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

IF 3 3区医学 Q2 GASTROENTEROLOGY & HEPATOLOGY Clinical and Translational Gastroenterology Pub Date : 2024-11-21 DOI:10.14309/ctg.0000000000000794

Sooyoung Jang, JaeYong Yu, Sowon Park, Hyeji Lim, Hong Koh, Yu Rang Park

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引用次数: 0

Abstract

Introduction: Pediatric Crohn's disease (CD) easily progresses to an active disease compared to 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 six 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.

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开发用于预测小儿克罗恩病复发的时间聚合机器学习模型

导言：与成人克罗恩病（CD）相比，小儿克罗恩病（CD）很容易进展为活动性疾病，因此预测并尽量减少 CD 复发非常重要。然而，对小儿克罗恩病不同时间点（TPs）的复发预测研究仍然不足。我们的目的是开发一个实时汇总模型，以预测小儿 CD 在不同时间点和时间窗 (TW) 的复发：这项回顾性研究针对2015年至2022年期间在塞弗兰医院确诊为CD的儿童。从确诊后 3 个月开始收集实验室检查结果和人口统计学数据，并以 1 个月为间隔，使用 6 个不同 TP 的数据组成队列。预测复发定义为小儿 CD 活动指数≥30 点，TW 为 3-7 个月，间隔为 1 个月。结果：结果：来自 180 名患者的数据被用于构建与 TPs 相对应的队列。我们确定了能可靠预测小儿 CD 复发的最佳 TP 和 TW，当以 3 个月的 TW 预测 3 个月的 TP 时，接收者操作特征曲线下面积得分为 0.89。C反应蛋白水平和淋巴细胞比例等变量被认为是重要因素：讨论：我们建立了一个时间聚合模型来预测小儿 CD 在多个 TP 和 TW 中的复发。该模型确定了预测小儿 CD 复发的重要变量，以支持实时临床决策。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical and Translational Gastroenterology GASTROENTEROLOGY & HEPATOLOGY-

CiteScore

7.00

自引率

0.00%

发文量

114

审稿时长

16 weeks

期刊介绍： Clinical and Translational Gastroenterology (CTG), published on behalf of the American College of Gastroenterology (ACG), is a peer-reviewed open access online journal dedicated to innovative clinical work in the field of gastroenterology and hepatology. CTG hopes to fulfill an unmet need for clinicians and scientists by welcoming novel cohort studies, early-phase clinical trials, qualitative and quantitative epidemiologic research, hypothesis-generating research, studies of novel mechanisms and methodologies including public health interventions, and integration of approaches across organs and disciplines. CTG also welcomes hypothesis-generating small studies, methods papers, and translational research with clear applications to human physiology or disease. Colon and small bowel Endoscopy and novel diagnostics Esophagus Functional GI disorders Immunology of the GI tract Microbiology of the GI tract Inflammatory bowel disease Pancreas and biliary tract Liver Pathology Pediatrics Preventative medicine Nutrition/obesity Stomach.