Junhai Zhen,1 Chuan Liu,2 Jixiang Zhang,2 Fei Liao,2 Huabing Xie,1 Cheng Tan,2 Ping An,2 Zhongchun Liu,3 Changqing Jiang,4 Jie Shi,5 Kaichun Wu,6 Weiguo Dong2 

1Department of General Practice, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People’s Republic of China; 2Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, 430060, People’s Republic of China; 3Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, 430060, People’s Republic of China; 4Department of Clinical Psychology, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, People’s Republic of China; 5Department of Medical Psychology, Chinese People’s Liberation Army Rocket Army Characteristic Medical Center, Beijing, 100032, People’s Republic of China; 6Department of Gastroenterology, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of China

Correspondence: Kaichun Wu, Department of Gastroenterology, Xijing Hospital, Air Force Medical University, Xi’an, 710032, People’s Republic of China, Tel/Fax +8629-84771600, Email kaicwu@fmmu.edu.cn Weiguo Dong, Department of Gastroenterology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, Hubei Province, 430060, People’s Republic of China, Tel/Fax +8627-88041911, Email dongweiguo@whu.edu.cn

Purpose: 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.
Patients and Methods: 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.
Results: 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.
Conclusion: 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.

Keywords: clinical research, artificial intelligence, model development, clinical decision support system