ArticleJACC. Clinical electrophysiology2025
Phenotyping of Mitral Valve Prolapse Without Severe Mitral Regurgitation Using Electrocardiographic and Echocardiographic Data.
Article in JACC. Clinical electrophysiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Utility of genetic screening for the prediction of severe arrhythmic outcomes in mitral valve prolapse.medRxiv : the preprint server for health sciences · 2026Article
- Automated echocardiographic detection of mitral valve prolapse and mitral regurgitation with video-based artificial intelligence algorithms.European heart journal. Digital health · 2026Article
- Automated Echocardiographic Detection of Mitral Valve Prolapse and Mitral Regurgitation with Video-based Artificial Intelligence Algorithms.medRxiv : the preprint server for health sciences · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundArrhythmic risk stratification in mitral valve prolapse (MVP) without significant mitral regurgitation (MR) remains elusive. Unsupervised machine learning may reveal phenotypic variation among arrhythmic MVP without severe MR.
objectivesIn this study, the authors hypothesized that hierarchical clustering of echocardiographic and 12-lead electrocardiographic (ECG) parameters alone could identify MVP phenotypes without severe MR associated with sustained ventricular arrhythmia and excess mortality.
methodsThe authors identified 343 consecutive MVPs (58 ± 16 years; 51% female) with ≤moderate MR and comprehensive echocardiographic, 12-lead and ambulatory ECG data. They used hierarchical clustering analysis to identify distinctive MVP phenotypes and investigated their association with: 1) arrhythmic events (sudden cardiac arrest, ventricular fibrillation/tachycardia, or frequent ventricular ectopy); and 2) overall mortality (mean follow-up: 5.4 ± 2.7 years).
resultsThree clusters were identified: Cluster 1 (83% of MVP cases), Cluster 2 (9%), and Cluster 3 (8%). Despite mostly trace/mild MR, Cluster 3 exhibited more abnormal parameters of left atrial (LA) and left ventricular structure and function compared with Clusters 1 and 2 (all P < 0.001). Top clustering features included ECG intervals, LA systolic strain, and LA function index. Arrhythmic presentations (n = 77) were identified in 19%, 38%, and 43% of Clusters 1, 2, and 3 (P < 0.001), respectively. Compared with Cluster 1, Clusters 2 (HR: 5.01; P < 0.001) and 3 (HR: 5.85; P < 0.001) had significantly increased mortality risk.
conclusionsHierarchical clustering based on standard ECG and echocardiographic data alone identifies 3 MVP clusters with distinct arrhythmic profiles and excess mortality, highlighting LA function as a novel risk parameter in MVP without significant MR.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.