Nomogram-based prediction model for tocilizumab-induced leukopenia in adults using FAERS and PMDA data.

Authors

Lujing Wang, Xiaochun Zhang, Xinglan Bao, Yan Chen, Hongjian Yuan

Published in

Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas. Volume 59. Pages e15395. Epub Aug 21, 2026.

Abstract

Tocilizumab, an interleukin-6 receptor antagonist used for immune-mediated diseases, is associated with leukopenia, a serious adverse event that increases infection risk. However, no predictive model exists to identify adult patients at high risk for this complication. The aim of this study was to develop and validate a clinical prediction model for tocilizumab-induced leukopenia in adults using real-world pharmacovigilance data. This observational study utilized data from the FDA Adverse Event Reporting System (FAERS) for model development (internal training/test sets; n=5,059) and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA) database for external validation (n=1,338). A nomogram was developed using multivariate logistic regression to identify key predictive factors. Multivariable analysis identified severe COVID-19 (P<0.001), higher tocilizumab dose (P=0.011), younger age (P<0.001), and lower body weight (P<0.001) as independent predictors of leukopenia. The nomogram demonstrated good discriminative ability, with area under the curve (AUC) values of 0.709 (internal training), 0.792 (internal test), and 0.761 (external validation). Calibration and decision curve analysis confirmed the model's robustness and clinical utility. Risk stratification effectively identified high-risk patients. This novel nomogram provides a practical, evidence-based tool for predicting tocilizumab-induced leukopenia in adults. It can assist clinicians in identifying high-risk patients for closer monitoring and personalized management, potentially improving treatment safety.

PMID:
42659457
Bibliographic data and abstract were imported from PubMed on 28 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.