Biomarkers to predict risk of venous thromboembolism in patients with rheumatoid arthritis receiving tofacitinib or tumour necrosis factor inhibitors
Bibliographic record
Abstract
OBJECTIVE: In the ORAL (Oral Rheumatoid Arthritis triaL) Surveillance study of patients with rheumatoid arthritis aged ≥50 years with ≥1 additional cardiovascular risk factor, incidence of pulmonary embolism was higher with tofacitinib 10 mg two times per day than with tumour necrosis factor inhibitors (TNFi). This exploratory post hoc analysis examined whether biomarkers explained the associations of tofacitinib versus TNFi with venous thromboembolism (VTE). METHODS: ORAL Surveillance was a prospective, open-label, event-driven, non-inferiority, postauthorisation safety study. Patients were randomised 1:1:1 to receive tofacitinib 5 mg or 10 mg two times per day or a TNFi. For this analysis, 294 soluble, proteomic, genetic and antibody biomarkers (of which 79 had a known role in inflammation, coagulation, vascular biology or Janus kinase signalling) were quantified in serum collected at baseline, month 12 and study end. RESULTS: Overall, 4362 patients were randomised and treated. The exploratory biomarker data set included 285 patients (57 VTE cases; 228 matched controls). D-dimer was quantified in 3732 patients (54 VTE cases; 3678 controls). No biomarker demonstrated a clear mechanistic association with the increased risk of VTE for tofacitinib versus TNFi. Month 12 D-dimer levels were positively associated with risk of a subsequent VTE within the tofacitinib 5 mg and 10 mg two times per day arms. CONCLUSIONS: Overall, this post hoc analysis did not identify biomarkers that explained the increased VTE risk for tofacitinib versus TNFi. Individual VTE risk should be considered when making decisions about initiation or maintenance of tofacitinib treatment. TRIAL REGISTRATION NUMBER: NCT02092467; ClinicalTrials.gov.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".