Development and external validation of prediction models for adverse health outcomes in rheumatoid arthritis: A multinational real-world cohort analysis

Cynthia Yang*, Ross D. Williams, Joel N. Swerdel, João Rafael Almeida, Emily S. Brouwer, Edward Burn, Loreto Carmona, Katerina Chatzidionysiou, Talita Duarte-Salles, Walid Fakhouri, Antje Hottgenroth, Meghna Jani, Raivo Kolde, Jan A. Kors, Lembe Kullamaa, Jennifer Lane, Karine Marinier, Alexander Michel, Henry Morgan Stewart, Albert Prats-UribeSulev Reisberg, Anthony G. Sena, Carmen O. Torre, Katia Verhamme, David Vizcaya, James Weaver, Patrick Ryan, Daniel Prieto-Alhambra, Peter R. Rijnbeek

*Corresponding author for this work

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Abstract

Background: Identification of rheumatoid arthritis (RA) patients at high risk of adverse health outcomes remains a major challenge. We aimed to develop and validate prediction models for a variety of adverse health outcomes in RA patients initiating first-line methotrexate (MTX) monotherapy. Methods: Data from 15 claims and electronic health record databases across 9 countries were used. Models were developed and internally validated on Optum® De-identified Clinformatics® Data Mart Database using L1-regularized logistic regression to estimate the risk of adverse health outcomes within 3 months (leukopenia, pancytopenia, infection), 2 years (myocardial infarction (MI) and stroke), and 5 years (cancers [colorectal, breast, uterine] after treatment initiation. Candidate predictors included demographic variables and past medical history. Models were externally validated on all other databases. Performance was assessed using the area under the receiver operator characteristic curve (AUC) and calibration plots. Findings: Models were developed and internally validated on 21,547 RA patients and externally validated on 131,928 RA patients. Models for serious infection (AUC: internal 0.74, external ranging from 0.62 to 0.83), MI (AUC: internal 0.76, external ranging from 0.56 to 0.82), and stroke (AUC: internal 0.77, external ranging from 0.63 to 0.95), showed good discrimination and adequate calibration. Models for the other outcomes showed modest internal discrimination (AUC < 0.65) and were not externally validated. Interpretation: We developed and validated prediction models for a variety of adverse health outcomes in RA patients initiating first-line MTX monotherapy. Final models for serious infection, MI, and stroke demonstrated good performance across multiple databases and can be studied for clinical use. Funding: This activity under the European Health Data & Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA.

Original languageEnglish
Article number152050
JournalSeminars in Arthritis and Rheumatism
Volume56
DOIs
Publication statusPublished - Oct 2022

Bibliographical note

Funding Information:
This activity under the European Health Data & Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 806968 . This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA. The sponsor of the study did not have any involvement in the writing of the manuscript or the decision to submit it for publication. The research was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). DPA is funded by a National Institute for Health Research Senior Research Fellowship. TDS is funded by the Department of Health of the Generalitat de Catalunya under the Strategic Plan for Research and Innovation in Health (PERIS; SLT002/16/00308 ). RK was supported by the European Social Fund via the IT Academy programme. SR was funded by Estonian Research Council grants PRG1095 and RITA1/02-96-11 . The views expressed in this publication are those of the authors and not those of their respective institutions. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit it for publication.

Funding Information:
CY, RDW, JRA, EB, TDS, MJ, RK, JAK, LK, APU, SR, HMS, and COT report no competing interests related to this work. JNS, AGS, JW, and PR are employees of Janssen Research & Development, a pharmaceutical company of Johnson & Johnson, and shareholders of Johnson & Johnson. At the time the study was conducted, ESB was an employee of Janssen Research & Development, a pharmaceutical company of Johnson & Johnson, shareholder of Johnson & Johnson, and shareholder of Takeda Pharmaceuticals. LC reports her institute has been hired for methodological consultancy by AbbVie Spain, S.L.U., Astellas Pharma, SA, Bristol-Myers Squibb, S.A.U. (BMS), Daiichi-Sankyo España, S.A., Dentsply Sirona Iberia, S.A.U., Eisai Farmacéutica, SA, Fresenius Kabi España, S. A. U., Laboratorios Gebro Pharma, SA, Lilly, S.A., Merck Sharp & Dohme España, S.A., Novartis Farmaceutica, SA, Pfizer, S.L.U., Roche Farma, S.A, Sanofi Aventis, UCB Pharma, S.A., outside the submitted work. KC reports consultancy fees from Eli Lilly, AbbVie, and Pfizer, outside the submitted work. WF is an employee and shareholder of Eli Lilly. AHO is an employee of Lilly Deutschland GmbH. JL reports grants from Versus Arthritis, grants from Medical Research Council, outside the submitted work. AM is an employee of Bayer AG. At the time the study was conducted, KM was an employee of Servier. KV works for a research institute which receives/received unconditional research grants from Yamanouchi, Pfizer/Boehringer Ingelheim, Novartis, GSK, UCB, Amgen, Chiesi, none of these are related to the content of this paper. DV reports personal fees from Bayer, during the conduct of the study and outside the submitted work. DPA reports grants and other (DPA's department has received fees for speaker services and advisory board membership) from AMGEN, grants, non-financial support and other (DPA's department has received fees for consultancy services) from UCB Biopharma, grants from Les Laboratoires Servier, outside the submitted work; and Janssen, on behalf of IMI-funded EHDEN and EMIF consortiums, and Synapse Management Partners have supported training programmes organised by DPA's department and open for external participants. PRR works for a research institute who receives/received unconditional research grants from Yamanouchi, Pfizer-Boehringer Ingelheim, GSK, Amgen, UCB, Novartis, AstraZeneca, Chiesi, Janssen Research and Development, none of which relate to the content of this work.

Publisher Copyright:
© 2022 The Author(s).

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