Evidence map›Paper›PMID 41340036›Full record

ArticleBMC cardiovascular disorders2025

Artificial intelligence-enhanced three-dimensional echocardiography reveals left atrial-ventricular coupling index as a novel prognostic marker in coronary artery disease.

Siwen Qin, Difei Li, Qin Feng, Yanchao Zhang, Donghui Zhou, Shuang Liu

Abstract read
In one paragraph

Article in BMC cardiovascular disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

6 authors.

Siwen Qin *Department of Ultrasound, The Fourth Affiliated Hospital of China Medical University, Shenyang, 110000, People's Republic of China.
Difei Li *Department of Ultrasound, The Fourth Affiliated Hospital of China Medical University, Shenyang, 110000, People's Republic of China.
Qin FengDepartment of Ultrasound, The Fourth Affiliated Hospital of China Medical University, Shenyang, 110000, People's Republic of China.
Yanchao ZhangDepartment of Ultrasound, The Fourth Affiliated Hospital of China Medical University, Shenyang, 110000, People's Republic of China.
Donghui ZhouDepartment of Cardiovascular Medicine, The Fourth Affiliated Hospital of China Medical University, Shenyang, 110000, People's Republic of China. cmu4h-zdh@126.com.
Shuang LiuDepartment of Ultrasound, The Fourth Affiliated Hospital of China Medical University, Shenyang, 110000, People's Republic of China. liushuang@cmu.edu.cn.

Funding

the Department of Science and Technology of Liaoning Province 2024JH2/102500029the National Natural Science Foundation of China 62171472
6 · The paper itself

Abstract

backgroundThe Left Atrial-Ventricular Coupling Index (LACI), which quantifies the volumetric interaction between the left atrium and ventricle, has emerged as a promising prognostic marker for cardiovascular events.

methodsWe applied three-dimensional echocardiography (3DE) and AI quantitative software to automatically obtain the new echocardiographic parameter LACI in 1084 coronary artery disease (CAD) patients at the Fourth Affiliated Hospital of China Medical University. LACI was defined as the ratio between the left atrial end-diastolic volume and the left ventricular end-diastolic volume. The primary endpoints were major adverse cardiovascular events (MACE), including unstable angina, myocardial reinfarction, heart failure (HF) and death.

resultsOver a median follow-up of 13 months, 216 MACE were recorded. LACI was significantly higher in patients with MACE than in those without (0.22 [IQR 0.17–0.28] vs. 0.20 [IQR 0.15–0.25], p < 0.001). ROC analysis indicated that LACI demonstrated higher predictive accuracy for MACE than traditional echocardiography parameters. Multivariable Cox regression analysis revealed that LACI was significantly correlated with MACE (HR: 1.68, 95% CI [1.25–2.27], p < 0.001), especially in patients with severe lesion subgroups according to the Gensini scores (HR 1.33, 95% CI [1.17–1.52], p < 0.002) and male (HR: 1.37, 95% CI [1.20–1.56], p < 0.002). Furthermore, LACI assessment enabled further risk stratification in high-risk patients with severe lesion (p < 0.001 on log-rank testing). Consistently, category-free NRI and IDI confirmed the improvement by LACI to stratify MACE risk in severe lesion patients (p < 0.05).

conclusionOur findings demonstrated that AI-enhanced 3DE-derived LACI provided incremental prognostic value beyond conventional echocardiographic parameters in assessing the prognosis among CAD patients, especially in severe coronary lesion subgroups.

Indexed as

Artificial IntelligenceAtrial Function, LeftCoronary Artery DiseaseEchocardiography, Three-DimensionalImage Interpretation, Computer-AssistedVentricular Function, LeftAgedFemaleHumansIntelligent SystemsMaleMiddle AgedPredictive Value of TestsPrognosisReproducibility of ResultsRisk AssessmentAI-enhanced 3D echocardiographyCardiovascular risk assessmentCoronary artery diseaseGensini scoresLeft atrial-ventricular coupling indexMajor adverse cardiovascular events

Identifiers

PMID41340036
PMCPMC12676787

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.