Hyperkalemia-related Heart Failure Therapy Discontinuation and the Association with Outcomes in Patients with Heart Failure
Bibliographic record
Abstract
Abstract Background Renin-angiotensin-aldosterone system (RAAS) inhibitors are essential treatments for heart failure (HF) patients, but their use is often limited by hyperkalemia. Objective This study assesses the incidence of hyperkalemia in chronic HF patients on RAAS inhibitors, examines changes in therapy following hyperkalemia episodes, and evaluates the impact of RAAS inhibitor discontinuation or down-titration on patient outcomes. Methods We conducted a population-based cohort study of patients hospitalized or visiting the emergency department in Alberta for chronic HF from April 2012 to March 2020, focusing on those with RAAS inhibitor-associated hyperkalemia. Episodes of hyperkalemia (K >5.0 mmol/L) were monitored, and patients were followed for 30 days to determine if their RAAS therapy was maintained, reduced, or discontinued. Results Among 7527 HF patients, we identified 123,038 RAAS inhibitor treatment years, resulting in 17 hyperkalemia events per 100 treatment years. Hyperkalemia led to RAAS inhibitor discontinuation in 35.2% of cases, down-titration in 8.4%, and continuation in 56.4%. Discontinuation or down-titration was more common when serum potassium exceeded 6.0 mmol/L (49.4%) compared to lower levels. Over a median follow-up of 1.4 years, discontinuing or down-titrating RAAS inhibitors was associated with increased all-cause mortality (aHR 1.80), higher cardiovascular hospitalizations (aHR 1.09), and more frequent ED visits for HF (aHR 1.17) compared to continued therapy. Conclusions Discontinuation or down-titration of RAAS inhibitors in HF patients is associated with higher mortality and cardiovascular events. Strategies to maintain RAAS therapy after hyperkalemia episodes may improve patient outcomes.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".