Stopping renin-angiotensin system inhibitors after hyperkalemia and risk of adverse outcomes
Bibliographic record
Abstract
BACKGROUND: Stopping renin-angiotensin system inhibitors (RASi) after an episode of hyperkalemia is common but may involve therapeutic compromises, in that the cessation of RASi deprives patients of their beneficial cardiovascular effects. METHODS AND RESULTS: Observational study from the Stockholm Creatinine Measurements (SCREAM) project including patients initiating RASi in routine care and surviving a first-detected episode of hyperkalemia (potassium >5.0 mmol/L). We used target trial emulation techniques based on cloning, censoring and weighting to compare stopping vs. continuing RASi within 6 months after hyperkalemia. Outcomes were 3-year risks of mortality, major adverse cardiovascular events (MACE, composite of cardiovascular death, myocardial infarction and stroke hospitalization) and recurrent hyperkalemia. Of 5669 new users of RASi who developed hyperkalemia (median age 72 years, 44% women), 1425 (25%) stopped RASi therapy within 6 months. Compared with continuing RASi, stopping therapy was associated with a higher 3-year risk of death (absolute risk difference 10.8%; HR 1.49, 95% CI 1.34-1.64) and MACE (risk difference 4.7%; HR 1.29, 1.14-1.45), but a lower risk of recurrent hyperkalemia (risk difference -9.5%; HR 0.76, 0.69-0.84). Results were consistent for events following potassium of >5.0 or >5.5 mmol/L, after censoring when the treatment decision was changed, across prespecified subgroups, and after adjusting for albuminuria. CONCLUSION: These findings suggest that stopping RASi after hyperkalemia may be associated with a lower risk of recurrence of hyperkalemia, but higher risk of death and cardiovascular events.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".