Patiromer for Heart Failure Medication Optimization in Patients With Current or Past Hyperkalemia
Bibliographic record
Abstract
Background For heart failure with reduced ejection fraction (HFrEF), suboptimal use of renin-angiotensin-aldosterone system inhibitors (RAASis), including mineralocorticoid receptor antagonists (MRAs), due to hyperkalemia, may be improved by potassium binders. Objectives This prespecified analysis of the phase 3 DIAMOND (Patiromer for the Management of Hyperkalemia in Subjects Receiving RAASi Medications for the Treatment of Heart Failure) trial assessed the effect of patiromer in patients with HFrEF and either current or past hyperkalemia. Methods Patients with HFrEF and current or past (within 1 year before enrollment) hyperkalemia (serum potassium [sK ] >5.0 mmol/L) entered a single-blind, run-in phase to optimize RAASis while receiving patiromer. They were subsequently randomized, double-blind, to continue patiromer or change to placebo. Results Of the 1,038 patients who completed run-in, 354 (83.9%) of 422 with current hyperkalemia and 524 (85.1%) of 616 with past hyperkalemia achieved RAASi optimization and were randomized to treatment. During the double-blind phase, patiromer lowered sK levels compared with placebo in both the current and past hyperkalemia subgroups: difference in adjusted mean change from baseline: –0.12 (95% CI: –0.17 to –0.07) and –0.08 (95% CI: –0.12 to –0.05), respectively; P interaction = 0.166. Patiromer was more effective than placebo in maintaining MRA at target dose in patients with current vs past hyperkalemia (HR: 0.45 [95% CI: 0.26-0.76] vs HR: 0.85 [95% CI: 0.54-1.32]; P interaction = 0.031). Adverse events were similar between subgroups. Conclusions The use of patiromer facilitates achieving target doses of RAASis in patients with HFrEF with either current or past hyperkalemia. For those with current hyperkalemia before RAASi optimization, use of patiromer may be more beneficial in helping to maintain sK control and achieve MRA target dose. (Patiromer for the Management of Hyperkalemia in Subjects Receiving RAASi Medications for the Treatment of Heart Failure [DIAMOND]; NCT03888066 )
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".