamRadiodiagnosis, Government Medical College, Omandurar Government Estate, Chennai, IND.
Aravind S KumarRadiodiagnosis, Government Medical College, Omandurar Government Estate, Chennai, IND.
Varun Visakan SivasakthiOrthopaedics, Kovai Medical Centre and Hospital Institute of Health Sciences and Research, Coimbatore, IND.
Srinidhi SrinivasanRadiodiagnosis, Alluri Sitarama Raju Academy of Medical Sciences College and Hospital, Eluru, IND.
Academy of Medical Sciences · GBGovernment Medical College · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background COVID-19, caused by the SARS-CoV-2 virus, has presented an unparalleled challenge and a profound learning curve globally. Among the myriad of investigative tools, CT scans of the chest have become instrumental in assessing the magnitude of lung involvement in the pathogenesis of this disease. Objectives This study aimed to evaluate the distribution and patterns of lung involvement depicted in the CT chest scans of COVID-19 patients admitted to a specialized tertiary care center located in a southern state of India. Methods With clearance secured from the Institutional Ethics Committee, an analytical cross-sectional study was conducted. It encompassed CT chest images from all symptomatic COVID-19 patients within the designated study center during the specified study timeline. Subsequent data analysis ensued. Results Among the 1066 COVID-19 patients evaluated, ground-glass opacities (GGO) were the predominant lung involvement pattern. Distinct patterns, such as GGOs combined with solid consolidation or atelectasis, were noted, with the highest mortality linked to GGOs paired with pneumomediastinum (PM). Data underscored a direct correlation between the extent of lung involvement and patient prognosis, with specific lung regions, namely the right apical, right posterior, right superior basal, left superior lingular, and left inferior lingular segments, showing frequent involvement. Conclusion Amidst the pandemic, our study emphasizes that ground-glass opacities on CT scans are robust indicators of COVID-19 in RT-PCR-positive patients. Early identification can enhance patient management, with findings highlighting a strong link between lung involvement and prognosis. This insight aids in refining patient triage, while further research is warranted to delve deeper into variations in lung involvement and guide treatment advancements.

Indexed as

computed tomographycovid-19distributionlung involvementpatterns

Identifiers

PMID38435896
PMCPMC10907113
OpenAlexW4391729595

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LicenceCC BY
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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.