Abstract 4144585: Association of renal function with mortality and heart failure hospitalization rates after Transcatheter Mitral Valve Edge to Edge Repair
Bibliographic record
Abstract
Background: Transcatheter edge to edge repair (TEER) is an established treatment for patients with symptomatic severe functional MR on optimal medical therapy or severe symptomatic primary MR and high surgical risk. Renal dysfunction is associated with adverse outcomes but the threshold at which risks rise are uncertain. Aim To determine the association of estimated glomerular filtration rate (eGFR) with adverse outcomes in patients undergoing TEER for severe symptomatic mitral regurgitation (MR) in Ontario, Canada. Methods: This was a population-based retrospective cohort study using linked administrative datasets of patients who underwent TEER in Ontario, Canada, between 2011 and 2023. The key exposure was eGFR, which was modeled using restricted cubic splines. Outcomes were 1-year mortality, cardiovascular mortality (CV) and heart failure hospitalization (HF). Cause-specific hazards regression was used to model the association between eGFR and outcomes, utilizing eGFR 30ml/min/1.73m2 as the reference value. Results: We studied 2076 patients, of whom 294 (14.2%) had eGFR <30ml/min/1.73m2 and 841 (40.5%) had eGFR 30-60ml/min/1.73m2. The incidence at one year was 16.8% for all-cause mortality, 11% for CV mortality and 14.2% for HF hospitalizations. The Figure illustrates that the predicted incidence of adverse outcomes increases with lower eGFR, with subtle differences in patterns of change in risk at lower eGFR levels such that all-cause mortality and CV mortality begin to rise rapidly at eGFR values ≤30ml/min/1.73m2, while HF hospitalization increases linearly at eGFR ≤50ml/min/1.73m2. Conclusions: There was a progressive increase in all-cause, CV mortality and HF hospitalization after TEER for patients with lower eGFR, particularly those with values <30 ml/min/1.73m2. This data can inform patient discussions and guide post-procedural follow-up.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".