Trends of venous thromboembolism risk before and after diagnosis of gout : a population-based study
Bibliographic record
Abstract
Background: Previous studies have shown that gout is an independent risk factor for cardiovascular diseases. Venous thromboembolism (VTE, including deep venous thrombosis [DVT] and pulmonary embolism [PE]) represents the third most common form of cardiovascular disease among the general population. However, data on the risk of VTE in gout patients are scarce. Objectives: 1) To estimate the overall risk of VTE, DVT, and PE before and after gout diagnosis in an incident cohort of individuals with gout; 2) To access the temporal trend of VTE, DVT, and PE before and after gout diagnosis compared with the general population. Methods: I conducted a 1:1 matched cohort study using a province-wide population-based administrative health database from British Columbia, Canada. I calculated incidence rate ratios and multivariable adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) for the risk of VTE, DVT, and PE before and after gout diagnosis. Results: Among 124,306 individuals with newly diagnosed gout (65% male, mean age 60 years), VTE developed in 1,594 patients, DVT in 989 patients, and PE in 813 patients. Incidence rates were 2.44, 1.51, and 1.24 per 1,000 person-years, respectively. The corresponding incidence rates among non-gout individuals were 1.37, 0.83, and 0.75 per 1,000 person-years, respectively. The final aHRs (95% CI) for VTE, DVT, and PE were 1.34 (1.23-1.46), 1.38 (1.24-1.54), and 1.27 (1.13-1.42), respectively. For the entire pre-gout period, compared to general population, the final aHRs (95% CI) were 1.56 (1.41-1.71), 1.55 (1.38-1.75) and 1.53 (1.34-1.76) for VTE, DVT and PE, respectively. During the 3rd, 2nd, and 1st years preceding the gout diagnosis, the final aHRs for VTE were 1.51, 1.61, and 1.74, respectively. During the 1st, 2nd, 3rd, 4th, and 5th years after the gout diagnosis, the final aHRs were 1.46, 1.44, 1.37, 1.36, and 1.32. Similar trends were also seen for DVT and PE. Conclusion: Increased risks of VTE, DVT, and PE were found both before and after gout diagnosis. The risk increased gradually before gout diagnosis, peaking in the year prior to gout diagnosis, and then progressively declined following the diagnosis. Gout associated inflammation may contribute to VTE risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".