Mitral Valve Surgery with and Without Mitral Annular Disjunction: A Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: Despite growing awareness of mitral annular disjunction’s (MAD) clinical significance, robust data regarding the outcomes of surgical interventions on MAD remain sparse. This meta-analysis aims to systematically evaluate the current evidence on the efficacy and safety of mitral valve (MV) surgery in patients with MAD. Methods: A systematic review was conducted from inception until May 2025 for studies comparing patients undergoing MV surgery with and without MAD and was registered in PROSPERO: CRD42025649821. Results: Patients with MAD were generally younger (59.3 ± 5.0 vs. 63.4 ± 2.2 years, standardized mean difference: −0.3073), had fewer comorbidities but more complex valve lesions (41.0% vs. 13.7%, risk difference: 0.2627) compared to those without MAD. MV replacement was performed less frequently in the MAD group than in the No-MAD group (risk ratio, RR: 0.690 [95% confidence interval, CI: 0.508; 0.937], p = 0.017), probably related to the significant difference in age between the two groups. The MAD cohort demonstrated a higher incidence of ventricular arrhythmia both after surgery (RR: 7.255 [95%CI: 1.231; 42.763], p = 0.029) and during follow-up (incidence rate ratio, IRR: 2.750 [95%CI: 1.372; 5.512], p = 0.004). Although the MAD group experienced more arrhythmic events over time, this did not translate into a significant difference in overall mortality compared to patients without MAD (IRR: 0.573 [95%CI: 0.072; 4.555], p = 0.599). Conclusions: This meta-analysis revealed significant baseline differences between the populations. Our findings suggest that patients with MAD remained at significantly higher risk for both postoperative and long-term arrhythmias. These results highlight the need for close arrhythmic surveillance in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.046 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".