Safety of Continuing Mineralocorticoid Receptor Antagonist Treatment in Patients with Heart Failure with Reduced Ejection Fraction and Severe Kidney Disease: Data from Swedish Heart Failure Registry
Bibliographic record
Abstract
ABSTRACT Aims Mineralocorticoid receptor antagonists (MRAs) improve outcomes in heart failure with reduced ejection fraction (HFrEF) but remain underused and are often discontinued especially in patients with chronic kidney disease (CKD) due to concerns on renal safety. Therefore, in a real-world HFrEF population we investigated the safety of MRA use, in terms of risk of renal events, any mortality and any hospitalization, across the estimated glomerular filtration rate (eGFR) spectrum including severe CKD. Methods and results We analysed patients with HFrEF (ejection fraction <40%), not on dialysis, from the Swedish Heart Failure Registry. We performed multivariable logistic regression models to investigate patient characteristics independently associated with MRA use, and univariable and multivariable Cox regression models to assess the associations between MRA use and outcomes. Of 33 942 patients, 17 489 (51%) received MRA, 32%, 45%, 54%, 54% with eGFR <30, 30–44, 45–59 or ≥60 ml/min/1.73 m2, respectively. An eGFR ≥60 ml/min/1.73 m2 and patient characteristics linked with more severe HF were independently associated with more likely MRA use. In multivariable analyses, MRA use was consistently not associated with a higher risk of renal events (i.e. composite of dialysis/renal death/hospitalization for renal failure or hyperkalaemia) (hazard ratio [HR] 1.04, 95% confidence interval [CI] 0.98–1.10), all-cause death (HR 1.02, 95% CI 0.97–1.08) as well as of all-cause hospitalization (HR 0.99, 95% CI 0.95–1.02) across the eGFR spectrum including also severe CKD. Conclusions The use of MRAs in patients with HFrEF decreased with worse renal function; however their safety profile was demonstrated to be consistent across the entire eGFR spectrum.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".