Sa1237 Prediction of Primary Response to Infliximab in Crohn's Disease: A Matrix-Based Prediction Model

Abstract

Background Primary non-response (PNR) to TNF antagonists in IBD still holds a challenge for clinicians. Furthermore, with the advent of anti-integrin molecules, selecting the right therapeutic class for a given patient would be welcomed. Current predictors are insufficiently identifying patients at risk for PNR and are therefore not used in clinical practice. We designed a matrix-based prediction model, which may avoid exposure in patients who are unlikely to have benefit of the drug. Methods 201 anti-TNF Naive Crohn's disease patients started on infliximab (IFX) induction 0-2-6 were used. For each patient, clinical information and biological markers (CRP, albumin,...) were collected prior to IFX start. Baseline serum TNF load and the IBD SGI serological panel (Prometheus Laboratories Inc.) were also available. PNR was defined as no clinical benefit after induction regimen. Univariate and correlation analyses were performed to select possible predictors. A combination of final predictors was selected through multiple regression based on the Bayesian information criterion (BIC). Finally these were categorized according to a clinically relevant threshold and were used to develop a matrix-based prediction model. Predicted probabilities were calculated and were organized into a matrix. Results: The incidence of PNR was 8% (16/ 201). Duration of disease, age at first IFX, BMI, previous surgery, serum albumin, ASCA IgA, ASCA IgG and serum TNF load differed substantially between responders and nonresponders in univariate analysis (all p<0.2). The parsimonious BIC in a multiple regression analysis withheld three independent significant final predictors (p<0.05): age at first IFX, BMI and previous surgery. The ROC-AUC for this final model was 0.79 and the predictors remained significant after bootstrapping. BMI was categorized into ≤20 (28% of patients), 21-25 (45%) and ≥ 26 (27%) and age at first IFX was categorized into ≤ 25 years (27%) , 26-64 years (68%) and ≥65 years (5%). Previous surgery was the strongest associated predictor of PNR with OR of 5.18 [1.51-23.96]. The matrix model using these final predictors showed a good spread of primary response rates for the different categories with a matching color code (see figure). Discussion We developed a matrix-based prediction tool to aid physicians in optimizing therapeutic decisions. After stringent selection of predictors only age at first IFX, BMI and previous surgery were withheld. A younger age has already been associated with primary response in several independent cohorts. BMI is also known to influence response and this is assigned to the weight based dosing of IFX. Previous surgery has also been associated with PNR and might reflect more refractory disease. The next step is to validate this matrix in an independent cohort and to construct a matrix-prediction tool for secondary loss of response.