Mitral Valve Endocarditis in Patient Awaiting TAVI: A Case Report
Bibliographic record
Abstract
Background: for TAVI is not well-studied. We present a unique case of a patient waiting for TAVI with decompensated heart failure who was found to have a large mitral vegetation, and consider risk factors for the development of IE in this population. Case description: We report the case of an 85-year-old male with severe aortic stenosis and recurrent small bowel angiodysplasias, requiring frequent blood transfusions and intravenous iron. He presented to a peripheral hospital in decompensated heart failure. Transfer was arranged to our center to expedite TAVI, under the premise that worsening aortic stenosis precipitated his decompensated state. Prior to TAVI, an echocardiogram was done, and demonstrated a 30 × 18 mm mass on the mitral valve with anterior leaflet perforation and severe mitral regurgitation. The findings were consistent with IE, and the TAVI was cancelled. Despite antibiotic therapy, the patient unfortunately deteriorated and palliative care was provided. Conclusions: This case highlights the need for further research regarding the risk of IE in patients waiting for TAVI. Current literature focuses on the development and management of IE following TAVI. Clinicians must understand that TAVI candidates have multiple risk factors for IE, including valvular disease, age, and comorbidities. IE should be considered as a possible cause for decompensated heart failure in patients awaiting TAVI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".