Abstract 16658: Serum Potassium and Risk of Death in Patients With HFpEF: An Analysis of PARAGON-HF
Bibliographic record
Abstract
Introduction: Hyper- and hypo-kalemia have each been associated with higher risk of death in in heart failure with reduced ejection fraction but the relationship between serum potassium and risk of death in heart failure with preserved ejection fraction (HFpEF) is not well established. We assessed the risk associated with high and low potassium in patients with HFpEF enrolled in the PARAGON-HF trial. Aim: To explore the association between serum potassium and mortality in patients with HFpEF and examine the interaction with renal function. Methods: Repeated events, Cox and mixed-effects models. The primary outcome in this analysis was death from any cause. Results: Patients: mean age 73 years, 52% female. Higher potassium was not associated with higher risk of death: adjusted time-updated HR (95%CI) for potassium >5.0 mmol/l =1.06 (0.85-1.32); p=0.61 (potassium 4-5 mmol/l referent HR=1.0). However, lower potassium was associated with higher risk of death: adjusted HR for potassium <4.0 mmol/l=1.51 (1.21-1.87); p<0.001. However, the risk related to potassium was modified by baseline renal function (p for interaction <0.05), whereby the excess mortality in patients with low potassium was most prominent in patients with an eGFR <60 ml/min/1.73m 2 (Figure). Conclusion: In adjusted analyses, low potassium was independently associated with mortality in patients with HFpEF, especially in the context of renal impairment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".