Print Email Facebook Twitter Systems approach for classifying the response to biological therapies in patients with rheumatoid arthritis in clinical practice Title Systems approach for classifying the response to biological therapies in patients with rheumatoid arthritis in clinical practice Author Fu, J. van Wietmarschen, H.A. van der Kooij, A. Cuppen, B.V.J. Schroën, Y. Marijnissen, A.K. Meulman, J.J. Lafeber, F.P.J.G. van der Greef, J. Publication year 2018 Abstract 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 Subject LifeMSB - Microbiology and Systems BiologyELSS - Earth, Life and Social SciencesBiomedical InnovationBiologyHealthy LivingBiological agentCategorical principal components analysisClassificationRheumatoid arthritisAbataceptAdalimumabC reactive proteinCertolizumab pegolCreatinineCyclic citrullinated peptide antibodyEtanerceptGolimumabRheumatoid factorRituximabTocilizumabAdultBiological therapyChinese medicineClinical assessmentClinical practiceDisease severityFemaleHumanMajor clinical studyMaleMiddle agedPredictive valuePrincipal component analysisPriority journalQuestionnaireSymptom assessmentSystem analysisTreatment response To reference this document use: http://resolver.tudelft.nl/uuid:5349a328-8c29-4cd1-beca-8aa3594506d9 DOI https://doi.org/10.1016/j.eujim.2018.02.006 TNO identifier 787787 ISSN 1876-3820 Source European Journal of Integrative Medicine, 19, 65-71 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.