Abstract 15033: Development of an Interpretable Machine Learning Model for Predicting Individual Response to Antihypertensive Treatments

Abstract

Introduction: Accurate prediction of individual response to anti-hypertensive treatment is crucial in enhancing the safety and efficiency of offering appropriate regimens to attain desired blood pressure (BP) goals. Hypothesis: Machine learning (ML) based models could accurately predict the BP reduction of different antihypertensive regimens. Methods: We utilized data from the LIGHT study, a pragmatic, cluster-randomized trial conducted in 94 primary care institutions in China, involving 13146 visits of 5192 patients who were required to attend the clinic for hypertension management every 3 months during an overall study period of 1 year. Clinical and laboratory variables, baseline BP, and anti-hypertensive therapies at baseline and at follow-up were used to develop models for predicting post-treatment individual BP response. Five ML algorithms were applied as candidate models, and mean squared error (MSE) was used to evaluate the model performance ( panel A ). Results: The mean (standard deviation) age of the study population at baseline was 64.2 (14.0) years, and 2207 (42.5%) were female. 29 features selected by the least absolute shrinkage and selection operator method and purposeful selection were used to build the model, including age, sex, weight, waist circumference, BMI, baseline systolic and diastolic BP, smoking status, visit interval, history of coronary heart disease, diabetes, and dyslipidemia, and baseline and prescript antihypertensive drugs. Among all candidate models, the Catboost model had the best performance with a training MSE of 149.6 and a validation MSE of 135.2 ( panel B ). The most important features include baseline BP, age, weight, and BMI ( panel C and D ). Conclusions: We developed an ML-based model for predicting the response to antihypertensive regimens for assisting clinicians to achieve individualized antihypertensive treatment effectively and safely.