POS0296 MYOCARDIAL INFARCTION, STROKE, VENOUS THROMBOEMBOLIC EVENTS, SERIOUS INFECTIONS AND MALIGNANCY IN PATIENTS WITH PSORIATIC ARTHRITIS TREATED WITH TOFACITINIB COMPARED TO BIOLOGIC TREATMENTS IN THE UNITED STATES
Bibliographic record
Abstract
Background: There is limited real-world safety information in patients (pts) with psoriatic arthritis (PsA) treated with tofacitinib vs biologic treatments. Objectives: This observational study examined the risk of inpatient diagnoses of myocardial infarction (MI)/stroke, venous thromboembolic events (VTE), serious infections and malignancy (excluding non-melanoma skin cancer) among PsA pts initiating tofacitinib or biologics, from an adjudicated United States (US) closed medical/pharmacy claims database (Komodo Health). Methods: Pts with PsA aged ≥18 years newly initiating tofacitinib or a biologic (adalimumab, certolizumab pegol, etanercept, golimumab, infliximab [tumour necrosis factor inhibitors (TNFi)]; secukinumab/ixekizumab [interleukin-17 inhibitors (IL-17i)]; risankizumab or ustekinumab) from 15 December 2017–30 September 2022, with ≥12 months of continuous enrolment prior to the index date (date of PsA therapy initiation) were included. New use=no prior use during the baseline period (pts with Janus kinase inhibitor use any time prior to index date were excluded). A pt could be a new user once for each specific drug, but could also be a new user for a second drug class. Stabilised inverse probability treatment weights (sIPTW) were calculated using 17 covariates (demographics/treatment history/comorbidities) in the main analysis, and an additional 53 in a sensitivity analysis to control for additional comorbidities/PsA-related measures/healthcare utilisation variables. Other sensitivity analyses examined the proportion of pts with unadjusted VTE risk factors pre/post-index date. Crude incidence rates (IRs) per 100 pt-years (PYs) were calculated. Cox proportional hazards models with sIPTW were used to calculate adjusted hazard ratios (aHRs) with bootstrapping to calculate 95% confidence intervals (CIs). Results: In total, 48,167 pts were included (tofacitinib, N=3,166; TNFi, N=26,760; IL-17i, N=20,252; risankizumab, N=4,381; ustekinumab, N=4,499) (Table 1). Mean age (all treatments) at index date ranged from 48.2–50.3 years, and mean follow-up was 288.5–347.0 days. At baseline, pts initiating tofacitinib were less likely to have a psoriasis diagnosis vs biologics (60.7% vs 70.9–94.8%). More pts initiating tofacitinib had used ≥3 prior biologics or other advanced treatments (23.7%) vs TNFi (5.4%), IL-17i (8.5%), risankizumab (15.5%) or ustekinumab (12.0%). Baseline systemic corticosteroid use was highest in tofacitinib users (20.8%) vs those using biologics (8.3–16.0%). Pts initiating tofacitinib were less likely to have history of MI/stroke or VTE (6.5–6.6%) vs biologics (7.2–9.4%), or history of malignancy (4.2%) vs biologics (4.5–5.3%) except TNFi (3.0%). Crude IRs per 100 PYs among treatments ranged from 0.27–0.61 for MI/stroke, 0.17–0.42 for VTE, 1.78–2.53 for serious infections and 0.74–1.06 for malignancy (Figure 1). In the main analysis, there were no statistically significant differences in risk of developing MI/stroke, serious infections or malignancy between treatments (Figure 1). There was a significantly decreased risk of VTE with TNFi vs tofacitinib (aHR 0.26 [95% CI 0.14, 0.62]), but not with other biologics. Sensitivity analyses were consistent with the main findings including that more pts on tofacitinib had surgery during the baseline period (45.9–46.7%) and 6 months after the index date (28.1–28.9%) vs biologics (40.2–44.3% and 22.2–27.4%, respectively). Conclusion: In this US claims dataset, there were no significant differences in risk of developing MI/stroke, serious infections or malignancy for tofacitinib vs biologics, among PsA pts not enriched for cardiovascular/VTE risk factors. A decreased risk of VTE with TNFi vs tofacitinib was found, aligning with previous clinical trial data. Limitations included the variable numbers of safety events across treatments and potential uncontrolled confounding of VTE-specific risk factors, such as surgery. 3 REFERENCES: NIL . Acknowledgements: This study was sponsored by Pfizer. Medical writing support, under the direction of the authors, was provided by Kimberley Haines, MSc, CMC Connect, a division of IPG Health Medical Communications, and was funded by Pfizer, New York, NY, USA, in accordance with Good Publication Practice (GPP 2022) guidelines (Ann Intern Med 2022; 175: 1298–1304). Disclosure of Interests: Marina Magrey Consultant: AbbVie, Eli Lilly, Johnson & Johnson, Novartis, Pfizer Inc, UCB, Grant/research support: Amgen, BMS, Milena A Gianfrancesco Shareholder: Pfizer Inc, Employee: Pfizer Inc, Lara Fallon Shareholder: Pfizer Inc, Employee: Pfizer Inc, Arne Yndestad Shareholder: Pfizer Inc, Employee: Pfizer Inc, Ivana Vranic Shareholder: Pfizer Inc, Employee: Pfizer Inc, You-Li Ling Shareholder: Pfizer Inc, Employee: Pfizer Inc, David C Gruben Shareholder: Pfizer Inc, Employee: Pfizer Inc, Jeffrey R Curtis Consultant: Pfizer Inc, Grant/research support: Pfizer Inc. © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.
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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) | 0.000 | 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 | 0.000 | 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".