P207 Modelling 1-year surgery risk in Crohn’s disease: A retrospective cohort study

Background

Accelerated therapeutic treatment should be considered in patients with progressive Crohn’s disease (CD) to prevent complications as well as surgery. Therefore, screening for risk factors and predicting the need for early surgery is of great importance in clinical practice. We aimed to establish a model to predict CD-related early surgery.

Methods

This was a retrospective study collecting data from CD patients diagnosed in our inflammatory bowel disease (IBD) centre from 1 January 2012 to 31 December 2016. All data were randomly stratified into a training set and a validation set at a ratio of 8:2. Multiple logistic regression analysis was conducted with receiver operating characteristic (ROC) curves constructed and areas under the curve (AUC) calculated. This model was further validated with a nomogram developed.

Results

A total of 1002 eligible patients were enrolled with a mean follow-up period of 53.54 ± 13.10 months. In total, 24.25% of patients received intestinal surgery within 1 year after diagnosis due to complications or disease relapse. Disease behaviour (B2: OR 6.693, p < 0.001; B3: OR 14.405, p < 0.001), smoking (OR 4.135, p < 0.001), BMI (OR 0.873, p < 0.001) and CRP (OR 1.022, p = 0.001) at diagnosis, previous perianal (OR 9.483, p < 0.001) or intestinal surgery (OR 8.887, p < 0.001), maximum bowel wall thickness (OR 1.965, p < 0.001), use of biologics (OR 0.264, p < 0.001), and exclusive enteral nutrition (OR 0.089, p < 0.001) were identified as independent significant factors associated with early intestinal surgery. A prognostic model was established as follows: X₁ = maximum BWT [mm]; X₂ = smoking [0: no, 1: yes]; X₃ = BMI at diagnosis [m/kg²]；X₄ = previous perianal surgery [0: no, 1: yes]; X₅ = previous intestinal surgery [0: no, 1: es]; X₆ = disease type (stricturing or penetrating disease); X₇ = use of biologics; X₈ = use of EEN; X₉ = CRP at diagnosis). ROC curve and calculated AUC (94.7%) confirmed an ideal predictive ability of this model with a sensitivity of 75.92% and specificity of 95.81%. Nomogram was developed to simplify the use of predictive model in clinical daily practice.

Figure 1. ROC curve of the training and testing data. (A) Predictive ability of this model was appraised with an AUC of 0.947, sensitivity of 75.92%, and specificity of 95.81%. (B) Discrimination of the validated model was estimated with an AUC of 0.937, sensitivity of 67.31%, and specificity of 97.55%. Abbreviations: PPV = positive predictive value; NPV = negative predictive value.

Figure 2. A prognostic nomogram for 1-year surgery in CD patients.