Infective Endocarditis in Patients with Mitral Annular Calcification: Clinical and Echocardiographic Presentation, Management, and Outcomes
Bibliographic record
Abstract
Background Mitral annular calcification (MAC) poses unique challenges in infective endocarditis (IE). This study aimed to characterize echocardiographic features of IE involving MAC and assess management and outcomes. Methods We reviewed cases discussed during our IE multidisciplinary meetings between 2021-2024. Clinical data, imaging findings, and outcomes were collected through chart review. Results Among 741 patients evaluated, 51 patients (7%) (73±11 years, 45% female) had possible or definite IE and moderate/severe MAC. IE involved the mitral valve in 24 patients (3%). Mitral IE was more frequent in women (63% vs. 30%, P=0.02) and less commonly associated with prior aortic valve replacement (17% vs. 59%, P <0.01). Staphylococcus aureus was the most common pathogen with no difference between groups (38% vs 30% in the mitral and non-mitral IE respectively, P=0.55). Patients with mitral IE often presented with large vegetation (median 13 mm), frequent valvular or perivalvular complications (moderate or greater MR in 54%, perforation in 33% and perivalvular abscess in 13%). A succulent aspect of the vegetation mass with abnormal MAC mobility, described as "rocking", was observed in 7 patients (29%). Surgical indications were found in 63% of patients, but only 40% underwent surgery due to high perceived risk. In-hospital mortality was not different between mitral and non-mitral IE groups (29% vs 15%, P=0.32). Conclusion In patients with IE and MAC, the mitral valve was often primarily affected, showing large, mobile vegetations with a rocking motion. Despite frequent surgical indications, few underwent surgery, highlighting the need for improved management in this high-risk group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".