Treatment-emergent major adverse cardiovascular and thromboembolic events were infrequent during clinical trials of pegloticase
Bibliographic record
Abstract
OBJECTIVES: Long-term maintenance of serum urate levels <6 mg/dl reduces gout flare frequency. However, urate-lowering therapy (ULT) initiation can induce gout flare. The incidence of thromboembolic (TE) and cardiovascular (CV) events has been shown to increase in the 30 and 120 days following gout flare, respectively; therefore, the question of ULT initiation increasing patient risk for CV/TE events has been raised. Here, we investigate CV/TE event incidence following pegloticase initiation in clinical trials. METHODS: This post hoc analysis of pooled data from four trials examined treatment-emergent gout flare and CV/TE events in patients with uncontrolled gout. Studies included two phase 3 trials (NCT00325195), the MIRROR open-label trial (NCT03635957), and the MIRROR randomized controlled trial (NCT03994731). Per protocol, pegloticase (8 mg) was administered every 2 (all trials) or 4 weeks (phase 3 trials); data from the first 24 weeks of therapy were included in this analysis. Some MIRROR patients received MTX (15 mg/week) as co-therapy. Based on prior studies, the high-risk window for CV/TE events was defined as 120 days following flare onset. RESULTS: Overall, 5/328 (1.5%) patients experienced ≥1 CV/TE event during pegloticase treatment, including 3/244 (1.2%) patients who received on-label (biweekly) dosing (35.4 events/1000 person-years). All events occurred within the 120-day gout flare exposure window. CONCLUSIONS: CV/TE event incidence during pegloticase treatment was similar to the general gout population (31.7 events/1000 person-years). These findings suggest that pegloticase initiation does not put patients at a higher risk for CV/TE events.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".