Biological Drug Treatment of Rheumatoid Arthritis and Spondyloarthritis: Effects on QT Interval and QT Dispersion
Bibliographic record
Abstract
OBJECTIVE: Tumor necrosis factor-α (TNF-α) antagonists bring about significant improvement in chronic inflammatory diseases such as rheumatoid arthritis (RA) and spondyloarthritis (SpA). There is some evidence that they can also have negative myocardial effects, but to date this issue has not been clarified. We evaluated changes in electrocardiographic measures [QT interval, corrected, dispersion, and dispersion corrected (QT, QTc, QTd, QTdc, respectively)] in patients with RA or SpA treated with anti-TNF agents (infliximab and etanercept), those treated with other biological agents (rituximab), and with methotrexate. METHODS: We studied 38 consecutive patients with RA (21 patients) or SpA (19 patients) being treated with TNF-α antagonists, 8 patients with RA being treated with rituximab, and 13 patients (8 with RA and 5 with SpA) taking methotrexate. Electrocardiographs (ECG) were performed on all participants at baseline and 12 months after initiation of treatment, and the QT, QTc, and QTd were calculated with standard procedures. RESULTS: After 12 months of treatment, significant increases over baseline values were observed in the mean QT (p < 0.009), QTd (p < 0.0001), and QTdc (p < 0.0001) of the anti-TNF group, but no significant changes were observed in those taking rituximab. QT changes in the anti-TNF group were unrelated to the disease (RA vs SpA) or drug (infliximab vs etanercept), and none were associated with clinical manifestations of cardiac disease. CONCLUSION: In patients with RA and SpA, TNF-α antagonists seem to increase the QT and QTd measures. Although these changes were completely asymptomatic, ECG may be indicated in patients being considered for anti-TNF therapy to identify those at risk for cardiac complications.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".