Renin-Angiotensin System Inhibition and Cardiac Damage in Patients Undergoing Transcatheter Aortic Valve Replacement
Bibliographic record
Abstract
Background The optimal medical treatment strategy after transcatheter aortic valve replacement (TAVR) has not been established, and might be affected by the extent of extravalvular cardiac damage. We aimed to investigate the prognostic effect of renin-angiotensin system (RAS) inhibitors in TAVR patients stratified according to the extent of extravalvular cardiac damage. Methods In a prospective TAVR registry, patients were retrospectively evaluated for baseline cardiac damage and classified into 5 stages of cardiac damage (0-4) according to established criteria. Clinical outcomes at 1 year were compared according to RAS inhibitor prescription at discharge. Results Among 2247 eligible patients who underwent TAVR between August 2007 and June 2021, 1634 (72.7%) were prescribed RAS inhibitors at discharge. Eighty-three patients (3.7%) were classified as stage 0, 276 (12.3%) as stage 1, 889 (39.6%) as stage 2, 489 (21.8%) as stage 3, and 510 (22.7%) as stage 4. RAS inhibitor prescription after TAVR was associated with a reduced risk of 1-year mortality (adjusted hazard ratio [HR adjusted ], 0.59; 95% confidence interval [CI], 0.45-0.77). The protective effect was accentuated among patients with cardiac stages 3 and 4 (HR adjusted , 0.54 [95% CI, 0.32-0.92]; and HR adjusted , 0.58 [95% CI, 0.36-0.92], respectively), but not statistically significant in for those with stage 2 (HR adjusted , 0.70; 95% CI, 0.43-1.14). Conclusions In patients who underwent TAVR, we found a strong association of RAS inhibitor prescription and improved clinical outcome in the overall population, and there were no signs of heterogeneity across stages of cardiac damage. Clinical Trial Registration NCT01368250.
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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 | 0.000 | 0.000 |
| 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".