Study Leverages Big Data To Map Categories Of Heart Failure

By Christine Kern, contributing writer

Phenomapping analysis provides an understanding of chronic heart problems.

A study published by Circulation shows how researchers leveraged big data to classify three distinct categories of a particular cardiac syndrome. The research is part of an effort to better predict how diverse patients will respond to treatments of a cardiac syndrome called heart failure with preserved ejection fraction (HFpEF).

“In recent years, the medical community and the government have recognized the need for ‘precision medicine,’ which tailors therapies to individual patients to tackle heterogeneous chronic medical conditions”, said first author Sanjiv Shah, M.D., a cardiologist at Northwestern University's Feinberg School of Medicine. “The majority of chronic medical conditions are a result of environmental influences and complex interactions between various risk factors. We need a new approach to understand them.”

In 2007, Shah began research committed to the diagnosis and treatment of HFpEF, also known as diastolic heart failure. He established a novel clinic at Northwestern where he conducted research funded by an American Heart Association grant aimed to combat the problem of heterogeneity in HFpEF.

The study, “Phenomapping for Novel Classifications of Heart Failure With Preserved Ejection Fraction,” used phenomapping techniques to analyze a combination of 67 laboratory, electrocardiographic, and echocardiographic markers with machine learning algorithms to find patterns in 397 patients with HFpEF.

“These types of approaches are typically used for genetic data, but we instead used the computer algorithms on non-genetic data gathered from our patients in the clinic,” Shah said. “Our analysis revealed, for the first time, that there are three types of HFpEF that are very different in terms of clinical characteristics and outcomes.”

Shah hopes that their findings may help improve outcomes for the approximately three million individuals afflicted with HFpEF in the United States. “Large-scale clinical trials have failed to demonstrate a significant benefit for any HFpEF treatment. That was really the impetus behind the phenomapping analysis,” Shah said. “In future clinical trials, we hope to match specific groups identified by our study to specific, tailored treatments, thereby potentially leading to more successful clinical trials and ultimately achieving our goal of precision medicine for our patients.”