The Relationship Between Cardiac Conduction Times, Cardiovascular Risk Factors, and Inflammation in Patients with Early Arthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate the prevalence of conduction disorders in patients with early arthritis and the relationship with inflammation and traditional cardiovascular (CV) risk factors. METHODS: Patients with rheumatoid arthritis (RA) have a 2-fold higher risk of sudden cardiac death, possibly owing to conduction disorders. This increased risk might already be present at the clinical onset of arthritis. Therefore, we assessed electrocardiography, blood pressure, 28-joint Disease Activity Score (DAS28), lipid profile, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) level in 480 patients with early arthritis at baseline and after 1 year. RESULTS: The prevalence of conduction disorders was 12.5%. Conduction times at baseline were not associated with DAS28, ESR, or CRP levels and did not change during antirheumatic treatment. Baseline and the improvement in DAS28 (European League Against Rheumatism response), ESR, and CRP were significantly associated with heart rate, lipid profile, and blood pressure. Elevated total cholesterol and blood pressure were associated with an increased QRS time. The change in heart rate differed 7.3 bpm between patients with the least versus largest DAS improvement. CONCLUSION: The prevalence of conduction disorders in patients with early arthritis was 12.5%, which is similar to the general population and was not associated with changes in inflammation markers. However, a high cholesterol was associated with a prolonged QRS time. Therefore, the emphasis of CV risk management in arthritis should not be only on treatment of disease activity but also on traditional CV risk factors. The relationship between the improvement in disease activity and heart rate is remarkable because this could imply a 10-year CV mortality risk difference of 24%.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".