Evidence map›Paper›PMID 34564786›Full record

ArticleInsights into imaging2021

Computed tomography-based multiple body composition parameters predict outcomes in Crohn's disease.

Ziling Zhou, Ziman Xiong, Qingguo Xie, Peng Xiao, Qingpeng Zhang, Jian Gu, Jing Li, Daoyu Hu, Xuemei Hu, Yaqi Shen and 1 more

Open access · goldAbstract read
In one paragraph

Article in Insights into imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
2.9field-weighted citation impact, top 8% 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

21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 citations in OpenAlex.

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  18. Body Composition Predictors of Complicated Crohn's Disease.Digestive diseases (Basel, Switzerland) · 2023
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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

11 authors at 4 institutions in 2 countries.

Ziling ZhouDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei, China.
Ziman XiongDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei, China.
Qingguo XieBiomedical Engineering Department, College of Life Sciences and Technology, Huazhong University of Science and Technology, Wuhan, China.
Peng XiaoBiomedical Engineering Department, College of Life Sciences and Technology, Huazhong University of Science and Technology, Wuhan, China.
Qingpeng ZhangSchool of Data Science, City University of Hong Kong, Kowloon, Hong Kong, China.
Jian GuDepartment of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Jing LiBiomedical Engineering Department, College of Life Sciences and Technology, Huazhong University of Science and Technology, Wuhan, China.
Daoyu HuDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei, China.
Xuemei HuDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei, China.
Yaqi ShenDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei, China. yqshen@hust.edu.cn.ORCID http://orcid.org/0000-0003-0589-8975
Zhen LiDepartment of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei, China.
Tongji Hospital · CNHuazhong University of Science and Technology · CNCity University of Hong Kong · HKUnion Hospital · CN

Funding

National Natural Science Foundation of China 81571642National Natural Science Foundation of China 81771801National Natural Science Foundation of China 81801695National Natural Science Foundation of China 82071889National Natural Science Foundation of China 82071890
6 · The paper itself

Abstract

backgroundThe efficacy of computed tomography-based multiple body composition parameters in assessing disease behavior and prognosis has not been comprehensively evaluated in Crohn's disease. This study aimed to assess the association of body composition parameters with disease behavior and outcomes in Crohn's disease and to compare the efficacies of indexes derived from body and lumbar spinal heights in body composition analysis.

resultsOne hundred twenty-two patients with confirmed Crohn's disease diagnoses and abdominal computed tomography scans were retrospectively included in this study. Skeletal muscle, visceral, and subcutaneous fat indexes were calculated by dividing each type of tissue area by height

conclusionsSkeletal muscle and adipose tissue principle component were associated with complicated Crohn's disease behavior and adverse outcome, respectively. Indexes derived from body and lumbar spinal heights have similar efficacies in body composition analysis.

Indexed as

Body composition parameterComputed tomographyCrohn’s disease

Identifiers

PMID34564786
PMCPMC8464641
OpenAlexW3203170371

What OpenQuestion holds

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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.