P138 Prediction model to safely cease anti-TNF therapy in Crohn’s disease: individual patient data meta-analysis (IPD-MA)
R. W. M. Pauwels*1, C. J. van der Woude1, D. Nieboer2, E. W. Steyerberg2,3, M. J. Casanova4, J. P. Gisbert4, A. J. Lobo5, C. W. Lees6, N. A. Kennedy5, T. Molnár7, K. Szánto7, E. Louis8, J-Y. Mary9, M. Lukas10,11, M. Duijvestein12, S. Bots12, G. R. A. M. D'Haens12, A. C. de Vries1
1Erasmus MC, Department of Gastroenterology and Hepatology, Rotterdam, The Netherlands, 2Erasmus MC, Department of Public Health, Rotterdam, The Netherlands, 3Leiden UMC, Department of Clinical Biostatistics and Medical Decision Making, Leiden, The Netherlands, 4Madrid Hospital Universitario de la Princesa, Instituto de Investigación Sanitaria Princesa (IIS-IP) and Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Department of Gastroenterology, Madrid, Spain, 5Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Department of Gastroenterology and Hepatology, Sheffield, UK, 6Western General Hospital, Department of Gastroenterology and Hepatology, Edinburgh, UK, 7University of Szeged, Department of Medicine, Szeged, Hungary, 8Centre Hospitalier Universitaire de Liège, Department of Gastroenterology and Hepatology, Liège, Belgium, 9INSERM U717, Department of Biostatistics and Clinical Epidemiology, Paris, France, 10IBD Clinical and Research Centre, Iscare a.s, Prague, Czech Republic, 11Institute of Medical Biochemistry and Laboratory Diagnostics, 1st Medical Faculty and General Teaching Hospital, Prague, Czech Republic, 12Amsterdam UMC, Academic Medical Centre, Department of Gastroenterology and Hepatology, Amsterdam, The Netherlands
Tools for patient stratification to safely cease anti-TNF therapy in Crohn’s disease (CD) are urgently needed. This IPD-MA aims at development of a predictive diagnostic tool for a personalised approach towards anti-TNF cessation in CD.
A systematic literature search was conducted to identify studies investigating the risk of relapse and risk factors in CD patients after anti-TNF therapy cessation by using Medline Ovid, Embase, Cochrane, Web of Science and Google Scholar. Cohort studies with >50 CD patients in remission (clinical or biochemical or endoscopic/radiological) were selected. IPD from the original study databases were used for analysis. Inclusion criteria: luminal CD as indication for anti-TNF therapy, duration of treatment ≥6 months. We associated baseline demographic and clinical data (age, gender, smoking, disease duration, Montreal classification, history of surgical resection, type of anti-TNF medication, concomitant immunosuppressants, corticosteroids prior to cessation and previous anti-TNF therapy) with time to relapse using a Cox model. A prediction model was constructed following the ‘TRIPOD’ statement, with backward selection and
A total of 10 cohort studies were identified, IPD were available from 6 studies. Anti-TNF was withdrawn in 1006 patients, who experienced 474 relapses after a median FU time of 14 months (IQR 8–28). At 1-year relapse rate was 36%, ranging from 24% to 44%. At 2-year relapse rate was 54% (41%-82%). Risk factors for relapse were age (HR 0.98, CI 0.97–0.99), smoking at baseline (HR 1.19 (CI 0.96–1.48), disease duration (HR 1.06, CI 1.03–1.10), disease location (L2) (HR 1.04, CI 0.77–1.41), disease location (L3) (HR 1.25, CI 0.96–1.62), +L4 (HR 1.50, CI 1.00–2.27), type of anti-TNF therapy (adalimumab vs. infliximab) (HR 1.18, CI 0.95–1.48), immunosuppressant use (HR 0.68, CI 0.54–0.85), steroid used 6–12 months prior to cessation (HR 1.24, CI 0.72–2.13), ≥1 anti-TNF therapy in medical history (HR 1.37, CI 1.04–1.80). The prediction model had a discriminative ability with a C-statistic of 0.62 (0.58–0.64). Biochemical parameters of remission (CRP, FC, haemoglobin, leucocytes), anti-TNF trough level and endoscopic data will be added to this preliminary prediction model.
The overall risk of relapse in CD patients in remission is 37% within 1 year after anti-TNF cessation. Despite associations between clinical parameters and relapse risk, individualised prediction solely based on clinical parameters remains challenging. Improvement of the discriminative ability of the prediction model may be anticipated after insertion of biochemical and endoscopic data.