Adverse effects during treatment with renin–angiotensin–aldosterone system inhibitors; should we stay or should we stop?
Bibliographic record
Abstract
PURPOSE OF REVIEW: To discuss recent evidence on the benefits and harms of stopping therapy with renin-angiotensin-aldosterone system inhibitors (RAASi) after the occurrence of adverse events or in patients with advanced chronic kidney disease (CKD). RECENT FINDINGS: RAASi may result hyperkalemia or acute kidney injury (AKI), particularly in persons with CKD. Guidelines recommend to temporarily stop RAASi until the problem is resolved. However permanent discontinuation of RAASi is common in clinical practice with the potential to heighten subsequent cardiovascular disease (CVD) risk. A series of studies evaluating the consequences of stopping RAASi (vs. continuing) after an episode of hyperkalemia or AKI consistently report worse clinical outcomes, both higher risk of death and cardiovascular events. The STOP-angiotensin converting enzyme inhibitors (ACEi) trial and two large observational studies also favor the decision to continue ACEi/ angiotensin receptor blockers in advanced CKD, refuting old observations that use of these medications can accelerate the risk of kidney replacement therapy. SUMMARY: Available evidence suggests continuing with RAASi after the occurrence of adverse events or in patients with advanced CKD, primarily attributed to sustained cardioprotection. This is in line with current guideline recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".