Big Data Maps Out Heart Failure Characteristics
In an effort to better predict how diverse patients will respond to treatments, clinical investigators have used big data tools to classify for the first time three distinct categories of a cardiac syndrome called heart failure with preserved ejection fraction (HFpEF).
In an effort to better predict how diverse patients will respond to treatments, clinical investigators have used big data tools to classify for the first time three distinct categories of a cardiac syndrome called heart failure with preserved ejection fraction (HFpEF).
The findings of the research, which conceptually varies from the genetic approach to precision medicine by also considering other factors, were published in Circulation.
The majority of chronic medical conditions are a result of environmental influences and complex interactions between various risk factors, said first author Sanjiv Shah, M.D., a cardiologist at Northwestern University's Feinberg School of Medicine. We need a new approach to understand them.
Also See: EHR Data Valuable to Study of Cardiac Structure, Function
In 2007, Shah established a novel clinic at Northwestern dedicated to the diagnosis and treatment of HFpEF, a common heart failure syndrome also known as diastolic heart failure. The clinics research component, funded by an American Heart Association grant, aimed to combat the problem of heterogeneity in HFpEF.
In the new study, Shah and colleagues, including Rahul Deo, M.D., of the University of California, San Francisco, analyzed a combination of 67 laboratory, electrocardiographic, and echocardiographic markers with machine learning algorithms to find patterns in 397 patients with HFpEF. They call the process phenomapping.
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.
The investigators validated the results in an additional 107 patients with the syndrome.
Currently, therapy options do not improve outcomes for the 3 million adults in the United States afflicted with HFpEF. The studys findings may ultimately help improve the track record of overwhelmingly unsuccessful clinical trials.
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.
The study is available here.
The findings of the research, which conceptually varies from the genetic approach to precision medicine by also considering other factors, were published in Circulation.
The majority of chronic medical conditions are a result of environmental influences and complex interactions between various risk factors, said first author Sanjiv Shah, M.D., a cardiologist at Northwestern University's Feinberg School of Medicine. We need a new approach to understand them.
Also See: EHR Data Valuable to Study of Cardiac Structure, Function
In 2007, Shah established a novel clinic at Northwestern dedicated to the diagnosis and treatment of HFpEF, a common heart failure syndrome also known as diastolic heart failure. The clinics research component, funded by an American Heart Association grant, aimed to combat the problem of heterogeneity in HFpEF.
In the new study, Shah and colleagues, including Rahul Deo, M.D., of the University of California, San Francisco, analyzed a combination of 67 laboratory, electrocardiographic, and echocardiographic markers with machine learning algorithms to find patterns in 397 patients with HFpEF. They call the process phenomapping.
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.
The investigators validated the results in an additional 107 patients with the syndrome.
Currently, therapy options do not improve outcomes for the 3 million adults in the United States afflicted with HFpEF. The studys findings may ultimately help improve the track record of overwhelmingly unsuccessful clinical trials.
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.
The study is available here.
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