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A simple Test may predict risk for heart failure

A simple Test may predict risk for  heart failure

The prediction of heart failure (HF) may now be possible by simply stepping on a scale in a way similar to measuring your weight if a new study published in the Journal of the American Heart Association is to be believed.

The study found that the people having lower leg bioimpedance are at a higher risk for heart failure and vice versa.

Bioimpedance, more frequently used to calculate body fat, uses low electrical currents to measure resistance within a tissue. Water, blood and other fluids easily conduct electricity and have lower bioimpedance than something more solid such as muscle tissue, which has higher resistance.

Erik Ingelsson, professor of medicine, Stanford University, and colleagues examined data on more than 500,000 U.K. patients between the ages of 49 and 69, to establish new risk factors of HF, which potentially could enable early diagnosis and preemptive treatment.

Heart failure is a condition in which the heart fails to pump blood efficiently enough to meet the body’s needs.

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For the study, researchers used computer-assisted techniques to weed through 3,646 variables reflecting lifestyle, health and disease-related factors for each patient. The results did not surprise – among the top predictors of heart failure were having a previous heart attack, chronic heart disease and a history of diabetes. And, then came leg bioimpedance.

“We homed in on that because the other risk factors are well established for heart failure, whereas [leg] bioimpedance was one of the novel risk factors and one that is easily measurable,” said the study’s senior author, Dr. Ingelsson.

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Bioimpedance was measured using a body composition analyzer, which looks like a standard scale but with handlebars. Electrodes beneath each foot send small electric currents and measure impedance, or resistance met in the tissue.

Key Findings:

  •  Leg bioimpedance was lower in those who developed heart failure during an up to 9.8‐year follow‐up.
  • When adjusting for known heart failure risk factors, leg bioimpedance was inversely related to heart failure (hazard ratio [95% confidence interval], 0.60 [0.48–0.73] and 0.75 [0.59–0.94], in age‐ and sex‐adjusted and fully adjusted models, respectively, comparing the upper versus lower quartile).
  • A model including leg bioimpedance, age, sex, and self‐reported history of myocardial infarction showed good discrimination for future heart failure hospitalization and good calibration.

Ingelsson said it’s possible that people who had lower leg bioimpedance in the study had early stages of fluid buildup in their legs, a very common symptom of heart failure. The low bioimpedance may have identified a level of water retention that had yet to be detected by health care providers and before the appearance of other heart failure signs, such as fatigue or shortness of breath.

Dr. Barry Borlaug, a Mayo Clinic cardiologist and medical professor who was not involved in the study, said more research is necessary, but he sees a potential for how leg bioimpedance could be used to predict heart failure, particularly among people who lead inactive lifestyles.

Borlaug said the findings need to be validated within another population to ensure the results can be reproduced, although he admitted that since there were more than 500,000 patients in the study, “the risk that this finding is spurious or due to a play of chance is fairly low.”

The study’s authors suggest that a simple algorithm they developed based on their findings could provide an accurate prediction of developing heart failure within eight years. The formula combines leg bioimpedance with a patient’s age, sex and whether the person has had a heart attack or not.

“Leg bioimpedance is inversely associated with heart failure incidence in the general population. A simple model of exclusively noninvasive measures, combining leg bioimpedance with a history of myocardial infarction, age, and sex provides accurate predictive capacity,” concluded the authors.

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Source: With inputs from JAHA

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