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Systems approach for classifying the response to biological therapies in patients with rheumatoid arthritis in clinical practice

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Author: Fu, J. · Wietmarschen, H.A. van · Kooij, A. van der · Cuppen, B.V.J. · Schroën, Y. · Marijnissen, A.K. · Meulman, J.J. · Lafeber, F.P.J.G. · Greef, J. van der
Source:European Journal of Integrative Medicine, 19, 65-71
Identifier: 787787
doi: doi:10.1016/j.eujim.2018.02.006
Keywords: Biology · Biological agent · Categorical principal components analysis · Classification · Rheumatoid arthritis · Abatacept · Adalimumab · C reactive protein · Certolizumab pegol · Creatinine · Cyclic citrullinated peptide antibody · Etanercept · Golimumab · Rheumatoid factor · Rituximab · Tocilizumab · Adult · Biological therapy · Chinese medicine · Clinical assessment · Clinical practice · Disease severity · Female · Human · Major clinical study · Male · Middle aged · Predictive value · Principal component analysis · Priority journal · Questionnaire · Symptom assessment · System analysis · Treatment response · Biomedical Innovation · Healthy Living · Life · MSB - Microbiology and Systems Biology · ELSS - Earth, Life and Social Sciences


Introduction: Biological therapies have greatly improved the treatment efficacy in rheumatoid arthritis (RA). However, in clinical practice a significant proportion of patients experience an inadequate response to treatment. The aim of this study is to classify responding and non-responding rheumatoid arthritis patients treated with biological therapies, based on clinical parameters and symptoms used in Western and Chinese medicine. Methods: Cold and Heat symptoms accessed by a Chinese medicine (CM) questionnaire and Western clinical data were collected as baseline data, before initiating biological therapy. Categorical principal components analysis with forced classification (CATPCA-FC) approach was applied to the baseline data set to classify responders and non-responders. Results: In this study, 61 RA patients were characterized using a CM questionnaire and clinical measurements. The combination of baseline symptoms (‘preference for warm food’, ‘weak tendon severity’) and clinical parameters (positive rheumatoid factor/anti-cyclic citrullinated peptide antibody, C-reactive protein, creatinine) were able to differentiate responders from non-responders to biological therapies with a positive predictive value of 82.35% and a misclassification rate of 24.59%. Adding CM symptom variables in addition to clinical data did not improve the classification of responders, but it did show 8.3% improvement in classifying non-responders. Conclusions: No significant differences were found between the three classification models. Adding CM symptoms to the clinical parameters in the combined model improved the classification of non-responders. Although this improvement is not significant in the current study, we consider it worthwhile to further investigate the potential of adding symptom variables for improving treatment efficacy. © 2018 Elsevier GmbH Chemicals/CAS: abatacept, 332348-12-6; adalimumab, 331731-18-1; C reactive protein, 9007-41-4; certolizumab pegol, 428863-50-7; creatinine, 19230-81-0, 60-27-5; etanercept, 185243-69-0, 200013-86-1; golimumab, 476181-74-5; rheumatoid factor, 9009-79-4; rituximab, 174722-31-7; tocilizumab, 375823-41-9