P6508Balance of risk and benefit of spironolactone according to renal function in heart failure patients with preserved ejection fraction
Bibliographic record
Abstract
Background: Guidelines now suggest that spironolactone may be useful to reduce heart failure hospitalization (HFH) in heart failure with preserved ejection fraction (HFpEF) patients. However, spironolactone may increase the risk for hyperkalaemia and worsening renal function, particularly in patients with severe renal dysfunction. Purpose: We investigated the influence of baseline renal function on clinical outcomes and the balance of safety and efficacy of spironolactone in HFpEF patients. Methods: Among patients enrolled in the Americas region of the TOPCAT trial (N=1767), we examined the association between baseline renal function categories (CKD-EPI eGFR <45, 45–60, ≥60 mL/min/1.73m2) and the primary efficacy outcome of cardiovascular (CV) death, HFH, or aborted cardiac arrest and safety outcome of drug discontinuation for adverse events (AE). Variation in the efficacy and safety according to baseline renal function was examined in Cox proportional hazard models. Results: Incidence rates for the primary efficacy outcome and drug discontinuation for AE increased with declining eGFR, with highest rates in those with severe renal dysfunction (eGFR<45). Compared to placebo, across all eGFR categories, spironolactone was associated with lower relative risk (RR) for the primary endpoint (interaction p=0.13) and higher RR for drug discontinuation (interaction p=0.46). Over 4-year follow-up, efficacy of spironolactone remained consistent across the range of eGFR, but the difference in absolute risk for drug discontinuation with spironolactone vs placebo was amplified in the low eGFR category (p=0.003).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".