Potassium and the Use of Renin–Angiotensin–Aldosterone System Inhibitors in Heart Failure with Reduced Ejection Fraction: Data from BIOSTAT-CHF
Bibliographic record
Abstract
Abstract Background Hyperkalaemia is a common co-morbidity in patients with heart failure with reduced ejection fraction (HFrEF). Whether it affects the use of renin–angiotensin–aldosterone system inhibitors and thereby negatively impacts outcome is unknown. Therefore, we investigated the association between potassium and uptitration of angiotensin-converting enzyme inhibitors (ACEi)/angiotensin receptor blockers (ARB) and its association with outcome. Methods and results Out of 2516 patients from the BIOSTAT-CHF study, potassium levels were available in 1666 patients with HFrEF. These patients were sub-optimally treated with ACEi/ARB or beta-blockers and were anticipated and encouraged to be uptitrated. Potassium levels were available at inclusion and at 9 months. Outcome was a composite of all-cause mortality and heart failure hospitalization at 2 years. Patients' mean age was 67 ± 12 years and 77% were male. At baseline, median serum potassium was 4.3 (interquartile range 3.9–4.6) mEq/L. After 9 months, 401 (24.1%) patients were successfully uptitrated with ACEi/ARB. During this period, mean serum potassium increased by 0.16 ± 0.66 mEq/L (P < 0.001). Baseline potassium was an independent predictor of lower ACEi/ARB dosage achieved [odds ratio 0.70; 95% confidence interval (CI) 0.51–0.98]. An increase in potassium was not associated with adverse outcomes (hazard ratio 1.15; 95% CI 0.86–1.53). No interaction on outcome was found between baseline potassium, potassium increase during uptitration, or potassium at 9 months and increased dosage of ACEi/ARB (Pinteraction > 0.5 for all). Conclusion Higher potassium levels are an independent predictor of enduring lower dosages of ACEi/ARB. Higher potassium levels do not attenuate the beneficial effects of ACEi/ARB uptitration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".