Development of Time-Aggregated Machine Learning Model for Relapse Prediction in Pediatric Crohn's Disease

Abstract

INTRODUCTION: Pediatric Crohn's disease (CD) easily progresses to an active disease compared with adult CD, making it important to predict and minimize CD relapses. However, prediction of relapse at various time points (TPs) during pediatric CD remains understudied. We aimed to develop a real-time aggregated model to predict pediatric CD relapse in different TPs and time windows (TWs). METHODS: This retrospective study was conducted on children diagnosed with CD between 2015 and 2022 at Severance Hospital. Laboratory test results and demographic data were collected starting at 3 months after diagnosis, and cohorts were formed using data from 6 different TPs at 1-month intervals. Relapse-defined as a pediatric CD activity index >= 30 points-was predicted, and TWs were 3-7 months with 1-month intervals. The feature importance of the variables in each setting was determined. RESULTS: Data from 180 patients were used to construct cohorts corresponding to the TPs. We identified the optimal TP and TW to reliably predict pediatric CD relapse with an area under the receiver operating characteristic curve score of 0.89 when predicting with a 3-month TW at a 3-month TP. Variables such as C-reactive protein levels and lymphocyte fraction were found to be important factors. DISCUSSION: We developed a time-aggregated model to predict pediatric CD relapse in multiple TPs and TWs. This model identified important variables that predicted relapse in pediatric CD to support real-time clinical decision making.