Evidence map›Paper›PMID 35488261›Full record

Observational studyRespiratory research2022

Usage of compromised lung volume in monitoring steroid therapy on severe COVID-19.

Ying Su, Ze-Song Qiu, Jun Chen, Min-Jie Ju, Guo-Guang Ma, Jin-Wei He, Shen-Ji Yu, Kai Liu, Fleming Y M Lure, Guo-Wei Tu and 2 more

Registry-linked trialOpen access · goldAbstract readObservational Study
In one paragraph

Observational study in Respiratory research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04953247 (Use of Compromised Lung Volume in Monitoring Usage of Steroid Therapy on Severe COVID-19), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 59% 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.

NCT04953247 completednot on this map

Use of Compromised Lung Volume in Monitoring Usage of Steroid Therapy on Severe COVID-19

TypeobservationalSponsorGuowei TuRan2020 to 2021Enrolled72ConditionsCOVID-19 Pneumonia, Quantitative Computed Tomography
3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Data science in the intensive care unit.World journal of critical care medicine · 2022
    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

12 authors at 7 institutions in 2 countries.

Ying Su *Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Ze-Song Qiu *School of Information Science and Technology, ShanghaiTech University, Shanghai, China.
Jun Chen *Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
Min-Jie JuDepartment of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Guo-Guang MaDepartment of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Jin-Wei HeSchool of Information Science and Technology, ShanghaiTech University, Shanghai, China.
Shen-Ji YuDepartment of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Kai LiuDepartment of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
Fleming Y M LureMS Technologies, Rockville, MD, USA.
Guo-Wei TuDepartment of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China. tu.guowei@zs-hospital.sh.cn.
Yu-Yao ZhangSchool of Information Science and Technology, ShanghaiTech University, Shanghai, China. zhangyy8@shanghaitech.edu.cn.
Zhe LuoDepartment of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China. luo.zhe@zs-hospital.sh.cn.
ShanghaiTech University · CNFudan University · CNSun Yat-sen University · CNThe First Affiliated Hospital, Sun Yat-sen University · CNRenmin Hospital of Wuhan University · CNSA Technologies (United States) · USShanghai University · CN

Funding

National Natural Science Foundation of China 82070085National Natural Science Foundation of China 82072131Natural Science Foundation of Shanghai 20ZR1411100Natural Science Foundation of Shanghai 21ZR1412900
6 · The paper itself

Abstract

backgroundQuantitative computed tomography (QCT) analysis may serve as a tool for assessing the severity of coronavirus disease 2019 (COVID-19) and for monitoring its progress. The present study aimed to assess the association between steroid therapy and quantitative CT parameters in a longitudinal cohort with COVID-19.

methodsBetween February 7 and February 17, 2020, 72 patients with severe COVID-19 were retrospectively enrolled. All 300 chest CT scans from these patients were collected and classified into five stages according to the interval between hospital admission and follow-up CT scans: Stage 1 (at admission); Stage 2 (3-7 days); Stage 3 (8-14 days); Stage 4 (15-21 days); and Stage 5 (22-31 days). QCT was performed using a threshold-based quantitative analysis to segment the lung according to different Hounsfield unit (HU) intervals. The primary outcomes were changes in percentage of compromised lung volume (%CL, - 500 to 100 HU) at different stages. Multivariate Generalized Estimating Equations were performed after adjusting for potential confounders.

resultsOf 72 patients, 31 patients (43.1%) received steroid therapy. Steroid therapy was associated with a decrease in %CL (- 3.27% [95% CI, - 5.86 to - 0.68, P = 0.01]) after adjusting for duration and baseline %CL. Associations between steroid therapy and changes in %CL varied between different stages or baseline %CL (all interactions, P < 0.01). Steroid therapy was associated with decrease in %CL after stage 3 (all P < 0.05), but not at stage 2. Similarly, steroid therapy was associated with a more significant decrease in %CL in the high CL group (P < 0.05), but not in the low CL group.

conclusionsSteroid administration was independently associated with a decrease in %CL, with interaction by duration or disease severity in a longitudinal cohort. The quantitative CT parameters, particularly compromised lung volume, may provide a useful tool to monitor COVID-19 progression during the treatment process. Trial registration Clinicaltrials.gov, NCT04953247. Registered July 7, 2021, https://clinicaltrials.gov/ct2/show/NCT04953247.

Indexed as

COVID-19 Drug TreatmentHumansLungLung Volume MeasurementsRetrospective StudiesSteroidsSteroidsCompromised lung volumeCOVID-19Quantitative computed tomographySteroid

Identifiers

PMID35488261
PMCPMC9051749
OpenAlexW4225247802

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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.