Management of Tricuspid Regurgitation in Patients with Rheumatic Mitral Disease
Bibliographic record
Abstract
BACKGROUND: Rheumatic heart disease (RHD) remains a major cause of cardiovascular morbidity and mortality in low and middle-income countries, with tricuspid regurgitation (TR) frequently complicating rheumatic mitral pathology. While mild TR often stabilizes after mitral correction, management of moderate and severe TR in this context remains controversial due to limited evidence and applicability of guidelines largely derived from degenerative etiologies. This review synthesizes current data on diagnostic strategies, surgical decision-making, and outcomes in rheumatic TR. METHODS: A comprehensive literature search was performed across PubMed, Google Scholar, and OMNI databases (2010-2024) using the keywords "rheumatic mitral disease," "tricuspid regurgitation," and "management." Eligible studies included clinical trials, case series, reviews, and meta-analyses focusing on TR associated with rheumatic mitral disease. Data was extracted and critically appraised for study design, population, and outcome relevance. RESULTS: Evidence for managing TR in rheumatic mitral disease primarily stems from retrospective cohorts and small prospective studies, with few randomized trials. Predictors of TR progression include annular dilation (>21 mm/m²), right ventricular dysfunction, pulmonary hypertension, and atrial fibrillation. Mild TR generally regresses following mitral surgery and is managed conservatively. For moderate TR, concomitant repair may prevent late progression and improve hemodynamics, though risks of postoperative arrhythmia and pacemaker implantation persist. Severe TR warrants surgical correction, preferably rigid ring annuloplasty, while valve replacement is reserved for advanced calcific disease. Emerging transcatheter therapies show promise for high-risk patients but lack robust rheumatic-specific data. CONCLUSION: Optimal management of TR in rheumatic mitral disease requires individualized, Heart Team-based decision-making due to limited high-quality evidence. Future research should prioritize multicenter trials comparing repair, replacement, and transcatheter approaches, integrating advanced imaging for risk stratification and addressing access disparities in resource-limited settings.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".