Evidence map›Paper›PMID 38252276›Full record

ArticleEuropean radiology2024

Scoring model based on cardiac CT and clinical factors to predict early good mitral valve repair in rheumatic mitral disease.

Maozhou Wang, Hongkai Zhang, Zhou Liu, Jie Han, Jing Liu, Nan Zhang, Shuang Li, Wenjie Tang, Peiyi Liu, Baiyu Tian and 7 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in European radiology, 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
4.4field-weighted citation impact, top 5% 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, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 2 institutions in 1 country.

Maozhou Wang *Department of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing, China.
Hongkai Zhang *Department of Radiology, Beijing Anzhen Hospital, Beijing Institute of Heart, Lung, and Vascular Diseases, Capital Medical University, Beijing, China.
Zhou Liu *Center for Clinical and Epidemiologic Research, Beijing An Zhen Hospital, Capital Medical University, Beijing, China.
Jie HanDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing, China.
Jing LiuCenter for Clinical and Epidemiologic Research, Beijing An Zhen Hospital, Capital Medical University, Beijing, China.
Nan ZhangDepartment of Radiology, Beijing Anzhen Hospital, Beijing Institute of Heart, Lung, and Vascular Diseases, Capital Medical University, Beijing, China.
Shuang LiDepartment of Radiology, Beijing Anzhen Hospital, Beijing Institute of Heart, Lung, and Vascular Diseases, Capital Medical University, Beijing, China.
Wenjie TangDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing, China.
Peiyi LiuDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing, China.
Baiyu TianDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing, China.
Tiange LuoDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing, China.
Jiangang WangDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing, China.
Xu MengDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing, China.
Hongyu YeDepartment of Cardiothoracic Surgery, Zhongshan City People's Hospital, Sun Wenzhong Road, Zhongshan, China. zsyehongyu@163.com.
Lei XuDepartment of Radiology, Beijing Anzhen Hospital, Beijing Institute of Heart, Lung, and Vascular Diseases, Capital Medical University, Beijing, China. leixu2001@hotmail.com.
Hongjia ZhangDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing, China. zhanghongjia722@ccmu.edu.cn.
Wenjian JiangDepartment of Cardiac Surgery, Beijing Anzhen Hospital, Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing, China. jiangwenjian@ccmu.edu.cn.ORCID http://orcid.org/0000-0003-3868-6573
Capital Medical University · CNZhongshan People's Hospital · CN

Funding

2022 China Association for Science and Technology Think Tank Youth Talent Plan KCZD202203Capital's Funds for Health Improvement and Research Foundation of China 2020-1-1052Major scientific and technological innovation research and development project of Beijing Anzhen Hospital affiliated to Capital Medical University and High end foreign experts introduction plan G2022001039Lthe National Natural Science Foundation of China 82170487the National Natural Science Foundation of China 82271986the National Natural Science Foundation of China U1908211
6 · The paper itself

Abstract

objectiveWe aimed to evaluate the mitral valve calcification and mitral structure detected by cardiac computed tomography (cardiac CT) and establish a scoring model based on cardiac CT and clinical factors to predict early good mitral valve repair (EGMR) and guide surgical strategy in rheumatic mitral disease (RMD). MATERIALS AND

methodsThis is a retrospective bi-center cohort study. Based on cardiac CT, mitral valve calcification and mitral structure in RMD were quantified and evaluated. The primary outcome was EGMR. A logical regression algorithm was applied to the scoring model.

resultsA total of 579 patients were enrolled in our study from January 1, 2019, to August 31, 2022. Of these, 443 had baseline cardiac CT scans of adequate quality. The calcification quality score, calcification and thinnest part of the anterior leaflet clean zone, and papillary muscle symmetry were the independent CT factors of EGMR. Coronary artery disease and pulmonary artery pressure were the independent clinical factors of EGMR. Based on the above six factors, a scoring model was established. Sensitivity = 95% and specificity = 95% were presented with a cutoff value of 0.85 and 0.30 respectively. The area under the receiver operating characteristic of external validation set was 0.84 (95% confidence interval [CI] 0.73-0.93).

conclusionsMitral valve repair is recommended when the scoring model value > 0.85 and mitral valve replacement is prior when the scoring model value < 0.30. This model could assist in guiding surgical strategies for RMD. CLINICAL RELEVANCE STATEMENT: The model established in this study can serve as a reference indicator for surgical repair in rheumatic mitral valve disease. KEY POINTS: • Cardiac CT can reflect the mitral structure in detail, especially for valve calcification. • A model based on cardiac CT and clinical factors for predicting early good mitral valve repair was established. • The developed model can help cardiac surgeons formulate appropriate surgical strategies.

Indexed as

Mitral ValveRheumatic Heart DiseaseTomography, X-Ray ComputedAdultCalcinosisCohort StudiesFemaleHumansMaleMiddle AgedMitral Valve InsufficiencyPredictive Value of TestsRetrospective StudiesCardiac computed tomographyEarly good mitral valve repairMitral valvePredict modelRheumatic heart disease

Identifiers

PMID38252276
OpenAlexW4391103281

What OpenQuestion holds

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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.