Evidence map›Paper›PMID 41692786›Full record

ArticleJournal of cardiovascular imaging2026

Detection and prognostic stratification of left ventricular systolic dysfunction in left bundle branch block using an artificial intelligence-enabled electrocardiography.

Soo Youn Lee, Ah-Hyun Yoo, Sora Kang, Jong-Hwan Jang, Yong-Yeon Jo, Jeong Min Son, Min Sung Lee, Ga In Han, Joon-Myoung Kwon, Hak Seung Lee and 1 more

Abstract read
In one paragraph

Article in Journal of cardiovascular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Soo Youn Lee *Division of Cardiology, Department of Internal Medicine, Incheon Sejong Hospital, Cardiovascular Center, Incheon, Republic of Korea.
Ah-Hyun Yoo *Digital Healthcare Institute, Sejong Medical Research Institute, Bucheon, Republic of Korea.
Sora KangDigital Healthcare Institute, Sejong Medical Research Institute, Bucheon, Republic of Korea.
Jong-Hwan JangMedical AI Co Ltd, Seoul, Republic of Korea.
Yong-Yeon JoMedical AI Co Ltd, Seoul, Republic of Korea.
Jeong Min SonDigital Healthcare Institute, Sejong Medical Research Institute, Bucheon, Republic of Korea.
Min Sung LeeDigital Healthcare Institute, Sejong Medical Research Institute, Bucheon, Republic of Korea.
Ga In HanDigital Healthcare Institute, Sejong Medical Research Institute, Bucheon, Republic of Korea.
Joon-Myoung KwonDigital Healthcare Institute, Sejong Medical Research Institute, Bucheon, Republic of Korea.
Hak Seung LeeDigital Healthcare Institute, Sejong Medical Research Institute, Bucheon, Republic of Korea. cardiolee@gmail.com.
Kyung-Hee KimDivision of Cardiology, Department of Internal Medicine, Incheon Sejong Hospital, Cardiovascular Center, Incheon, Republic of Korea. learnbyliving9@gmail.com.

Funding

The Korean Society of Echocardiography KSER-202001
6 · The paper itself

Abstract

backgroundLeft bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchrony. Although artificial intelligence-enabled electrocardiography (AI-ECG) models show promise in detecting LVSD, their performance in LBBB patients remains underexplored. We hypothesized that an AI-ECG model clinically validated for detecting LVSD would accurately detect LVSD and predict future clinical outcomes in LBBB patients.

methodsIn this retrospective multicenter study, 5,689 expert-validated LBBB ECGs collected from 2,813 patients between 2016 and 2024 were analyzed using a previously developed and validated AI-ECG model. LVSD was defined as an ejection fraction of ≤ 40%. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), sensitivity, and specificity. Patients were stratified into high- and low-risk groups based on a threshold that achieved 90% sensitivity. A Kaplan-Meier analysis was used to compare clinical outcomes.

resultsAmong the 2,813 LBBB patients (mean age, 70.7 years; male sex, 43.7%), hypertension and a history of heart failure were common. The AiTiALVSD model showed strong diagnostic performance for LVSD (AUROC, 0.930 [95% CI, 0.924-0.937]; AUPRC, 0.913 [95% CI, 0.902-0.923]; sensitivity, 0.979; specificity, 0.473). During the mean follow-up of 4.1 years, high-risk patients had significantly higher hazards than low-risk patients for all-cause mortality (adjusted hazard ratio [HR], 1.87; 95% CI, 1.53-2.28), implantable cardioverter defibrillator/cardiac resynchronization therapy implantation (adjusted HR, 15.2; 95% CI, 7.51-30.77), and cardiovascular hospitalization (adjusted HR, 1.11; 95% CI, 0.96-1.28).

conclusionsAiTiALVSD effectively detects LVSD and stratifies long-term cardiovascular risk in LBBB patients, supporting its clinical utility for early detection and patient management.

Indexed as

Artificial intelligenceArtificial intelligence–enabled electrocardiographyEchocardiographyElectrocardiographyLeft ventricular systolic dysfunction

Identifiers

PMID41692786
PMCPMC12914959

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.