Abstract
Background: The incidence and global burden of inflammatory bowel disease (IBD) have steadily increased in the past few decades. Improved methods to stratify risk and predict disease-related outcomes are required for IBD. Aim: The aim of this study was to develop and validate a machine learning (ML) model to predict the 5-year risk of starting biologic agents in IBD patients. Method: We applied an ML method to the database of the Korean common data model (K-CDM) network, a data sharing consortium of tertiary centers in Korea, to develop a model to predict the 5-year risk of starting biologic agents in IBD patients. The records analyzed were those of patients diagnosed with IBD between January 2006 and June 2017 at Gil Medical Center (GMC; n = 1299) or present in the K-CDM network (n = 3286). The ML algorithm was developed to predict 5- year risk of starting biologic agents in IBD patients using data from GMC and externally validated with the K-CDM network database. Result: The ML model for prediction of IBD-related outcomes at 5 years after diagnosis yielded an area under the curve (AUC) of 0.86 (95% CI: 0.82–0.92), in an internal validation study carried out at GMC. The model performed consistently across a range of other datasets, including that of the K-CDM network (AUC = 0.81; 95% CI: 0.80–0.85), in an external validation study. Conclusion: The ML-based prediction model can be used to identify IBD-related outcomes in patients at risk, enabling physicians to perform close follow-up based on the patient’s risk level, estimated through the ML algorithm.
Highlights
Inflammatory bowel disease (IBD) consists of a spectrum of chronic and progressive inflammatory disorders including Crohn’s disease (CD) and ulcerative colitis (UC) [1,2]
Limsrivilai et al investigated the predictors for high health care utilization among inflammatory bowel disease (IBD) patients as a single center study, and they found that psychiatric illness, use of corticosteroids, use of narcotics, low levels of hemoglobin, and high numbers of IBD related hospitalizations were associated with worsened prognosis for IBD patients [10]
We aimed to develop a risk prediction model of 5-year IBD-related outcomes based on an machine learning (ML) algorithm, internally validated its performance at Gil Medical Center (GMC), and externally validated a large sample derived from the Korean common data model (K-CDM) network
Summary
Inflammatory bowel disease (IBD) consists of a spectrum of chronic and progressive inflammatory disorders including Crohn’s disease (CD) and ulcerative colitis (UC) [1,2]. Machine learning (ML) is a methodology that can examine large datasets to develop prediction models [12,13] [14,15] and is known to have several advantages over traditional statistical approaches14 It has been used in many areas and is on the verge of application in the medical field including disease related outcome prediction, type classification, even epigenomics [13,16,17,18,19,20,21,22,23,24,25,26,27]. We aimed to develop a risk prediction model of 5-year IBD-related outcomes based on an ML algorithm, internally validated its performance at Gil Medical Center (GMC), and externally validated a large sample derived from the Korean common data model (K-CDM) network
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
