Serum Potassium in the PARADIGM-HF Trial
Bibliographic record
Abstract
Abstract Aims The associations between potassium level and outcomes, the effect of sacubitril–valsartan on potassium level, and whether potassium level modified the effect of sacubitril–valsartan in patients with heart failure and a reduced ejection fraction were studied in PARADIGM-HF. Several outcomes, including cardiovascular death, sudden death, pump failure death, non-cardiovascular death and heart failure hospitalization, were examined. Methods and results A total of 8399 patients were randomized to either enalapril or sacubitril–valsartan. Potassium level at randomization and follow-up was examined as a continuous and categorical variable (≤3.5, 3.6–4.0, 4.1–4.9, 5.0–5.4 and ≥5.5 mmol/L) in various statistical models. Hyperkalaemia was defined as K+ ≥5.5 mmol/L and hypokalaemia as K+ ≤3.5 mmol/L. Compared with potassium 4.1–4.9 mmol/L, both hypokalaemia [hazard ratio (HR) 2.40, 95% confidence interval (CI) 1.84–3.14] and hyperkalaemia (HR 1.42, 95% CI 1.10–1.83) were associated with a higher risk for cardiovascular death. However, potassium abnormalities were similarly associated with sudden death and pump failure death, as well as non-cardiovascular death and heart failure hospitalization. Sacubitril–valsartan had no effect on potassium overall. The benefit of sacubitril–valsartan over enalapril was consistent across the range of baseline potassium levels. Conclusions Although both higher and lower potassium levels were independent predictors of cardiovascular death, potassium abnormalities may mainly be markers rather than mediators of risk for death.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".