Patiromer Facilitates Angiotensin Inhibitor and Mineralocorticoid Antagonist Therapies in Patients With Heart Failure and Hyperkalemia
Bibliographic record
Abstract
BACKGROUND: Hyperkalemia (HK) is associated with suboptimal renin-angiotensin system (RAS) inhibitor and mineralocorticoid receptor antagonist (MRA) use in heart failure with reduced ejection fraction (HFrEF). OBJECTIVES: This study sought to assess characteristics and RAS inhibitor/MRA use in patients receiving patiromer during the DIAMOND (Patiromer for the Management of Hyperkalemia in Subjects Receiving RAASi Medications for the Treatment of Heart Failure) run-in phase. METHODS: Patients with HFrEF and HK or past HK entered a run-in phase of ≤12 weeks with patiromer-facilitated RAS inhibitor/MRA optimization to achieve ≥50% recommended RAS inhibitor dose, 50 mg/d MRA, and normokalemia. Patients achieving these criteria (randomized group) were compared with the run-in failure group (patients not meeting the randomization criteria). RESULTS: Of 1,038 patients completing the run-in, 878 (84.6%) were randomized and 160 (15.4%) were run-in failures. Overall, 422 (40.7%) had HK entering run-in with a similar frequency in the randomized and run-in failure groups (40.3% vs 42.5%; P = 0.605). From start to the end of run-in, in the randomized group, an increase was observed in target RAS inhibitor and MRA use in patients with HK (RAS inhibitor: 76.8% to 98.6%; MRA: 35.9% to 98.6%) and past HK (RAS inhibitor: 60.5% to 98.1%; MRA: 15.6% to 98.7%). Despite not meeting the randomization criteria, an increase after run-in was observed in the run-in failure group in target RAS inhibitor (52.5% to 70.6%) and MRA use (15.0% to 48.1%). This increase was observed in patients with HK (RAS inhibitor: 51.5% to 64.7%; MRA: 19.1% to 39.7%) and past HK (RAS inhibitor: 53.3% to 75.0%; MRA: 12.0% to 54.3%). CONCLUSIONS: In patients with HFrEF and HK or past HK receiving suboptimal RAS inhibitor/MRA therapy, RAS inhibitor/MRA optimization increased during patiromer-facilitated run-in.
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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".