Machine learning-based model for prediction of carbamazepine- and allopurinol-induced severe cutaneous adverse reactions in Vietnamese.

BACKGROUND: The low positive predictive value of HLA-B∗15:02 and HLA-B∗58:01 for risk stratification of carbamazepine (CBZ) and allopurinol (ALLO) induced severe cutaneous adverse reactions (SCARs) suggests that we need a better model to prevent cases. OBJECTIVE: This study aims to comprehensively investigate the role of genomic factors in CBZ- and ALLO-induced SCARs using machine-learning. METHODS: A total of 249 patients with SCARs and non-affected controls were genotyped using whole exome sequencing (WES), including 75 cases and 73 controls for ALLO, and 48 cases and 53 controls for CBZ, respectively. We then applied 8 risk prediction machine learning models. RESULTS: For predicting ALLO-induced SCARs, the Random Forest and Extra Tree models demonstrated exceptional performance among the 8 prediction models, achieving an average accuracy of 99.67% across 10 independent tests. For CBZ-induced SCARs, the Linear SVC model performed best with an average AUC of 86% on the test dataset over the 10 independent tests. CONCLUSION: These findings are crucial for understanding the underlying mechanisms in SCARs and for developing an accurate model which will identify patients at high risk of CBZ and ALLO-induced SCARs. CLINICAL IMPLICATIONS: Patients who need a high-risk medication and who are also at high risk of SCARs, due to their inheritance of HLA-B∗15:02 and/or HLA-B∗58:01, can be further assessed using our model which enables better prediction of the risk of developing SCAR.