Cynthia Yang1, Ross D Williams2, Joel N Swerdel3, João Rafael Almeida4, Emily S Brouwer3, Edward Burn5, Loreto Carmona6, Katerina Chatzidionysiou7, Talita Duarte-Salles8, Walid Fakhouri9, Antje Hottgenroth10, Meghna Jani11, Raivo Kolde12, Jan A Kors2, Lembe Kullamaa13, Jennifer Lane14, Karine Marinier15, Alexander Michel16, Henry Morgan Stewart17, Albert Prats-Uribe14, Sulev Reisberg18, Anthony G Sena19, Carmen O Torre17, Katia Verhamme2, David Vizcaya20, James Weaver21, Patrick Ryan21, Daniel Prieto-Alhambra14, Peter R Rijnbeek2. 1. Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands. Electronic address: c.yang@erasmusmc.nl. 2. Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands. 3. Janssen Research and Development, Titusville, NJ, United States. 4. DETI/IEETA, University of Aveiro, Aveiro, Portugal. 5. Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom; Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain. 6. Instituto de Salud Musculoesquelética, Madrid, Spain. 7. Department of Medicine, Solna, Rheumatology Unit, Karolinska Institute, Stockholm, Sweden. 8. Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain. 9. Eli Lilly and Company, Windlesham, Surrey, United Kingdom. 10. Lilly Deutschland GmbH, Bad Homburg, Germany. 11. Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom. 12. Institute of Computer Science, University of Tartu, Tartu, Estonia. 13. Department of Epidemiology and Biostatistics, National Institute for Health Development, Tallinn, Estonia; Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia; European Patients' Forum, Brussels, Belgium. 14. Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom. 15. Servier, Suresnes, France. 16. Epidemiology, Bayer Basel, Basel, Switzerland. 17. Real-World Solutions, IQVIA, Brighton, United Kingdom. 18. Institute of Computer Science, University of Tartu, Tartu, Estonia; STACC, Tartu, Estonia; Quretec, Tartu, Estonia. 19. Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands; Janssen Research and Development, Titusville, NJ, United States. 20. Bayer Pharmaceuticals, Barcelona, Spain. 21. Janssen Research and Development, Titusville, NJ, United States; Observational Health Data Sciences and Informatics, New York, NY, United States.
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.
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.