Safety of Janus Kinase Inhibitors: A Real-World Multicenter Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Oral Janus kinase inhibitors (JAKis) represent an effective strategy for rheumatoid arthritis (RA) treatment. A previous study supported that tofacitinib (TOF) is associated with higher incidence of cardiovascular (CV) and neoplastic events compared to tumor necrosis factor inhibitors. Given the apparent discrepancy between these data and real-world experience, we aimed to investigate the safety and efficacy of the available JAKis in a multicenter cohort. METHODS: We retrospectively evaluated patients with RA who ever received 1 JAKi (TOF, baricitinib [BAR], upadactinib [UPA], filgotinib [FIL]) from 4 tertiary care centers in Milan, Italy. Outcomes related to JAKi safety were recorded, particularly major CV events as well as adverse events of special interest (AESIs), which included serious infections, opportunistic infections, venous thromboembolism, herpes zoster infections, liver injury, malignancies, and deaths; retention rates were also calculated. Further analyses included patients fulfilling the risk factors suggested to influence TOF safety. RESULTS: Six hundred eighty-five patients were included and received BAR (48%), TOF (31%), UPA (14%), or FIL (7%) as first-line innovative treatment prior to a biologic. Of a total of 1137 patient-years of observation, we recorded 1 stroke and 123 (18%) AESIs, including 3 deaths, all a result of severe infections. Among patients with a higher CV risk, we observed a higher frequency of AESIs (23%). CONCLUSION: Our real-world data confirm that JAKis are effective and carry a low risk of AESIs, especially in patients who do not display CV risk factors at baseline. Our study could not identify differences between JAKis. Different safety profiles should be defined in larger prospective cohorts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".