Potassium Reduction with Sodium Zirconium Cyclosilicate in Patients with Heart Failure
Bibliographic record
Abstract
AIMS: Several patients with heart failure and reduced ejection fraction (HFrEF) do not receive renin-angiotensin-aldosterone system (RAAS) inhibitors at the recommended dose or at all, frequently due to actual or feared hyperkalaemia. Sodium zirconium cyclosilicate (SZC) is an orally administered non-absorbed intestinal potassium binder proven to lower serum potassium concentrations. METHODS AND RESULTS: PRIORITIZE-HF was an international, multicentre, parallel-group, randomized, double-blind, placebo-controlled study to evaluate the benefits and risks of using SZC to intensify RAAS inhibitor therapy. Patients with symptomatic HFrEF were eligible and randomly assigned to receive SZC 5 g or placebo once daily for 12 weeks. Doses of study medication and RAAS inhibitors were titrated during the treatment period. The primary endpoint was the proportion of patients at 12 weeks in the following categories: (i) any RAAS inhibitor at less than target dose, and no MRA; (ii) any RAAS inhibitor at target dose and no MRA; (ii) MRA at less than target dose; and (iv) MRA at target dose. Due to challenges in participant management related to the COVID-19 pandemic, the study was prematurely terminated with 182 randomized patients. There was no statistically significant difference in the distribution of patients by RAAS inhibitor treatment categories at 3 months (P = 0.43). The proportion of patients at target MRA dose was numerically higher in the SZC group (56.4%) compared with the placebo group (47.0%). Overall, SZC was well tolerated. CONCLUSIONS: PRIORITIZE-HF was terminated prematurely due to COVID-19 and did not demonstrate a statistically significant increase in the intensity of RAAS inhibitor therapies with the potassium-reducing agent SZC compared with placebo.
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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 | 0.001 |
| 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 | 0.000 | 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".