Sodium Zirconium Cyclosilicate in HFrEF and Hyperkalemia
Bibliographic record
Abstract
BACKGROUND: Mineralocorticoid receptor antagonists (MRAs) improve outcomes in patients with heart failure and reduced ejection fraction (HFrEF). However, MRAs are often underused because of hyperkalemia concerns. OBJECTIVES: The purpose of this study was to assess whether sodium zirconium cyclosilicate (SZC), a nonabsorbed crystal that traps and rapidly lowers potassium, enables MRA use in patients with HFrEF and prevalent hyperkalemia (or at high risk). METHODS: REALIZE-K is a prospective, double-blind, placebo-controlled trial in patients with HFrEF (NYHA functional class II-IV; left ventricular ejection fraction ≤40%), optimal therapy (except MRA), and prevalent hyperkalemia (or at high risk). During the open-label run-in, all participants underwent protocol-mandated spironolactone titration (target: 50 mg daily); those with prevalent (cohort 1) or incident (cohort 2) hyperkalemia during titration started SZC. Participants achieving normokalemia while on spironolactone ≥25 mg daily were randomized to continuing SZC or matching placebo for 6 months. The primary composite endpoint was proportion of participants with optimal response (normokalemia, on spironolactone ≥25 mg daily, no rescue for hyperkalemia [months 1-6]). RESULTS: , 38% atrial fibrillation/flutter), high N-terminal pro B-type natriuretic peptide (median 1,136 pg/mL), and high HFrEF therapy use (64% sacubitril/valsartan, 96% beta-blocker, 42% sodium glucose co-transporter 2 inhibitor). At randomization, 78% were receiving spironolactone 50 mg daily. CONCLUSIONS: REALIZE-K is the first trial to evaluate whether SZC can enable rapid and safe MRA optimization and long-term continuation in patients with HFrEF and prevalent/high risk of hyperkalemia. (Study to Assess Efficacy and Safety of SZC for the Management of High Potassium in Patients with Symptomatic HFrEF Receiving Spironolactone [REALIZE-K]; NCT04676646).
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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 | 0.001 | 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".