Zhen Junhai, Liu Chuan, Zhang Jixiang, Liao Fei, Xie Huabing, Tan Cheng, An Ping, Liu Zhongchun, Jiang Changqing, Shi Jie, Wu Kaichun, Dong Weiguo
Department of General Practice, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People's Republic of China.
Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People's Republic of China.
J Inflamm Res. 2024 Aug 9;17:5271-5283. doi: 10.2147/JIR.S470197. eCollection 2024.
Impaired quality of life (QOL) is common in patients with inflammatory bowel disease (IBD). A tool to more quickly identify IBD patients at high risk of impaired QOL improves opportunities for earlier intervention and improves long-term prognosis. The purpose of this study was to use a machine learning (ML) approach to develop risk stratification models for evaluating IBD-related QOL impairments.
An online questionnaire was used to collect clinical data on 2478 IBD patients from 42 hospitals distributed across 22 provinces in China from September 2021 to May 2022. Eight ML models used to predict the risk of IBD-related QOL impairments were developed and validated. Model performance was evaluated using a set of indexes and the best ML model was explained using a Local Interpretable Model-Agnostic Explanations (LIME) algorithm.
The support vector machine (SVM) classifier algorithm-based model outperformed other ML models with an area under the receiver operating characteristic curve (AUC) and an accuracy of 0.80 and 0.71, respectively. The feature importance calculated by the SVM classifier algorithm revealed that glucocorticoid use, anxiety, abdominal pain, sleep disorders, and more severe disease contributed to a higher risk of impaired QOL, while longer disease course and the use of biological agents and immunosuppressants were associated with a lower risk.
An ML approach for assessing IBD-related QOL impairments is feasible and effective. This mechanism is a promising tool for gastroenterologists to identify IBD patients at high risk of impaired QOL.
生活质量受损在炎症性肠病（IBD）患者中很常见。一种能更快速识别生活质量受损高风险IBD患者的工具可增加早期干预机会并改善长期预后。本研究的目的是使用机器学习（ML）方法开发用于评估IBD相关生活质量受损的风险分层模型。
2021年9月至2022年5月，通过在线问卷收集了来自中国22个省份42家医院的2478例IBD患者的临床数据。开发并验证了8种用于预测IBD相关生活质量受损风险的ML模型。使用一组指标评估模型性能，并使用局部可解释模型无关解释（LIME）算法解释最佳ML模型。
基于支持向量机（SVM）分类算法的模型优于其他ML模型，其受试者操作特征曲线下面积（AUC）和准确率分别为0.80和0.71。SVM分类算法计算的特征重要性显示，使用糖皮质激素、焦虑、腹痛、睡眠障碍以及病情更严重会导致生活质量受损风险更高，而病程较长以及使用生物制剂和免疫抑制剂则与较低风险相关。
一种用于评估IBD相关生活质量受损的ML方法是可行且有效的。该机制是胃肠病学家识别生活质量受损高风险IBD患者的有前景的工具。
