Evidence map›Paper›PMID 38255225›Full record

ArticleBiomedicines2024

Quantitative Computed Tomography Lung COVID Scores with Laboratory Markers: Utilization to Predict Rapid Progression and Monitor Longitudinal Changes in Patients with Coronavirus 2019 (COVID-19) Pneumonia.

Da Hyun Kang, Grace Hyun J Kim, Sa-Beom Park, Song-I Lee, Jeong Suk Koh, Matthew S Brown, Fereidoun Abtin, Michael F McNitt-Gray, Jonathan G Goldin, Jeong Seok Lee

Open access · goldAbstract read
In one paragraph

Article in Biomedicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
8.5field-weighted citation impact, top 2% 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

19 citing papers in PubMed, 21 citations in OpenAlex.

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  18. Investigation of Serum Endocan Levels in SARS-CoV-2 Patients.International journal of molecular sciences · 2024
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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 4 institutions in 2 countries.

Da Hyun KangDepartment of Internal Medicine, College of Medicine, Chungnam National University, Daejeon 35015, Republic of Korea.ORCID 0000-0002-3495-0931
Grace Hyun J KimDepartment of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA 90095, USA.
Sa-Beom ParkCenter of Biohealth Convergence and Open Sharing System, Hongik University, Seoul 04401, Republic of Korea.
Song-I LeeDepartment of Internal Medicine, College of Medicine, Chungnam National University, Daejeon 35015, Republic of Korea.ORCID 0000-0001-8372-4511
Jeong Suk KohDepartment of Internal Medicine, College of Medicine, Chungnam National University, Daejeon 35015, Republic of Korea.
Matthew S BrownDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA.
Fereidoun AbtinDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA.
Michael F McNitt-GrayDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA.ORCID 0000-0003-3004-4613
Jonathan G GoldinDepartment of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, CA 90024, USA.
Jeong Seok LeeGraduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
University of California, Los Angeles · USChungnam National University · KRHongik University · KRKorea Advanced Institute of Science and Technology · KR

Funding

Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and Technology NRF-2022R1C1C1007301Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea HR22C1734
6 · The paper itself

Abstract

Coronavirus disease 2019 (COVID-19), is an ongoing issue in certain populations, presenting rapidly worsening pneumonia and persistent symptoms. This study aimed to test the predictability of rapid progression using radiographic scores and laboratory markers and present longitudinal changes. This retrospective study included 218 COVID-19 pneumonia patients admitted at the Chungnam National University Hospital. Rapid progression was defined as respiratory failure requiring mechanical ventilation within one week of hospitalization. Quantitative COVID (QCOVID) scores were derived from high-resolution computed tomography (CT) analyses: (1) ground glass opacity (QGGO), (2) mixed diseases (QMD), and (3) consolidation (QCON), and the sum, quantitative total lung diseases (QTLD). Laboratory data, including inflammatory markers, were obtained from electronic medical records. Rapid progression was observed in 9.6% of patients. All QCOVID scores predicted rapid progression, with QMD showing the best predictability (AUC = 0.813). In multivariate analyses, the QMD score and interleukin(IL)-6 level were important predictors for rapid progression (AUC = 0.864). With >2 months follow-up CT, remained lung lesions were observed in 21 subjects, even after several weeks of negative reverse transcription polymerase chain reaction test. AI-driven quantitative CT scores in conjugation with laboratory markers can be useful in predicting the rapid progression and monitoring of COVID-19.

Indexed as

coronavirus disease 2019 (COVID-19)predictionquantitative computed tomography (CT) scorerapid progression

Identifiers

PMID38255225
PMCPMC10813449
OpenAlexW4390669554

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.