Risk Factors for Sudden Cardiac Arrest and Ventricular Arrhythmias in Arrhythmogenic Mitral Valve Prolapse Syndrome
Bibliographic record
Abstract
ABSTRACT Background Patients with the arrhythmogenic mitral valve prolapse syndrome (AMVPS) are at increased risk for life-threatening ventricular arrhythmias (VAs), but studies have been limited by small sample sizes. We sought to assemble an international AMVPS registry to delineate clinical, imaging, treatment characteristics, and risk factors for sudden cardiac arrest (SCA). Methods We retrospectively identified two groups of subjects with AMVPS: 1) the MVP-SCA group with SCA, sustained ventricular tachycardia (VT), and ventricular fibrillation (VF); and 2) the MVP-PVC group with significant premature ventricular complexes (PVCs) only. Deidentified data was abstracted locally and combined centrally. Results We included 217 subjects with AMVPS: 148 (68%) had SCA or VT/VF (MVP-SCA group) and 69 (32%) had PVCs only (MVP-PVC group). Phenotypically, both groups were similar [mean age 44.2±16.7 years, 66% female, 76% with bileaflet prolapse, 55% with mitral annular disjunction (MAD)]. Syncope was more common in the MVP-SCA group than the MVP-PVC group (47% vs 22%, p=0.001) as were anterolateral T-wave inversions (TWIs, 22% vs 7%, p=0.011). Prior mitral valve surgery was less common in the MVP-SCA group (6% vs 20%, p=0.002). These differences remained significant after multivariable adjustment. An electrophysiology (EP) study was negative in 15/45 (33%) of the MVP-SCA subjects. Conclusions In this international registry, AMVPS subjects were young, female, and had bileaflet prolapse with MAD. A history of syncope and anterolateral TWIs were associated with SCA. Prior mitral valve surgery was less common in SCA subjects. A negative EP study had limited negative predictive value in high-risk patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".