Mineralocorticoid Receptor Antagonists and Renal Outcomes in Heart Failure Patients with and without Chronic Kidney Disease
Bibliographic record
Abstract
INTRODUCTION: The effect of mineralocorticoid receptor antagonists (MRAs) on chronic kidney disease (CKD) progression in patients with heart failure (HF) and with or without preexisting CKD has not been adequately studied. METHODS: We conducted a retrospective cohort study including consecutive adult patients followed at the HF clinic of a tertiary care center who had already been on an angiotensin-converting enzyme inhibitor (ACEI) or an angiotensin receptor blocker (ARB). Exposure to MRAs was assessed at 6 months from registration. Patients who were never exposed to an MRA were the control group. RESULTS: A total of 314 patients who were prescribed an MRA were compared to 1,116 patients who never received an MRA. Among them, 121 and 408 patients, respectively, had CKD (estimated glomerular filtration rate <60 mL/min/1.73 m2). MRAs had to be discontinued in 36/121 patients with CKD (29.8%) and 57/165 patients without CKD (34.5%) (p = 0.39). MRA treatment was associated with a higher risk for persistent creatinine doubling among patients without CKD (hazard ratio 4.07, 95% confidence interval 1.41-11.73). A numerically lower risk was identified among CKD patients (hazard ratio 0.33, 95% confidence interval 0.04-2.78) (p for interaction = 0.009). The primary safety outcome, a composite of any doubling of serum creatinine or any episode of serious hyperkalemia (K+ >6 mmol/L), occurred more commonly in MRA users compared with nonusers in the subgroup of patients without CKD, but not in CKD patients (p for interaction = 0.02). CONCLUSION: MRA treatment in addition to an ACEI or an ARB could be safely prescribed in HF patients with CKD as it is not associated with persistent renal function decline, acute kidney injury, or serious hyperkalemia, compared with ACEI/ARB monotherapy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".