Changes in Natriuretic Peptide Levels Following Patiromer-Enabled Optimization of Medical Therapy In Heart Failure: A Post Hoc Analysis of the DIAMOND Study
Bibliographic record
Abstract
AIMS: In the DIAMOND (Patiromer for the Management of Hyperkalaemia in Subjects Receiving RAASi Medications for the Treatment of Heart Failure) trial, the potassium binder patiromer enabled optimization of renin-angiotensin-aldosterone system inhibitors (RAASi) for patients with heart failure and a reduced ejection fraction (HFrEF) and current or recent hyperkalaemia. In this post-hoc analysis, we evaluated the effect of patiromer-enabled RAASi optimization on N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels, an established surrogate endpoint for clinical outcomes in HFrEF. METHODS AND RESULTS: During screening, 539 (61.4%) of the 878 subsequently randomized patients had NT-proBNP ≥1000 pg/ml, measured prior to a 12-week run-in period on single-blinded patiromer during which RAASi were optimized. Among these patients, 165/266 (62%) in the patiromer and 172/273 (63%) in the placebo arm had follow-up NT-proBNP. For these 337 patients, we evaluated the change in NT-proBNP from screening to week 18 after randomization. NT-proBNP declined by -53% (95% confidence interval -59% to -46%; p < 0.001) in both arms combined (median absolute change: -731 [-1832, 107] pg/ml), with no significant difference between the two arms (p = 0.135). A >30% NT-proBNP reduction was observed in 93/165 (56%) patiromer and 88/172 (51%) placebo patients (p = 0.38), whereas 60/165 (36%) and 53/172 (31%), respectively, achieved NT-proBNP levels <1000 pg/ml at week 18 (p = 0.30). CONCLUSIONS: In this post-hoc analysis of DIAMOND, patients with HFrEF and elevated (>1000 ng/ml) NT-proBNP at screening experienced clinically meaningful NT-proBNP reductions following a RAASi optimization strategy that included patiromer during the run-in phase, with no significant differences observed between patiromer and placebo groups during the randomized withdrawal phase.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".