An international comparison of spontaneous adverse event reports and potentially inappropriate medicine use associated with dabigatran
Bibliographic record
Abstract
PURPOSE: The objective of this study was to analyse spontaneous adverse event (SAE) reports associated with the oral anticoagulant dabigatran from Australia, Canada and USA and to examine concomitant medicine use. METHODS: Spontaneous adverse event national databases from Australia, Canada and the USA were used to examine all reports of adverse events associated with dabigatran from 1st August 2005 to 31st March 2013. Disproportionality analysis was conducted for the quantitative detection of signals using the USA database. Concomitant medicine use was examined to identify potentially inappropriate medicines, which may place the patient at increased risk for adverse events. RESULTS: There were a total of 1039, 1333 and 13 788 SAE reports associated with dabigatran from Australia, Canada and USA, respectively. Gastrointestinal (GI) disorders were the most commonly reported adverse event, ranging from 27.5% for Australia and up to 40.5% for USA. Of these, GI haemorrhage accounted for 81.5% of Australian, 71.5% of Canadian and 42% of the USA adverse event reports for GI disorders. Positive signals were confirmed in the USA data (GI haemorrhage; PRR 18.18, χ2 40993.51 and ROR 19.55 95% CI 18.77-20.36). Use of concomitant medicines with the potential to increase bleeding risk across all three countries ranged from 34.1% for Australia to 51.1% for the USA. CONCLUSIONS: A large proportion of adverse events were associated with concomitant therapies, which may have placed the patient at increased risk of harm. This highlights the need for pharmacovigilance by the prescribing clinician to minimise risk and ensure the safe and effective integration of dabigatran into routine clinical practice.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".