Novel Approach Predicts Outcomes in Heart Failure Patients

A new method has been identified that determines whether a patient's heart will fail, which in the future may help physicians better treat patients and tailor therapeutic interventions.

This novel method tests energy metabolism in the heart and has proved to be a significant predictor of clinical outcomes, independent of a patient's symptoms, race, or strength of the heart and helps determine which patients with heart failure (HF) will do well and which patients will not.

Scientists at the Johns Hopkins University School of Medicine (Baltimore, MD, USA) measured energy metabolism in 58 heart failure patients with nonischemic cardiomyopathy, or heart failure not due to blocked arteries and 17 healthy subjects using magnetic resonance spectroscopy (MRS). They then followed these patients for a median of 4.7 years, recording any hospitalizations, heart transplantation, placement of a ventricular assist device and death from all causes.

The investigators looked at adenosine triphosphate (ATP), an energy source for heart muscle cells, and an energy reserve called creatine kinase (CK), an enzyme that interacts with ATP to keep the energy supply constant in a beating heart. They measured the rate of ATP synthesis through CK, called CK flux, using MRS (GE Healthcare Technologies, Pittsburgh, PA, USA). They found that measurements of CK flux were significantly lower in heart failure patients whose condition had worsened.

Gurusher Panjrath, MD, an assistant professor of medicine and co-lead author of the study said, “While various used methods are currently used for prediction, none of these methods are reflective of the underlying mechanism in the weak heart. Furthermore, some of these measures are not very consistent in their predictive ability. There is a need for newer methods, which could potentially be more specific and reproducible. By targeting impaired energy metabolism, it may also be possible in the future to develop and tailor therapies to this new target.” The study was published on December 11, 2013, in the journal Science Translational Medicine.

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Johns Hopkins University School of Medicine
GE Healthcare Technologies