ArticleClinical rheumatology2025
Development and validation of a prediction model for serious infections in rheumatoid arthritis patients treated with tocilizumab in Japan.
Article in Clinical rheumatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesTo develop a prediction model for serious infections (SIs) in rheumatoid arthritis (RA) patients treated with tocilizumab in Japan and to evaluate the model's performance compared to previously developed models, i.e., 'DANBIO' and 'postmarketing surveillance' (PMS).
methodThis non-interventional retrospective cohort study utilized the Medical Data Vision database in Japan. The study population was derived from patients ≥ 18 years with RA who initiated tocilizumab between April 2008 and July 2021. SIs were assessed during the 1-year follow-up from tocilizumab initiation. The candidate predictors were identified based on previous studies, known risk factors, potentially relevant factors, and data availability. The prediction model was developed using logistic regression. The model's performance was compared with previously developed models using cross-entropy and area under the receiver operating characteristic curve (AUC).
resultsOf the 6501 RA patients, 4.57% experienced SIs during the 1-year follow-up. The model included 17 predictors for SI (e.g., age (odds ratio 1.013 (95% confidence interval 1.002-1.024)), history of SIs (2.569 (1.636-3.745)), diverticulitis (2.183 (1.000-3.989))). The model showed a lower cross-entropy and a higher AUC (0.1488; 0.712) compared to DANBIO (0.1932; 0.591) and PMS (0.1561; 0.565) models, and the sensitivity, specificity, positive predictive value, and negative predictive value using 5% threshold were 72%, 64%, 7%, and 98%, respectively.
conclusionsThe model developed in this study seems to have the potential to inform the risk of SIs in RA patients treated with tocilizumab and may help the early identification of patients at risk of SIs to reduce morbidity and mortality.
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