The American Association for Thoracic Surgery (AATS) 2025 Expert Consensus Document: Surgical management of mitral annular calcification
Bibliographic record
Abstract
OBJECTIVE: Surgery for mitral valve disease in patients with mitral annular calcification (MAC) remains challenging. There is no consensus on the ideal management strategy or patient selection, and perioperative and periprocedural morbidity and mortality rates remain high. The recent surge of patients presenting with MAC has been accompanied by increased interest in MAC surgery and interventions. This expert consensus document is meant to provide a simplified outline for managing MAC, including patient selection, imaging, and surgical and transcatheter therapeutic options, with a particular focus on conventional surgical techniques and hybrid approaches. METHODS: The American Association for Thoracic Surgery Clinical Practice Standards Committee assembled an international panel of cardiac surgeons and structural heart interventionalists with established expertise in the field of MAC. A comprehensive literature review was performed by the panel and a medical librarian. Clinical recommendations were developed utilizing a modified Delphi method. RESULTS: Expert consensus was reached on 33 recommendations, with class of recommendation and level of evidence, for each of 5 main topics: (1) preoperative evaluation for patients with MAC, patient selection, and indications for intervention; (2) standard surgical techniques in MAC; (3) hybrid procedures in MAC; (4) transcatheter MAC interventions; and (5) complications and bailout of MAC surgery and interventions. CONCLUSIONS: Despite the complexity and heterogenicity of patients presenting with MAC, consensus on several key recommendations was reached by this American Association for Thoracic Surgery expert panel. These recommendations provide guidance for cardiac surgeons and structural heart interventionists in treating most patients who present with MAC.
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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 | 0.049 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".