Dose-reducing H2 receptor antagonists in the presence of low glomerular filtration rate: a systematic review of the evidence
Bibliographic record
Abstract
BACKGROUND: While it is recommended that H2 receptor antagonists (H2RAs) be dose reduced in the presence of low glomerular filtration rate (GFR), in practice such adjustments often do not occur. We reviewed the evidence for this recommendation. METHODS: We searched multiple medical reference databases for relevant cohort studies and randomized clinical trials. Studies that enrolled five or more participants with low GFR who also received at least one unadjusted dose of an H2RA, and who were compared with controls were included. Data were abstracted on study and participant characteristics and drug-related adverse effects. Pharmacokinetic measures were pooled using meta-analysis. RESULTS: A total of 22 articles were included, comprising 19 unique cohort studies. With declining GFR, there was a significant increase in the area under the curve (AUC) and elimination half-life (t(1/2)) of the serum drug concentration of H2RAs (P < 0.001). Compared with a GFR >80 ml/min/1.73 m2, drug AUC increased by 200% when the GFR was 30 ml/min/1.73 m2, and by 300% when the GFR was 20 ml/min/1.73 m2. In hospitalized patients with low GFR, reducing the interval dose of intravenous H2RA was associated with fewer adverse reactions. The gastro-protective effects of H2RAs were similar with reduced and unadjusted doses. CONCLUSIONS: Reducing the dose of H2RAs in persons with low GFR will decrease drug expenditure and may prevent adverse events, without a change in efficacy. Quality assurance programmes, which improve deficiencies in H2RAs prescribing, appear justified.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".