Dismal Outcomes and High Societal Burden of Mitral Valve Regurgitation in France in the Recent Era: A Nationwide Perspective
Bibliographic record
Abstract
Background Although US recent data suggest that mitral regurgitation (MR) is severely undertreated and carries a poor outcome, population-based views on outcome and management are limited. We aimed to define the current treatment standards, clinical outcomes, and costs related to MR at the nationwide level. Methods and Results In total, 107 412 patients with MR were admitted in France in 2014 to 2015. Within 1 year, 8% were operated and 92% were conservatively managed and constituted our study population (68% primary MR and 32% secondary MR). The mean age was 77±15 years; most patients presented with comorbidities. In-hospital and 1-year mortality rates were 4.1% and 14.3%, respectively. Readmissions were common (63% at least once and 37% readmitted ≥2 times). Rates of 1-year mortality or all-cause readmission and 1-year mortality or heart failure readmission were 67% and 34%, respectively, and increased with age, Charlson index, heart failure at admission, and secondary MR etiology; however, the event rate remained notably high in the primary MR subset (64% and 28%, respectively). The mean costs of hospital admissions and of readmissions were 5345±6432 and 10 080±10 847 euros, respectively. Conclusions At the nationwide level, MR was a common reason for admission and affected an elderly population with frequent comorbidities. Less than 10% of patients underwent a valve intervention. All subsets of patients who were conservatively managed incurred high mortality and readmissions rates, and MR represented a major societal burden with an extrapolated annual cost of 350 to 550 million euros (390-615 million US dollars). New strategies to improve the management and outcomes of patients with both primary and secondary MR are critical and warranted.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".