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

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.

This study aims to comprehensively investigate the role of genomic factors in CBZ- and ALLO-induced SCARs using machine-learning.

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.

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.

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.

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.