Venous thromboembolism in patients with gout and the impact of hospital admission, disease duration and urate-lowering therapy
Bibliographic record
Abstract
BACKGROUND: Systemic inflammatory diseases have been associated with increased risk of venous thromboembolism. We aimed to quantify the risk of venous thromboembolism in patients with gout, the most common inflammatory arthritis, and to assess how disease duration, hospital admission and urate-lowering therapy affect this risk. METHODS: We used data from the population-representative, England-based Clinical Practice Research Datalink linked to Hospital Episode Statistics, to identify incident gout cases between 1998 and 2017. We matched cases individually to 1 control without gout on age, gender, general practice and follow-up time. We calculated absolute and relative risks of venous thromboembolism, stratified by age, gender and hospital admission. Among those with gout, we assessed the risk of venous thromboembolism by exposure to urate-lowering therapy. RESULTS: We identified 62 234 patients with incident gout matched to 62 234 controls. Gout was associated with higher risk of venous thromboembolism compared with controls (absolute rate 37.3 [95% confidence interval (CI) 35.5-39.3] v. 27.0 [95% CI 25.5-28.9] per 10 000 person-years, adjusted hazard ratio [HR] 1.25, 95% CI 1.15-1.35). The excess risk in patients with gout, which was sustained up to a decade after diagnosis, was present during the time outside hospital stay (adjusted HR 1.30, 95% CI 1.18-1.42), but not during it (adjusted HR 1.01, 95% CI 0.83-1.24). The risk of venous thromboembolism was similar among patients prescribed versus not prescribed urate-lowering therapy (incidence rate ratio 1.04, 95% CI 0.89-1.23). INTERPRETATION: Gout was associated with higher risk of venous thromboembolism, particularly when the patient was not in hospital and regardless of exposure to urate-lowering therapy. Although the observed excess risk may not be sufficient to warrant preventive intervention, clinical vigilance may be required when caring for these patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".