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Prediction of remission and low disease activity in disease-modifying anti-rheumatic drug-refractory patients with rheumatoid arthritis treated with golimumab

N. Vastesaeger, AG. Kutzbach, H. Amital, K. Pavelka, MA. Lazaro, RJ. Moots, J. Wollenhaupt, CA. Zerbini, I. Louw, B. Combe, A. Beaulieu, H. Schulze-Koops, B. Dasgupta, B. Fu, S. Huyck, HH. Weng, M. Govoni, P. Durez,

. 2016 ; 55 (8) : 1466-76. [pub] 20160425

Language English Country England, Great Britain

Document type Clinical Trial, Journal Article, Multicenter Study

Persistent link   https://www.medvik.cz/link/bmc17024037

OBJECTIVE: To create a tool to predict probability of remission and low disease activity (LDA) in patients with RA being considered for anti-TNF treatment in clinical practice. METHODS: We analysed data from GO-MORE, an open-label, multinational, prospective study in biologic-naïve patients with active RA (DAS28-ESR ⩾3.2) despite DMARD therapy. Patients received 50 mg s.c. golimumab (GLM) once monthly for 6 months. In secondary analyses, regression models were used to determine the best set of baseline factors to predict remission (DAS28-ESR <2.6) at month 6 and LDA (DAS28-ESR ⩽3.2) at month 1. RESULTS: In 3280 efficacy-evaluable patients, of 12 factors included in initial regression models predicting remission or LDA, six were retained in final multivariable models. Greater likelihood of LDA and remission was associated with being male; younger age; lower HAQ, ESR (or CRP) and tender joint count (or swollen joint count) scores; and absence of comorbidities. In models predicting 1-, 3- and 6-month LDA or remission, area under the receiver operating curve was 0.648-0.809 (R(2) = 0.0397-0.1078). The models also predicted 6-month HAQ and EuroQoL-5-dimension scores. A series of matrices were developed to easily show predicted rates of remission and LDA. CONCLUSION: A matrix tool was developed to show predicted GLM treatment outcomes in patients with RA, based on a combination of six baseline characteristics. The tool could help provide practical guidance in selection of candidates for anti-TNF therapy.

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$a Vastesaeger, Nathan $u Department of Medical Affairs, MSD Danmark ApS, Ballerup, Denmark nathan.vastesaeger@merck.com.
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$a OBJECTIVE: To create a tool to predict probability of remission and low disease activity (LDA) in patients with RA being considered for anti-TNF treatment in clinical practice. METHODS: We analysed data from GO-MORE, an open-label, multinational, prospective study in biologic-naïve patients with active RA (DAS28-ESR ⩾3.2) despite DMARD therapy. Patients received 50 mg s.c. golimumab (GLM) once monthly for 6 months. In secondary analyses, regression models were used to determine the best set of baseline factors to predict remission (DAS28-ESR <2.6) at month 6 and LDA (DAS28-ESR ⩽3.2) at month 1. RESULTS: In 3280 efficacy-evaluable patients, of 12 factors included in initial regression models predicting remission or LDA, six were retained in final multivariable models. Greater likelihood of LDA and remission was associated with being male; younger age; lower HAQ, ESR (or CRP) and tender joint count (or swollen joint count) scores; and absence of comorbidities. In models predicting 1-, 3- and 6-month LDA or remission, area under the receiver operating curve was 0.648-0.809 (R(2) = 0.0397-0.1078). The models also predicted 6-month HAQ and EuroQoL-5-dimension scores. A series of matrices were developed to easily show predicted rates of remission and LDA. CONCLUSION: A matrix tool was developed to show predicted GLM treatment outcomes in patients with RA, based on a combination of six baseline characteristics. The tool could help provide practical guidance in selection of candidates for anti-TNF therapy.
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$a Kutzbach, Abraham Garcia $u Department of Rheumatology, AGAR Francisco Marroquin University, Guatemala City, Guatemala.
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$a Amital, Howard $u Department of Internal Medicine B and Research Center for Autoimmune Diseases, Sheba Medical Center, Tel-Hashomer, Israel. $7 gn_A_00005638
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$a Pavelka, Karel $u Institute of Rheumatology and Clinic of Rheumatology, Charles University, Prague, Czech Republic.
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$a Lazaro, María Alicia $u Instituto de Asistencia Reumatologica Integral, Buenos Aires, Argentina.
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$a Moots, Robert J $u Department of Rheumatology, Clinical Sciences Centre, University Hospital Aintree, Liverpool, UK.
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$a Wollenhaupt, Jürgen $u Department of Rheumatology, Klinik für Rheumatologie, Schön Klinik Hamburg-Eilbek, Hamburg, Germany.
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$a Zerbini, Cristiano A F $u Department of Rheumatology, Centro Paulista de Investigação Clinica, São Paulo, Brazil.
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$a Louw, Ingrid $u Panorama Medical Centre, Cape Town, South Africa.
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$a Combe, Bernard $u Departement de Rhumatologie, Hôpital Lapeyronie, Montpellier University Hospital, Montpellier, France.
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$a Beaulieu, Andre $u Centre de Rhumatologie St-Louis, Québec, Canada.
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$a Schulze-Koops, Hendrik $u Division of Rheumatology and Clinical Immunology, Department of Medicine IV, University of Munich, Munich, Germany.
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$a Dasgupta, Bhaskar $u Department of Rheumatology, Southend University Hospital, Westcliff-on-Sea, Essex, UK.
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$a Fu, Bo $u Department of Biostatistics.
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$a Huyck, Susan $u Department of Biostatistics.
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$a Weng, Haoling H $u Clinical Development, Merck & Co, Inc., Kenilworth, NJ, USA.
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$a Govoni, Marinella $u Department of Immunology, MSD Italy, Global Medical Affairs, Rome, Italy.
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$a Durez, Patrick $u Department of Rheumatology, Université Catholique de Louvain, Brussels, Belgium.
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