Heart disease and the risk of allopurinol-associated severe cutaneous adverse reactions: a general population–based cohort study
Bibliographic record
Abstract
BACKGROUND: Allopurinol is commonly prescribed for gout, and its clinical use may expand with ongoing trials assessing its potential cardiorenal benefits. Because heart disease has been suggested to be a risk factor for allopurinol-associated severe cutaneous adverse reactions, we sought to confirm this association in a Canadian general population cohort. METHODS: We used population data from British Columbia, Canada, to identify all incident allopurinol users between 1997 and 2015. We examined the association between heart disease (ischemic heart disease and heart failure) and the risk of hospital admission for severe cutaneous adverse reactions, adjusting for known and purported risk factors. We also evaluated the joint effects of combined clinical and demographic risk factors. RESULTS: Among 130 325 allopurinol initiators, 109 hospital admissions occurred for allopurinol-associated severe cutaneous adverse reactions. The multivariable relative risk among those with heart disease was 1.55 (95% confidence interval 1.01-2.37). Patients with heart disease and chronic kidney disease who were started on an allopurinol dosage of greater than 100 mg/d had an 11-fold higher risk. Allopurinol initiation at a lower dosage among patients with heart disease and chronic kidney disease resulted in a fivefold reduction in risk. Older women with heart disease from regions with large Asian populations had a 23-fold higher risk of allopurinol-associated severe cutaneous adverse reactions than younger men without heart disease from other regions. INTERPRETATION: Heart disease is independently associated with risk of allopurinol-associated severe cutaneous adverse reactions, similar to chronic kidney disease, and low-dosage allopurinol initiation may substantially mitigate this risk. Risk factors for these rare but serious reactions should be considered when initiating allopurinol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".