ArticleBMC pharmacology & toxicology2025
Characteristics, risk factors and a risk prediction model of tocilizumab-induced hypofibrinogenemia: a retrospective real-world study of inpatients.
Article in BMC pharmacology & toxicology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A nomogram based on LASSO-logistic regression for predicting tocilizumab-associated hypofibrinogenemia in patients with rheumatic diseases.Frontiers in pharmacology · 2026Article
- A prediction nomogram for sodium-glucose co-transporter 2 inhibitors-induced diabetes ketoacidosis in non-surgical patients: a retrospective multicenter cohort study.Frontiers in endocrinology · 2026Article
- Incidence and risk factors of tocilizumab-induced hypofibrinogenemia in patients with thyroid eye disease: a single-center retrospective study.Frontiers in endocrinology · 2026Article
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6 authors.
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Abstract
objectiveThe occurrence of hypofibrinogenemia after tocilizumab treatment has attracted increasing attention, which may cause bleeding and even life-threatening. This study aims to explore the risk factors for tocilizumab-induced hypofibrinogenemia (T-HFIB) and construct a risk prediction model.
methodsA total of 221 inpatients that received tocilizumab from 2015 to 2023 were retrospectively collected and divided into T-HFIB group or control group. The risk factors for T-HFIB were obtained by logistic regression equation and used to establish the nomogram.
resultsT-HFIB was observed in 121 of 221 patients (54.75%). Multifactorial logistic regression analysis revealed that infection (OR = 2.002, 95%CI:1.018 ~ 3.935), COVID-19 (OR = 3.752, 95%CI:1.264 ~ 11.139), CAR-T therapy (OR = 4.409, 95%CI:2.017 ~ 0.894), and concomitant glucocorticoids (OR = 5.303, 95%CI:0.227 ~ 0.894) were identified as independent risk factors for T-HFIB, while high baseline fibrinogen level (OR = 0.813, 95%CI:0.670 ~ 0.988) and concomitant antirheumatic drugs (OR = 0.451, 95%CI:0.227 ~ 0.894) were identified as protective factors. A nomogram was established, and area under the curve (AUC) of prediction model was 0.772 (95%CI:0.709 ~ 0.836). Calibration curve showed a good prediction accuracy for the occurrence of T-HFIB.
conclusionThe infection, COVID-19, CAR-T therapy, and concomitant glucocorticoids were independent risk factors for T-HFIB, while high baseline fibrinogen and concomitant antirheumatic drugs were protective factors. This nomogram can help early identify the patients at potential high risk of developing T-HFIB.
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