Safety and Tolerability of the Direct Renin Inhibitor Aliskiren in Combination With Angiotensin Receptor Blockers and Thiazide Diuretics: A Pooled Analysis of Clinical Experience of 12,942 Patients
Bibliographic record
Abstract
Combinations of the direct renin inhibitor aliskiren with angiotensin receptor blockers (ARBs) or diuretics are effective therapeutic regimens for the treatment of hypertension. A large database of safety information has become available during the past several years with aliskiren in combination trials. Data were pooled from 9 short-term (8-week) and 4 longer-term (26- to 52-week) randomized controlled trials of aliskiren in patients with hypertension. Adverse event (AE) rates were assessed for aliskiren combination therapy compared with component monotherapies. In short-term studies, overall AE rates were similar for patients receiving aliskiren/valsartan or aliskiren/diuretic combinations (32.2%-39.8%) and those receiving the component monotherapies (30.0%-39.6%). In longer-term studies, AE rates with aliskiren/losartan (55.5%) and aliskiren/diuretic (45.0%) combination therapy were similar to those with losartan (53.9%) and diuretic (48.9%) alone. Angioedema and hyperkalemia occurred in similar proportions of patients taking combination therapies vs monotherapy. The safety and tolerability profile of aliskiren in combination with the ARBs valsartan or losartan, or diuretic, is similar to aliskiren, ARBs, or diuretics alone.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".