Comorbidities in the Spondyloarthritis GISEA Cohort: an average treatment effect analysis on patients treated with bDMARDs
Bibliographic record
Abstract
OBJECTIVES: We aimed to investigate the effectiveness of tumour necrosis factor inhibitors (TNFi), anti-interleukin-17 or interleukin-12/23 monoclonal antibodies (anti-IL) on comorbidities in a cohort of patients with spondyloarthritis (SpA), using an average treatment effect (ATE) analysis. METHODS: SpA patients from the multicentre Italian GISEA Registry were divided into groups according to pharmacological exposure: no treatment (G0), TNFi (G1) and non-responders to TNFi switched to anti-IL (G2). In each group, we recorded the prevalence and incidence of infectious, cardiopulmonary, endocrinological, gastrointestinal, oncologic, renal and neurologic comorbidities. Each comorbidity was then fitted for ATE and baseline features were evaluated for importance. RESULTS: The main findings of this study comprising 4458 SpA patients relate to cancer, other gastrointestinal diseases (OGID) and fibromyalgia. ATE showed no increased risk of solid cancer in G1 (0.42 95% CI 0.20-0.85) and G2 (0.26 95% CI 0.08-0.71) vs. G0, with significantly higher incidence in G0 (14.07/1000 patient-years, p=0.0001). Conversely, a significantly higher risk of OGID and fibromyalgia was found in G1 (1.56 95% CI 1.06-2.33; 1.69 95% CI 1.05-2.68, respectively) and G2 (1.91 95% CI 1.05-3.24; 2.13 95% CI 1.14-3.41, respectively) vs. G0. No treatment risk reduction was observed in haematological malignancies, cardiovascular events and endocrinological comorbidities. CONCLUSIONS: Overall, our study confirms the safety of TNFi and anti-IL in SpA patients, albeit with some caveats pertaining to solid cancers, OGID and fibromyalgia. Furthermore, taking into consideration causality with observational data may yield more reliable and relevant clinical information.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".