Early and late outcomes of mitral valve surgery in the setting of mitral annular calcification: a systematic review with narrative synthesis
Bibliographic record
Abstract
Background: Mitral annular calcification (MAC) is a degenerative calcific pathology of the mitral valve (MV) associated with MV dysfunction and poor patient outcomes. The pathophysiologic complexity of MAC presents unique challenges for surgical management. In this systematic review, we summarize the heterogenous approaches to MV surgery for MAC and assess early and late outcomes of each approach. Methods: A systematic literature search was performed in the PubMed, EMBASE, and Web of Science databases. Three reviewers independently selected relevant studies through a sequential three-step review process. Based on included descriptions of intraoperative methods, each study was categorized as either a "MAC Respect" or "MAC Resect" intervention. Quantitative data were collected, aggregated across all studies, and analyzed by surgical approach. Results: Our initial search yielded 635 unique studies, of which 19 studies met inclusion criteria for quantitative data extraction. Based on the operative approach, two cohorts of "MAC Respect" (N=550) and "MAC Resect" (N=487) were created. Baseline characteristics were similar; the median patient age and proportion of female patients were 71.5 years and 66.4% in the "Respect" group and 70.3 years and 54.9% in the "Resect" group, respectively. A median of 26.9% of patients in the "Resect" group and 12.5% in the "Resect" group were classified as New York Heart Association (NYHA) class III or IV. "Respect" studies had a median cardiopulmonary bypass time of 156 minutes, while "Resect" studies had a median time of 181.5 minutes. The median intensive care unit stay was two days for the "Respect" group and 3.5 days for the "Resect" group. Ranges of complication rates largely overlapped between groups. Thirty-day, one-year, and long-term mortality rates were 0-25%, 0-44%, and 0-27% in the "Respect" group and 0-14%, 0-18%, and 0-50% in the "Resect" group. Conclusions: Surgical intervention remains the gold-standard for management of MAC-related MV dysfunction; however, there is no standardized consensus for the optimal surgical approach. This systematic review evaluates the advantages, disadvantages, and outcomes of several approaches to MAC surgical intervention. Our findings underscore the heterogeneous presentation of MAC and the associated complications to avoid in order to improve patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| 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) | 0.000 | 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 teacher head, 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".