Cardiovascular adverse events of tocilizumab and baricitinib in COVID-19 and rheumatoid arthritis

Junmo Moon; Chaemin Lee; Seoyeong Lee; Meehoh Kim; Xuerong Wen; Francesco Branda; Jeongseon Oh; Yejun Son; Kyeongmin Lee; Taelim Choi; Dong Keon Yon

Abstract: ® Cardiovascular adverse events of tocilizumab and baricitinib in COVID-19 and rheumatoid arthritis A real-world disproportionality and machine learning analysis Junmo Moon, BS a,b , Chaemin Lee, BS a,c , Seoyeong Lee, BS a,d , Meehoh Kim, PhD a , Xuerong Wen, PhD e , Francesco Branda, PhD f , Jeongseon Oh, MS a,g , Yejun Son, PhD a,g , Kyeongmin Lee, MS a,g , Taelim Choi, MS a,g , Dong Keon Yon, MD a,g,h, * Abstract Tocilizumab and baricitinib are used in rheumatoid arthritis (RA) and COVID-19, where acute hyperinflammation and systemic stress may amplify cardiovascular risk. Comparative evidence on indication-specific cardiovascular signals remains scarce; we therefore assessed these signals by indication. We analyzed the FDA Adverse Event Reporting System data from January 2010 to June 2025 to compare cardiovascular adverse events (CVAEs) associated with baricitinib and tocilizumab. Primary outcomes included 6 CVAE categories: cardiac arrhythmias, central nervous system vascular disorders, embolic and thrombotic events, ischemic heart disease, noninfectious myocarditis/pericarditis, and other cardiac disorders. Disproportionality was assessed using the information component (IC) and reporting odds ratio (ROR), stratified by pre- and post-COVID-19 Emergency Use Authorization (EUA) periods and indications (COVID-19 vs RA). Machine learning models were additionally trained to characterize risk patterns for each drug, with feature contributions interpreted using SHapley Additive exPlanations. Among 17,750,053 reports, 5994 CVAEs were identified (baricitinib, n = 965; tocilizumab, n = 5029). Disproportionality was higher in the post-EUA than the pre-EUA period for both baricitinib (IC, 0.84 [IC 025 , 0.59] to 1.51 [1.39]; ROR, 1.89 [95% confidence interval, 1.62-2.21] to 3.22 [2.98-3.48]) and tocilizumab (0.22 [0.16] to 0.54 [0.47]; 1.18 [1.14-1.23] to 1.50 [1.43-1.57]). Baricitinib for COVID-19 showed the highest disproportionality (IC, 2.90 [IC 025 , 2.70]; ROR, 11.95 [95% confidence interval, 10.33-13.83]). Gradient boosting machine achieved the highest AUROC (baricitinib, 0.728; tocilizumab, 0.702), and SHapley Additive exPlanations highlighted COVID-19 indication for baricitinib and RA for tocilizumab. Our findings suggest indicationdependent cardiovascular signals when immunomodulators are repurposed for acute viral infections, underscoring the need for context-aware evaluation in drug repurposing. Abbreviations: AUROC = area under the receiver operating characteristic curve, CI = confidence interval, CVAEs = cardiovascular adverse events, EUA = Emergency Use Authorizations, FAERS = FDA Adverse Event Reporting System, FDA = Food and Drug Administration, GBM = gradient boosting machine, IC = information component, ML = machine learning, PRIMARYID = primary identification number, RA = rheumatoid arthritis, ROR = reporting odds ratio, SHAP = SHapley Additive exPlanations. Keywords: baricitinib, FAERS, machine learning, pharmacovigilance, tocilizumab JM, CL, and SL contributed to this article equally. KL, TC, and DKY contributed to this article equally. This research was supported by the Ministry of Science and ICT (RS-202400509257 and IITP-2026-RS-2024-00438239) and the Ministry of Health & Welfare (RS-2025-02220492), Republic of Korea. The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The authors have no conflicts of..

DOI record: { "DOI": "10.1097/md.0000000000050993", "ISSN": [ "0025-7974", "1536-5964" ], "URL": "http://dx.doi.org/10.1097/md.0000000000050993", "abstract": "\n \n \n Tocilizumab and baricitinib are used in rheumatoid arthritis (RA) and COVID-19, where acute hyperinflammation and systemic stress may amplify cardiovascular risk. Comparative evidence on indication-specific cardiovascular signals remains scarce; we therefore assessed these signals by indication. We analyzed the FDA Adverse Event Reporting System data from January 2010 to June 2025 to compare cardiovascular adverse events (CVAEs) associated with baricitinib and tocilizumab. Primary outcomes included 6 CVAE categories: cardiac arrhythmias, central nervous system vascular disorders, embolic and thrombotic events, ischemic heart disease, noninfectious myocarditis/pericarditis, and other cardiac disorders. Disproportionality was assessed using the information component (IC) and reporting odds ratio (ROR), stratified by pre- and post-COVID-19 Emergency Use Authorization (EUA) periods and indications (COVID-19 vs RA). Machine learning models were additionally trained to characterize risk patterns for each drug, with feature contributions interpreted using SHapley Additive exPlanations. Among 17,750,053 reports, 5994 CVAEs were identified (baricitinib, n = 965; tocilizumab, n = 5029). Disproportionality was higher in the post-EUA than the pre-EUA period for both baricitinib (IC, 0.84 [IC\n 025\n , 0.59] to 1.51 [1.39]; ROR, 1.89 [95% confidence interval, 1.62–2.21] to 3.22 [2.98–3.48]) and tocilizumab (0.22 [0.16] to 0.54 [0.47]; 1.18 [1.14–1.23] to 1.50 [1.43–1.57]). Baricitinib for COVID-19 showed the highest disproportionality (IC, 2.90 [IC\n 025\n , 2.70]; ROR, 11.95 [95% confidence interval, 10.33–13.83]). Gradient boosting machine achieved the highest AUROC (baricitinib, 0.728; tocilizumab, 0.702), and SHapley Additive exPlanations highlighted COVID-19 indication for baricitinib and RA for tocilizumab. Our findings suggest indication-dependent cardiovascular signals when immunomodulators are repurposed for acute viral infections, underscoring the need for context-aware evaluation in drug repurposing.\n \n ", "assertion": [ { "group": { "label": "Publication History", "name": "publication_history" }, "label": "Received", "name": "received", "value": "2026-03-27" }, { "group": { "label": "Publication History", "name": "publication_history" }, "label": "Accepted", "name": "accepted", "value": "2026-09-17" } ], "author": [ { "affiliation": [ { "name": "Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea" }, { "name": "Department of Biology, Kyung Hee University, Seoul, South Korea" } ], "family": "Moon", "given": "Junmo", "role": [ { "role": "author", "vocabulary": "crossref" } ], "sequence": "first" }, { "affiliation": [ { "name": "Center for Digital Health, Medical Science Research Institute, Kyung Hee University 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"Department of Pharmacy Practice and Clinical Research, College of Pharmacy, University of Rhode Island, Kingston, RI" } ], "family": "Wen", "given": "Xuerong", "role": [ { "role": "author", "vocabulary": "crossref" } ], "sequence": "additional" }, { "affiliation": [ { "name": "Unit of Medical Statistics and Molecular Epidemiology, Università Campus Bio-Medico di Roma, Rome, Italy" } ], "family": "Branda", "given": "Francesco", "role": [ { "role": "author", "vocabulary": "crossref" } ], "sequence": "additional" }, { "affiliation": [ { "name": "Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea" }, { "name": "Department of Precision Medicine, Kyung Hee University School of Medicine, Seoul, South Korea" } ], "family": "Oh", "given": "Jeongseon", "role": [ { "role": "author", "vocabulary": "crossref" } ], "sequence": "additional" }, { "affiliation": [ { "name": "Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea" }, { "name": "Department of Precision Medicine, Kyung Hee University School of Medicine, Seoul, South Korea" } ], "family": "Son", "given": "Yejun", "role": [ { "role": "author", "vocabulary": "crossref" } ], "sequence": "additional" }, { "affiliation": [ { "name": "Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea" }, { "name": "Department of Precision Medicine, Kyung Hee University School of Medicine, Seoul, South Korea" } ], "family": "Lee", "given": "Kyeongmin", "role": [ { "role": "author", "vocabulary": "crossref" } ], "sequence": "additional" }, { "affiliation": [ { "name": "Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea" }, { "name": "Department of Precision Medicine, Kyung Hee University School of Medicine, Seoul, South Korea" } ], "family": "Choi", 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