Patiromer Facilitates Angiotensin Inhibitor and Mineralocorticoid Antagonist Therapies in Patients With Heart Failure and Hyperkalemia
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it