The effect of combination treatment with aliskiren and blockers of the renin-angiotensin system on hyperkalaemia and acute kidney injury: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To examine the safety of using aliskiren combined with agents used to block the renin-angiotensin system. DESIGN: Systematic review and meta-analysis of randomised controlled trials. DATA SOURCES: Medline, Embase, the Cochrane Library, and two trial registries, published up to 7 May 2011. STUDY SELECTION: Published and unpublished randomised controlled trials that compared combined treatment using aliskiren and angiotensin converting enzyme inhibitors or angiotensin receptor blockers with monotherapy using these agents for at least four weeks and that provided numerical data on the adverse event outcomes of hyperkalaemia and acute kidney injury. A random effects model was used to calculate pooled risk ratios and 95% confidence intervals for these outcomes. RESULTS: 10 randomised controlled studies (4814 participants) were included in the analysis. Combination therapy with aliskiren and angiotensin converting enzyme inhibitors or angiotensin receptor blockers significantly increased the risk of hyperkalaemia compared with monotherapy using angiotensin converting enzymes or angiotensin receptor blockers (relative risk 1.58, 95% confidence interval 1.24 to 2.02) or aliskiren alone (1.67, 1.01 to 2.79). The risk of acute kidney injury did not differ significantly between the combined therapy and monotherapy groups (1.14, 0.68 to 1.89). CONCLUSION: Use of aliskerin in combination with angiotensin converting enzyme inhibitors or angiotensin receptor blockers is associated with an increased risk for hyperkalaemia. The combined use of these agents warrants careful monitoring of serum potassium levels.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.037 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".