New algorithm predicts patients who will benefit from high blood pressure treatment

Using data from large clinical trials, UT Southwestern researchers developed a way to predict which patients will benefit most from aggressive high blood pressure treatment. The machine learning algorithm they devised combines three variables routinely collected during clinic visits and demonstrates how the emerging field of bioinformatics could transform patient care. Their work, available online now and publishing July 15 in the American Journal of Cardiology, describes a risk prediction model in which patient age, urinary albumin/creatinine ratio (UACR), and cardiovascular disease history successfully identified hypertensive patients for whom the benefits of intensive therapy outweigh the risks. "Large randomized trials have provided inconsistent evidence regarding the benefit of intensive blood pressure lowering in hypertensive patients," said corresponding author Dr. Yang Xie, Director of the Quantitative Biomedical Research Center at UT Southwestern and of the University's Bioinformatics Core Facility. "To the best of our knowledge, this is the first study to identify a subgroup of patients who derive a higher net benefit from intensive blood pressure treatment."