P0579 Predictive Modeling for Colectomy Risk in Patients with Ulcerative Colitis and Primary Sclerosing Cholangitis: A Brazilian Multicentric Study Utilizing Machine Learning.
Authors: da Silva B C , Kronbauer A H , Chebli J M F , Chebli L A , Ferreira S D C , Lima G A S , Rzetelna H , Freire C C F , Braga L L B C , Santana G O , Parente J M L 
Published: January 2025

Background

Ulcerative Colitis (UC) and Primary Sclerosing Cholangitis (PSC) are interrelated chronic inflammatory diseases. Patients with both conditions are at an elevated risk of colectomy, predominantly due to a higher incidence of dysplasia and colorectal cancer. Identify which patients are at higher risk of colectomy can be crucial to personalize their management. The primary objective was to develop a predictive model that can accurately forecast colectomy risk.

Methods

A cross-sectional study was conducted with patients from multiple tertiary centers across Brazil. We employed logistic regression and Random Forest (RF) models to analyze the data and identify significant predictors for colectomy. Data extraction was performed using Excel, and statistical analyses were conducted using R Studio.

Results

The study population comprised 83 individuals diagnosed with both UC and PSC. The mean age of the patients was 45 years (range 6 to 67), with 56 patients being male (67.5%). The mean age at diagnosis of UC and PSC was 32 years (range 18 to 77) and 36 years (range 8 to 72), respectively. Among patients with UC, the majority (74/83) had extensive colitis (89.2%), and 49.4% (41/83) had used at least one biological therapy. Only 13 patients (15.7%) required colectomy. Liver cirrhosis occurred in 15 cases (18.1%), and 11 patients (13.3%) required liver transplantation. Twelve patients (14.5%) had dysplasia or colorectal cancer. Our analysis identified several key predictors for colectomy. In the linear regression analysis, the duration of UC greater than 11 years significantly increased the risk of colectomy (OR=2.15, p=0.04). The presence of hepatic complications was associated with a tendency towards an increased risk of colectomy (OR=1.79, p=0.059). In the RF model, the presence of hepatic complications, and duration of UC greater than 11 years were significant predictors for colectomy. Protective factors identified by the RF model included use of biologic therapy, clinical and endoscopic remission. The RF model demonstrated an accuracy of 88.2%, outperforming the linear regression model and providing superior accuracy and robustness in predicting colectomy risk within the defined period.

Conclusion

Our study developed a predictive model using the RF algorithm, which demonstrated superior performance over linear regression in identifying high-risk patients for colectomy. This model can serve as a valuable tool in clinical practice, aiding in early intervention and tailored management strategies to mitigate severe outcomes in patients with UC and PSC. The use of RF provides a more reliable prediction, emphasizing the importance of machine learning in clinical decision-making.

References

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