Side effects of acetazolamide: a systematic review and meta-analysis assessing overall risk and dose dependence
Bibliographic record
Abstract
Introduction Acetazolamide (AZM) is used for various conditions (eg, altitude sickness, sleep apnoea, glaucoma), but therapy is often limited by its side effect profile. Our objective was to estimate the risk of commonly reported side effects based on meta-analyses. We hypothesised that these risks are dose-dependent. Methods We queried MEDLINE/EMBASE (Medical Literature Analysis and Retrieval System Online/Excerpta Medica dataBASE) up until 04/10/2019, including any randomised placebo-controlled trial in which adults received oral AZM versus placebo reporting side effects. Eligibility assessment was performed by two independent reviewers. Data were abstracted by one reviewer who verified key entries at a second time point. For side effects reported by > 3 studies a pooled effect estimate was calculated, and heterogeneity assessed via I 2 ; for outcomes reported by > 5 studies effect modification by total daily dose (EMbyTDD; <400 mg/d, 400–600 mg/d, >600 mg/d) was assessed via meta-regression. For pre-specified, primary outcomes (paraesthesias, taste disturbances, polyuria and fatigue) additional subgroup analyses were performed using demographics, intervention details, laboratory changes and risk of bias. Results We included 42 studies in the meta-analyses (N subjects =1274/1211 in AZM/placebo groups). AZM increased the risk of all primary outcomes (p<0.01, I 2 ≤16% and low-to-moderate quality of evidence for all)—the numbers needed to harm (95% CI; n Studies ) for each were: paraesthesias 2.3 (95% CI 2 to 2.7; n=39), dysgeusia 18 (95% CI 10 to 38, n=22), polyuria 17 (95% CI 9 to 49; n=22), fatigue 11 (95% CI 6 to 24; n=14). The risk for paraesthesias (beta=1.8 (95% CI 1.1 to 2.9); P EMbyTDD =0.01) and dysgeusia (beta=3.1 (95% CI 1.2 to 8.2); P EMbyTDD =0.02) increased with higher AZM doses; the risk of fatigue also increased with higher dose but non-significantly (beta=2.6 (95% CI 0.7 to 9.4); P EMbyTDD =0.14). Discussion This comprehensive meta-analysis of low-to-moderate quality evidence defines risk of common AZM side effects and corroborates dose dependence of some side effects. These results may inform clinical decision making and support efforts to establish the lowest effective dose of AZM for various conditions.
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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 | 0.027 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.061 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".