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

Autor: 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-Uribe, Sulev Reisberg, Anthony G. Sena, Carmen O. Torre, Katia Verhamme, David Vizcaya, James Weaver, Patrick Ryan, Daniel Prieto-Alhambra, Peter R. Rijnbeek
Přispěvatelé: Medical Informatics
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Seminars in Arthritis and Rheumatism, 56:152050. W.B. Saunders
Semin Arthritis Rheum
ISSN: 0049-0172
Popis: 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 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 Under- taking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.
Databáze: OpenAIRE
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