Hydroxychloroquine Use and Cardiovascular Events Among Patients With Systemic Lupus Erythematosus and Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: We evaluated the potential temporal association between hydroxychloroquine (HCQ) use and cardiovascular (CV) events among patients with systemic lupus erythematosus (SLE) or rheumatoid arthritis (RA). METHODS: We conducted a nested case-control study within inception cohorts of SLE and RA patients using administrative health databases including the entire population of British Columbia, Canada. We identified cases with incident CV events, including myocardial infarction (MI), stroke, or venous thromboembolism (VTE). We matched each case with up to 3 controls on age, sex, and rheumatic disease. HCQ exposure was categorized by the time between the last HCQ prescription date covered and the index date as current use, recent use, remote use, or never used. We used conditional logistic regression to assess the association between HCQ exposure and CV events, using remote use as the reference group. RESULTS: We identified 10,268 cases and 29,969 controls. Adjusted conditional odd ratios (cORs) and 95% confidence intervals (95% CIs) for current HCQ use relative to remote use were 0.86 (0.77-0.97) for combined CV events, 0.88 (0.74-1.05) for MI, 0.87 (0.74-1.03) for stroke, and 0.74 (0.59-0.94) for VTE. Recent HCQ users and nonusers had similar odds of combined CV events as remote users (cORs 0.93, 95% CI 0.77-1.13 and 0.96, 95% CI 0.88-1.04, respectively). CONCLUSION: In this nested case-control study of patients with SLE and RA, we found a reduced risk of overall CV events associated with current HCQ use, including reductions in VTE and trends toward reductions in MI and stroke. These findings suggest a possible cardiovascular preventative benefit of HCQ use.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".