Predicting laboratory aspirin resistance in Chinese stroke patients using machine learning models by GP1BA polymorphism.
Journal:
Pharmacogenomics
PMID:
39440554
Abstract
This study aims to use machine learning model to predict laboratory aspirin resistance (AR) in Chinese stroke patients by incorporating patient characteristics and single nucleotide polymorphisms of and . 2405 patients were analyzed to measure the Mutation frequency of rs6065 and rs730012. 112 patients with first-stroke arteriostenosis were prospectively enrolled to establish machine learning model. GP1BA rs6065 mutation frequency is 5.26% and LTC4S rs730012 is 14.78%. rs6065 CT patients have more sensitivity to aspirin than CC genotype. Simple linear regression identified significant associations with age, smoking, HDL and rs6065. Random forest (RF) and extreme gradient boosting (XGBoost) demonstrated predictive capabilities for AR. Findings suggest pre-identifying rs6065 could optimize aspirin treatment, enabling personalized care and future research avenues.