The Association of Tumor Necrosis Factor Inhibitor Use With Incident Hypertension in Ankylosing Spondylitis: Data From the PSOAS Cohort
Bibliographic record
Abstract
OBJECTIVE: Individuals with ankylosing spondylitis (AS) have a greater cardiovascular (CV) risk than those in the general population. The effect of tumor necrosis factor inhibitors (TNFis) on CV risk, including on the development of hypertension (HTN), remains unclear, with some data suggesting higher risk. We assessed the association of TNFi use with incident HTN in a longitudinal AS cohort. METHODS: Adults with AS enrolled in a prospective cohort in 2002-2018 were examined every 4-6 months. TNFi use during the preceding 6 months was ascertained at each study visit. We defined HTN by patient-reported HTN, antihypertensive medication use, or, on 2 consecutive visits, systolic blood pressure (BP) ≥ 140 mmHg or diastolic BP ≥ 90 mmHg. We evaluated the association between TNFi use and the development of HTN with marginal structural models, estimated by inverse probability-of-treatment weighting, to account for time-dependent confounders and informative censoring. Potential confounders included age, sex, race, site, nonsteroidal antiinflammatory drug use, and disease activity. RESULTS: We included 630 patients without baseline HTN and with at least 1 year of follow-up. Of these, 72% were male, mean age was 39 ± 13 years, and 43% used TNFi at baseline. On follow-up (median 5 yrs), 129 developed incident HTN and 163 started on TNFi during follow-up. TNFi use was not associated with incident HTN (adjusted HR 1.10, 95% CI 0.83-1.37). CONCLUSION: In our prospective AS cohort, TNFi use was not significantly associated with incident HTN.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".