In a recent study, researchers have devised a machine learning algorithm which combines three variables routinely collected during clinic visits and demonstrates how the emerging field of bioinformatics could transform patient care.

It takes a patient age, urinary albumin/creatinine ratio (UACR), and cardiovascular disease history to successfully identify 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 a researcher, Yang Xie. "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," he added.

Researchers used patient data under controlled trials that tested intensive vs. standard blood pressure-lowering treatments -- the Systolic Blood Pressure Intervention Trial (SPRINT) and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. The SPRINT trial included 9,361 non-diabetic hypertensive adults at an elevated risk of a cardiovascular event, while ACCORD enrolled 10,251 patients with Type 2 diabetes.

"I think our algorithm can help us identify high-risk patients who will most likely benefit from intensive blood pressure reduction. Long-term intensive HBP drug therapy can reduce the risk of heart failure and death, but it carries an increased risk of side effects," said another researcher, Wanpen Vongpatanasin. The researchers' machine learning method determined three simple criteria to identify adults with high blood pressure who are at the highest risk for early major adverse cardiovascular events -- such as cardiovascular death, heart attack, or stroke.

Those criteria are: an age of 74 or older, a Urine Albumin-to-Creatinine Ratio (UACR) of 34 or higher, and a history of a clinical cardiovascular disease, such as heart disease, stroke, or heart failure. Patients who met one or more of the three criteria were predicted to be among a high-risk group who had a greater benefit from intensive blood pressure-lowering treatment. In contrast, the team found that patients younger than age 74 who had a UACR less than 34 and no history of cardiovascular disease may do equally as well with less intensive treatment.

"We feel that our findings have major clinical implications since, in addition to its predictive effects, the model generated here is simple and easy to implement in clinical practice without additional lab tests or computational tools," said Xie. "We hope that clinicians can someday use this algorithm to identify which patients should receive standard versus intensive treatment, and we hope to design a prospective clinical trial to further validate this algorithm," he added. The study appears in American Journal of Cardiology.