Evidence map›Paper›PMID 32885294›Full record

ArticleEuropean radiology2021

Evaluating the severity of aortic coarctation in infants using anatomic features measured on CTA.

Yiming Yu, Yubo Wang, Maoqing Yang, Meiping Huang, Jun Li, Qianjun Jia, Jian Zhuang, Liyu Huang

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Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 25% 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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 10 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Yiming Yu *School of Life Science and Technology, Xidian University, No. 2 Taibainan Road, Xi'an, 710071, Shaanxi, China.
Yubo Wang *School of Life Science and Technology, Xidian University, No. 2 Taibainan Road, Xi'an, 710071, Shaanxi, China.
Maoqing YangSchool of Life Science and Technology, Xidian University, No. 2 Taibainan Road, Xi'an, 710071, Shaanxi, China.
Meiping HuangDepartment of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong General Hospital), Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China.
Jun LiSchool of Life Science and Technology, Xidian University, No. 2 Taibainan Road, Xi'an, 710071, Shaanxi, China.
Qianjun JiaDepartment of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong General Hospital), Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China.
Jian ZhuangDepartment of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital (Guangdong General Hospital), Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China.
Liyu HuangSchool of Life Science and Technology, Xidian University, No. 2 Taibainan Road, Xi'an, 710071, Shaanxi, China. huangly@mail.xidian.edu.cn.ORCID http://orcid.org/0000-0001-6534-2712
Xidian University · CNGuangdong General Hospital · CN

Funding

Science and Technology Planning Project of Guangdong Province No. 2014A020212228the National Key Research and Development Program of China No. 2017YFA0205202the National Natural Science Foundation of China No. U1401255
6 · The paper itself

Abstract

objectivesA machine learning model was developed to evaluate the severity of aortic coarctation (CoA) in infants based on anatomical features measured on CTA.

methodsIn total, 239 infant patients undergoing both thorax CTA and echocardiography were retrospectively reviewed. The patients were assigned to either mild or severe CoA group based on their pressure gradient on echocardiography. They were further divided into patent ductus arteriosus (PDA) and non-PDA groups. The anatomical features were measured on double-oblique multiplanar reconstructed CTA images. Then, the optimal features were identified by using the Boruta algorithm. Subsequently, the coarctation severity was classified using linear discriminant analysis (LDA). We further investigated the relationship between the anatomical features and re-coarctation using Cox regression.

resultsFour anatomical features showed significant differences between the mild and severe CoA groups, including the smallest aortic cross-sectional area indexed to body surface area (p < 0.001), the narrowest aortic diameter (CoA diameter) indexed to height (p < 0.001), the diameter of the descending aorta at the diaphragmatic level (p < 0.001) and weight (p = 0.005). With these features, accuracy of 88.6% and 90.2%, sensitivity of 65.0% and 72.1%, and specificity of 92.9% and 100% were obtained for classifying the CoA severity in the non-PDA and PDA groups, respectively. Moreover, CoA diameter indexed to weight was associated with the risk of re-coarctation.

conclusionsCoA severity can be evaluated by using LDA with anatomical features. When quantifying the severity of CoA and risk of re-coarctation, both anatomical alternations at the CoA site and the growth of the patients need to be considered. KEY POINTS: • CTA is routinely ordered for infants with coarctation of the aorta; however, whether anatomical variations observed with CTA could be used to assess the severity of CoA remains unknown. • Using the diameter and area of the coarctation site adjusted to body growth as features, the LDA model achieved an accuracy of 88.6% and 90.2% in differentiating between the mild and severe CoA patients in the non-PDA group and PDA group, respectively. • The narrowest aortic diameter (CoA diameter) indexed to weight has a hazard ratio of 10.29 for re-coarctation.

Indexed as

Aortic CoarctationAortaEchocardiographyHumansInfantProportional Hazards ModelsRetrospective StudiesAortic coarctationComputed tomography angiographyMachine learningRe-coarctationRisk assessment

Identifiers

PMID32885294
OpenAlexW3083535948

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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.