Dong Rongrong, Wang Yiting, Yao Han, Chen Taoran, Zhou Qi, Zhao Bo, Xu Jiancheng
Department of Laboratory Medicine, First Hospital of Jilin University, Changchun, 130021, People's Republic of China.
Department of Laboratory Medicine, Second Hospital of Jilin University, Changchun, 130022, People's Republic of China.
J Inflamm Res. 2025 Apr 15;18:5115-5131. doi: 10.2147/JIR.S378069. eCollection 2025.
Inflammatory bowel disease (IBD) is a chronic, incurable gastrointestinal disease without a gold standard for diagnosis. This study aimed to develop predictive models for diagnosing IBD, Crohn's disease (CD), and Ulcerative colitis (UC) by combining two approaches: machine learning (ML) and traditional nomogram models.
Cohorts 1 and 2 comprised data from the UK Biobank (UKB), and the First Hospital of Jilin University, respectively, which represented the initial laboratory tests upon admission for 1135 and 237 CD patients, 2192 and 326 UC patients, and 1798 and 298 non-IBD patients. Cohorts 1 and 2 were used to create predictive models. The parameters of the machine learning model established by Cohorts 1 and 2 were merged, and nomogram models were developed using Logistic regression. Cohort 3 collected initial laboratory tests from 117 CD patients, 197 UC patients, and 241 non IBD patients at a tertiary hospital in different regions of China for external testing of three nomogram models.
For Cohort 1, ML-IBD-1, ML-CD-1 and ML-UC-1 models developed using the LightGBM algorithm demonstrated exceptional discrimination (ML-IBD-1: AUC = 0.788; ML-CD-1: AUC = 0.772; ML-UC-1: AUC = 0.841). For Cohort 2, ML-IBD-2, ML-CD-2, and ML-UC-2 models developed using XGBoost and Logistic Regression algorithms demonstrated exceptional discrimination (ML-IBD-2: AUC = 0.894; ML-CD-2: AUC = 0.932; ML-UC-2: AUC = 0.778). The nomogram model exhibits good diagnostic capability (nomogram-IBD: AUC=0.778, 95% CI (0.688-0.868); nomogram-CD: AUC=0.744, 95% CI (0.710-0.778); nomogram-UC, AUC=0.702, 95% CI (0.591-0.814)). The predictive ability of the three models was validated in cohort 3 (nomogram-IBD: AUC=0.758, 95% CI (0.683-0.832); nomogram-CD: AUC=0.791, 95% CI (0.717-0.865); nomogram-UC, AUC=0.817, 95% CI (0.702-0.932)).
This study utilized three cohorts and developed risk prediction models for IBD, CD, and UC with good diagnostic capability, based on conventional laboratory data using ML and nomogram.
炎症性肠病（IBD）是一种慢性、无法治愈的胃肠道疾病，尚无诊断的金标准。本研究旨在通过结合机器学习（ML）和传统列线图模型这两种方法，开发用于诊断IBD、克罗恩病（CD）和溃疡性结肠炎（UC）的预测模型。
队列1和队列2分别包含来自英国生物银行（UKB）和吉林大学第一医院的数据，分别代表1135例和237例CD患者、2192例和326例UC患者以及1798例和298例非IBD患者入院时的初始实验室检查。队列1和队列2用于创建预测模型。将队列1和队列2建立的机器学习模型的参数合并，并使用逻辑回归开发列线图模型。队列3收集了中国不同地区一家三级医院的117例CD患者、197例UC患者和241例非IBD患者的初始实验室检查，用于对三个列线图模型进行外部验证。
对于队列1，使用LightGBM算法开发的ML-IBD-1、ML-CD-1和ML-UC-1模型表现出卓越的区分能力（ML-IBD-1：AUC = 0.788；ML-CD-1：AUC = 0.772；ML-UC-1：AUC = 0.841）。对于队列2，使用XGBoost和逻辑回归算法开发的ML-IBD-2、ML-CD-2和ML-UC-2模型表现出卓越的区分能力（ML-IBD-2：AUC = 0.894；ML-CD-2：AUC = 0.932；ML-UC-2：AUC = 0.778）。列线图模型表现出良好的诊断能力（列线图-IBD：AUC = 0.778，95%CI（0.688 - 0.868）；列线图-CD：AUC = 0.744，95%CI（0.710 - 0.778）；列线图-UC，AUC = 0.702，95%CI（0.591 - 0.814））。这三个模型的预测能力在队列3中得到验证（列线图-IBD：AUC = 0.758，95%CI（0.683 - 0.832）；列线图-CD：AUC = 0.791，95%CI（0.717 - 0.865）；列线图-UC，AUC = 0.817，95%CI（0.702 - 0.932））。
本研究利用三个队列，基于常规实验室数据，通过ML和列线图开发了具有良好诊断能力的IBD、CD和UC风险预测模型。
