Competing Risks of Cardiac and Noncardiac Mortality in Patients With Secondary Mitral Regurgitation Undergoing Transcatheter Edge‐to‐Edge Repair
Bibliographic record
Abstract
Background The relative impact of cardiac and noncardiac mortality in patients with secondary mitral regurgitation undergoing mitral transcatheter edge‐to‐edge repair (M‐TEER) has been poorly investigated. We aimed to assess the competing risks and independent predictors of cardiac and noncardiac mortality in a real‐world secondary mitral regurgitation population treated with M‐TEER and included in the GIOTTO (Italian Society of Interventional Cardiology [GIse] Registry Of Transcatheter Treatment of Mitral Valve Regurgitation) registry. Methods Competing risks analysis was used to assess the cumulative incidence of cardiac and noncardiac mortality. Cox regression identified independent predictors of each outcome. Co‐primary outcomes were cardiac and noncardiac death at 2 years. Results The analysis included 1185 consecutive patients with secondary mitral regurgitation treated with M‐TEER between January 2016 and March 2020 (median age 74 years). Two‐year cumulative incidences of cardiac and noncardiac mortality were 19% and 12%, respectively. At multivariable analysis, predictors of cardiac mortality were age (hazard ratio [HR], 1.03; P =0.002), New York Heart Association class (HR, 1.44; P =0.018), previous hospitalization for heart failure (HR, 1.67; P =0.016), hemoglobin (HR, 0.89; P =0.016), left ventricular end‐diastolic diameter (HR, 1.02; P =0.025), left ventricular ejection fraction (HR, 0.98; P =0.022), and daily furosemide dose (HR, 1.19; P =0.003). Predictors of noncardiac mortality were New York Heart Association class (HR, 1.70; P =0.03), estimated glomerular filtration rate (HR, 0.98; P =0.002), and smoking habit (HR, 1.82; P =0.009). Conclusions Patients with secondary mitral regurgitation treated with M‐TEER show a high 2‐year incidence of both cardiac and noncardiac mortality. Understanding competing risks of mortality may improve patient selection for M‐TEER.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".