Thinking Outside the Box: Case Report of a Rare Quadricuspid Aortic Valve as an Underrecognized Cause of Heart Failure and Atrial Fibrillation
Bibliographic record
Abstract
Quadricuspid aortic valve (QAV) is a rare congenital anomaly with an estimated incidence of 0.008% to 0.043% based on autopsy and echocardiographic studies. Although often asymptomatic, it can lead to progressive aortic regurgitation (AR), left ventricular (LV) dysfunction, and arrhythmias such as atrial fibrillation (AF). Due to its rarity, QAV is often misdiagnosed or discovered incidentally, highlighting the need for advanced cardiac imaging in young patients presenting with unexplained heart failure symptoms and arrhythmias. We present the case of a 41-year-old female patient who was admitted with new-onset dyspnea classified as New York Heart Association (NYHA) class III and palpitations due to persistent AF with a European Heart Rhythm Association (EHRA) symptom class 2b. There was no family history of congenital or structural heart disease, with arterial hypertension being the only identified predisposing condition. Initial transthoracic echocardiography revealed moderate AR, but more detailed transesophageal echocardiography performed before pulmonary vein isolation incidentally revealed a QAV. Further cardiac magnetic resonance imaging confirmed normal aortic root dimensions with early LV remodeling. The patient was managed conservatively with rate control, anticoagulation, and regular follow-up to monitor disease progression. This case highlights the importance of advanced imaging techniques in the diagnosis of rare structural heart abnormalities in young patients presenting with unexplained heart failure symptoms and arrhythmias. Early identification of QAV allows for timely medical intervention, optimal patient monitoring, and prevention of long-term complications. Regular follow-up is essential to monitor disease progression and determine the need for surgical intervention.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".