학술논문
Prognostic modeling in early rheumatoid arthritis: reconsidering the predictive role of disease activity scores.
Document Type
Academic Journal
Author
Bird A; Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia. alix.bird@adelaide.edu.au.; School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia. alix.bird@adelaide.edu.au.; Oakden-Rayner L; Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.; School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.; Smith LA; Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.; School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.; Zeng M; Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.; School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.; Ray S; Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.; Artificial Intelligence and Machine Learning, GSK Plc, South San Francisco, CA, USA.; Proudman S; Department of Rheumatology, Royal Adelaide Hospital, Adelaide, SA, 5000, Australia.; Palmer LJ; Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.; School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.
Source
Publisher: Springer Country of Publication: Germany NLM ID: 8211469 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1434-9949 (Electronic) Linking ISSN: 07703198 NLM ISO Abbreviation: Clin Rheumatol Subsets: MEDLINE
Subject
Language
English
Abstract
Objective: In this prospective cohort study, we provide several prognostic models to predict functional status as measured by the modified Health Assessment Questionnaire (mHAQ). The early adoption of the treat-to-target strategy in this cohort offered a unique opportunity to identify predictive factors using longitudinal data across 20 years.
Methods: A cohort of 397 patients with early RA was used to develop statistical models to predict mHAQ score measured at baseline, 12 months, and 18 months post diagnosis, as well as serially measured mHAQ. Demographic data, clinical measures, autoantibodies, medication use, comorbid conditions, and baseline mHAQ were considered as predictors.
Results: The discriminative performance of models was comparable to previous work, with an area under the receiver operator curve ranging from 0.64 to 0.88. The most consistent predictive variable was baseline mHAQ. Patient-reported outcomes including early morning stiffness, tender joint count (TJC), fatigue, pain, and patient global assessment were positively predictive of a higher mHAQ at baseline and longitudinally, as was the physician global assessment and C-reactive protein. When considering future function, a higher TJC predicted persistent disability while a higher swollen joint count predicted functional improvements with treatment.
Conclusion: In our study of mHAQ prediction in RA patients receiving treat-to-target therapy, patient-reported outcomes were most consistently predictive of function. Patients with high disease activity due predominantly to tenderness scores rather than swelling may benefit from less aggressive treatment escalation and an emphasis on non-pharmacological therapies, allowing for a more personalized approach to treatment. Key Points • Long-term use of the treat-to-target strategy in this patient cohort offers a unique opportunity to develop prognostic models for functional outcomes using extensive longitudinal data. • Patient reported outcomes were more consistent predictors of function than traditional prognostic markers. • Tender joint count and swollen joint count had discordant relationships with future function, adding weight to the possibility that disease activity may better guide treatment when the components are considered separately.
(© 2024. The Author(s).)
Methods: A cohort of 397 patients with early RA was used to develop statistical models to predict mHAQ score measured at baseline, 12 months, and 18 months post diagnosis, as well as serially measured mHAQ. Demographic data, clinical measures, autoantibodies, medication use, comorbid conditions, and baseline mHAQ were considered as predictors.
Results: The discriminative performance of models was comparable to previous work, with an area under the receiver operator curve ranging from 0.64 to 0.88. The most consistent predictive variable was baseline mHAQ. Patient-reported outcomes including early morning stiffness, tender joint count (TJC), fatigue, pain, and patient global assessment were positively predictive of a higher mHAQ at baseline and longitudinally, as was the physician global assessment and C-reactive protein. When considering future function, a higher TJC predicted persistent disability while a higher swollen joint count predicted functional improvements with treatment.
Conclusion: In our study of mHAQ prediction in RA patients receiving treat-to-target therapy, patient-reported outcomes were most consistently predictive of function. Patients with high disease activity due predominantly to tenderness scores rather than swelling may benefit from less aggressive treatment escalation and an emphasis on non-pharmacological therapies, allowing for a more personalized approach to treatment. Key Points • Long-term use of the treat-to-target strategy in this patient cohort offers a unique opportunity to develop prognostic models for functional outcomes using extensive longitudinal data. • Patient reported outcomes were more consistent predictors of function than traditional prognostic markers. • Tender joint count and swollen joint count had discordant relationships with future function, adding weight to the possibility that disease activity may better guide treatment when the components are considered separately.
(© 2024. The Author(s).)