Evidence map›Paper›PMID 39869394›Full record

ArticleInternational journal of surgery (London, England)2025

Clinical value of aortic arch morphology in transfemoral TAVR: artificial intelligence evaluation.

Yu Mao, Yang Liu, Mengen Zhai, Ping Jin, Fangyao Chen, Yuhui Yang, Guangyu Zhu, Tingting Yang, Gejun Zhang, Kai Xu and 16 more

Registry-linked trialAbstract read
In one paragraph

Article in International journal of surgery (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07320274 (ArcFlex Sheath in TAVR Trial), which is not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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.

NCT07320274 nanot yet recruitingnot on this mapstarted 2026, after this paper: background citation

ArcFlex Sheath in TAVR Trial(Arc-FIT)

TypeinterventionalSponsorSun Yat-sen UniversityRan2026 to 2030Enrolled180ConditionsValve Disease, AorticArmsArcFlex Sheath, ordinary sheath
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

26 authors.

Yu MaoDepartment of Cardiovascular Surgery, Xijing Hospital, Xi'an, Shaanxi, China.
Yang LiuDepartment of Cardiovascular Surgery, Xijing Hospital, Xi'an, Shaanxi, China.
Mengen ZhaiDepartment of Cardiovascular Surgery, Xijing Hospital, Xi'an, Shaanxi, China.
Ping JinDepartment of Cardiovascular Surgery, Xijing Hospital, Xi'an, Shaanxi, China.
Fangyao ChenDepartment of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China.
Yuhui YangDepartment of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China.
Guangyu ZhuSchool of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Tingting YangSchool of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Gejun ZhangDepartment of Cardiovascular Surgery, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Kai XuDepartment of Cardiovascular Surgery, Northern Theater General Hospital, Shenyang, Liaoning, China.
Xiaoke ShangDepartment of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Yuan ZhaoDepartment of Cardiac Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Buqing NiDepartment of Cardiovascular Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Hongxin LiDepartment of Cardiovascular Surgery, Shandong Provincial Hospital, Jinan, Shandong, China.
Min TangDepartment of Cardiovascular Surgery, Xinhua Hospital affiliated to Shanghai Jiaotong University, Shanghai, China.
Zhao JianDepartment of Cardiovascular Surgery, Xinqiao Hospital, Chongqing, China.
Yining YangHeart Center, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China.
Haibo ZhangDepartment of Cardiovascular Surgery, Anzhen Hospital, Capital Medical University, Beijing, China.
Lai WeiDepartment of Cardiovascular Surgery, Shanghai Cardiovascular Institution and Zhongshan Hospital, Fudan University, Shanghai, China.
Jian LiuGuangdong Provincial People's Hospital, Guangzhou, Guangdong, China.
Timothée NoterdaemeDepartment of cardiovascular surgery, German Heart Center Munich, Munich, Germany.
Ruediger LangeDépartement of Cardiology, Boulevard Patience et Beaujonc, Liège, Belgium.
Yingqiang GuoDepartment of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Xiangbin PanDepartment of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Yongjian WuDepartment of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Jian YangDepartment of Cardiovascular Surgery, Xijing Hospital, Xi'an, Shaanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe impact of aortic arch (AA) morphology on the management of the procedural details and the clinical outcomes of the transfemoral artery (TF)-transcatheter aortic valve replacement (TAVR) has not been evaluated. The goal of this study was to evaluate the AA morphology of patients who had TF-TAVR using an artificial intelligence algorithm and then to evaluate its predictive value for clinical outcomes. MATERIALS AND

methodsA total of 1480 consecutive patients undergoing TF-TAVR using a new-generation transcatheter heart valve at 12 institutes were included in this retrospective study. The AA measurements were evaluated by deep learning, and then the approach index (I A ) was determined. The machine learning algorithm was used to construct the predictive model and was validated externally.

resultsThe area under the curve of the I A model using random forest and logistic regression was 0.675 [95% confidence interval (CI): 0.586-0.764] and 0.757 (95% CI: 0.665-0.849), respectively. The I A model was validated externally, and consistent distinctions were obtained. After we used a generalized propensity score matching method for continuous exposure, the I A was the strongest correlation factor for major procedural events (odds ratio: 3.87; 95% CI: 2.13-7.59, P < 0.001). When leaflet morphology or transcatheter heart valve type was an interactive item with I A , neither of them was statistically significant in terms of clinical outcomes.

conclusionI A may be used to identify the impact of AA morphology on procedural and clinical outcomes in patients having TF-TAVR and to help to predict the procedural complications.

Indexed as

Aorta, ThoracicAortic Valve StenosisArtificial IntelligenceTranscatheter Aortic Valve ReplacementAgedAged, 80 and overFemaleFemoral ArteryHumansMaleRetrospective StudiesTreatment Outcome

Identifiers

PMID39869394
PMCPMC12372727

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