Development and Validation of Diagnosis Model for Inflammatory Bowel Diseases Based on a Serologic Biomarker Panel: A Decision Tree Model Study

Background and study aims Currently, an increasing amount of experimental data is available on newly discovered biomarkers in inflammatory bowel diseases (IBD), but the role of these biomarkers is often questionable due to their limited sensitivity. Therefore, this study aimed to build a diagnostic tool incorporating a panel of serum biomarkers into a computational algorithm to identify patients with IBD and differentiate those with Crohn’s disease (CD) from those with ulcerative colitis (UC). Patients and methods We studied sera from 192 CD patients, 118 UC patients, 60 non-IBD controls and 60 healthy controls. Indirect immunofluorescence (IIF) assays were utilized to determine several serum biomarkers previously associated with IBD, and the decision tree algorithm was used to construct the diagnosis model. Performances of models were evaluated by prediction accuracy, precision, AUC and Matthews’s correlation coefficient (MCC). The “Inflammatory Bowel Disease Multi-omics Database (IBDMDB)” cohorts were used to validate the model as external validation set. Results The prediction rates were determined and compared for decision tree models after each data was developed using C5.0, C&RT, QUEST and CHAID. The C5.0 and CHAID algorithms, which ranked top for the prediction rate in the IBD vs. non-IBD model and the CD vs. UC model, respectively, were utilized for final pattern analysis. The final decision tree model achieved higher classification accuracy than the approach based on conservative marker combinations (sensitivity 75.0% vs. 79.5%, specificity 93.8% vs. 78.3% for differentiating IBD from non-IBD; and sensitivity 84.3% vs. 73.4%, specificity 92.5% vs. 54.9% for differentiating CD from UC, respectively). The model prediction consistency was 93% (28/30) in the external validation set. Conclusion The decision-tree-based approach used in this study, based on serum biomarkers, has shown to be a valid and useful approach to identifying IBD and differentiating CD from UC.