Now, simpler ways to predict heart failure

| Updated on: Nov 11, 2012

Researchers, including an Indian origin doctor, have developed a simple model that uses age, race and two blood bio-markers to predict the risk of heart failure.

The most widely used models for predicting heart failure rely on a complex combination of lifestyle, demographic and cardiovascular risk factor information.

However, Vijay Nambi and Christie Ballantyne from Baylor College of Medicine and The Methodist Hospital Center for Cardiovascular Disease Prevention presented new data that show two biomarkers can improve heart failure risk prediction as part of a simpler model.

Nambi and Ballantyne said their simpler model could use information from lab reports to assess heart failure risk, and could be useful to both patients and doctors.

Their model uses age, race, and the blood concentrations of two blood biomarkers — troponin T and NT-proBNP — to show whether or not a patient is at elevated risk for heart failure.

Applying the model to patient data from the ongoing ARIC study (Atherosclerosis Risk in Communities), the researchers found their simple heart failure risk model was comparable to more complex models that take into account age, race, systolic blood pressure, antihypertensive medication use, smoking or former smoking, diabetes, body-mass index, prevalent coronary heart disease and heart rate.

The protein troponin T is part of the troponin complex and is traditionally used in the diagnosis of heart attacks.

NT-proBNP is an inactive peptide fragment left over from the production of brain natiuretic peptide (BNP), a small neuropeptide hormone that has been shown to have value in diagnosing recent and ongoing congestive heart failure.

Researchers used both these markers in the prediction of future heart failure (over 10 years) thereby understanding which individuals among a general population are at the highest risk of heart failure.

Finally, they showed that adding these biomarkers to the existing models resulted in the best risk prediction models.

Published on November 11, 2012

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