Development of machine learning models for detection of vision threatening Behçet’s disease (BD) using Egyptian College of Rheumatology (ECR)–BD cohort

Abstract

Abstract Background Eye lesions, occur in nearly half of patients with Behçet’s Disease (BD), can lead to irreversible damage and vision loss; however, limited studies are available on identifying risk factors for the development of vision-threatening BD (VTBD). Using an Egyptian college of rheumatology (ECR)-BD, a national cohort of BD patients, we examined the performance of machine-learning (ML) models in predicting VTBD compared to logistic regression (LR) analysis. We identified the risk factors for the development of VTBD. Methods Patients with complete ocular data were included. VTBD was determined by the presence of any retinal disease, optic nerve involvement, or occurrence of blindness. Various ML-models were developed and examined for VTBD prediction. The Shapley additive explanation value was used for the interpretability of the predictors. Results A total of 1094 BD patients [71.5% were men, mean ± SD age 36.1 ± 10 years] were included. 549 (50.2%) individuals had VTBD. Extreme Gradient Boosting was the best-performing ML model (AUROC 0.85, 95% CI 0.81, 0.90) compared with logistic regression (AUROC 0.64, 95%CI 0.58, 0.71). Higher disease activity, thrombocytosis, ever smoking, and daily steroid dose were the top factors associated with VTBD. Conclusions Using information obtained in the clinical settings, the Extreme Gradient Boosting identified patients at higher risk of VTBD better than the conventional statistical method. Further longitudinal studies to evaluate the clinical utility of the proposed prediction model are needed.

Keywords

• Behçet’s disease
• Vision-threatening BD
• Machine learning
• SHAP analysis