Evidence map›Paper›PMID 41225157›Full record

ArticleNPJ digital medicine2025

SPEED-TR: a self-distilled and pre-trained transformer model for enhanced ECG detection of tricuspid regurgitation.

Xiaolin Diao, Wei Xu, Huaibing Cheng, Ya Zhou, Yang Liu, Yanni Huo, Jianli Lu, Jinghan Huang, Jia He, Fang Liu and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

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

2 citing papers in PubMed.

  1. Article
  2. 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

14 authors.

Xiaolin Diao *Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Wei Xu *Department of Cardiology, Cardiac Arrhythmia Center, Fuwai Hospital, National Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Huaibing Cheng *Function Test Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Ya Zhou *Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yang LiuDepartment of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yanni HuoDepartment of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jianli LuDepartment of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jinghan HuangFunction Test Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jia HeFunction Test Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Fang LiuFunction Test Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zhihui CaoDepartment of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xue ZhangDepartment of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Wei ZhaoCenter for Health Statistics and Information, National Health Commission People's Republic of China, Beijing, China. zhaowei@nhc.gov.cn.
Xiaohan FanDepartment of Cardiology, Cardiac Arrhythmia Center, Fuwai Hospital, National Center for Cardiovascular Diseases, State Key Laboratory of Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. fanxiaohan@fuwaihospital.org.

Funding

Postdoctoral Fellowship Program of the China Postdoctoral Science Foundation GZC20240151The National High Level Hospital Clinical Research Funding 2022-GSP-GG-014
6 · The paper itself

Abstract

Tricuspid regurgitation (TR) remains underdiagnosed due to the lack of effective screening tools. We developed a self-distilled and pre-trained transformer model for detecting TR (SPEED-TR) from electrocardiography. The model was trained using 466,149 electrocardiogram-echocardiogram pairs from 291,673 patients and validated in one internal (63,925 patients) and two external cohorts (44,951 and 21,300 patients). SPEED-TR accurately detected moderate-to-severe TR in the hold-out set (AUROC 0.945, NPV 0.983; specificity 0.973) and maintained stable performance in multi-center testing sets (AUROCs 0.939-0.943; NPVs 0.978-0.988). Three thresholds enabled SPEED-TR severity grading: none (0-0.008), mild (0.008-0.255), moderate (0.255-0.755), and severe (0.755-1), achieving accuracies of 0.749 (hold-out), 0.730 (internal), 0.775 and 0.726 (external), with overall accuracy of 0.744. SPEED-TR remained robust in patients with 1 to ≥3 risk factors and 1 to ≥2 valvular diseases. SPEED-TR demonstrated potential as a screening tool and may provide reference for TR severity assessment.

Identifiers

PMID41225157
PMCPMC12612177

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