Long-Term Outcomes After Mitral Valve Replacement in Sex- and Age-Matched Patients With vs Without Mitral Annular Calcification
Bibliographic record
Abstract
Background Mitral annular calcification (MAC) portends the need for a technically challenging mitral valve surgery and is associated with poor outcomes after mitral valve replacement (MVR). No study has compared long-term outcomes for patients with vs without MAC in age- and sex-matched cohorts. Methods Between 2000 and 2017, a total of 67 patients with MAC who underwent MVR were age- and sex-matched 1:3 with patients with other etiologies without MAC to create a study cohort of 268. An extended Cox regression model was used to investigate long-term outcomes of patients with MAC, compared to those with other mitral etiologies. Results The groups were matched for age (MAC, 70.5 years; non-MAC, 70.4 years) and sex (MAC, 61.2% male; non-MAC, 61.7% male). MAC was not a risk factor for 1-year mortality. After 1 year, MAC was an independent risk factor for reduced survival (hazard ratio 2.781, 95% confidence interval 1.642-4.709, P < 0.001). The 5-year and 10-year survival rates were significantly lower in the MAC group than they were in the non-MAC group (5-year: 51.0% ± 6.9% vs 74.6% ± 3.1%; 10-year: 40.1% ± 8.0% vs 51.8% ± 4.1%, P < 0.001). Peripheral vascular disease was the only independent risk factor for both early mortality and reduced long-term survival, and chronic renal failure was a strong independent risk factor for 1-year mortality. Conclusions In an age- and sex-matched cohort, patients with MAC have similar early outcomes, but poorer long-term survival following MVR, compared to those without MAC, suggesting that MVR can be performed safely in selected patients with MAC. MAC and PVD are independent risk factors for reduced long-term survival.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".