This novel method can predict fatal heart disease, says study

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A novel method called stress cardiac MRI can not only diagnose heart disease but can also predict which cases are potentially fatal, suggests new research. The study showed that predicting mortality is a key requirement for technology to be more widely used.

In addition, results suggest cardiac magnetic resonance (CMR) has the potential to be a non-invasive, non-toxic alternative to stress echocardiograms, catheterizations and stress nuclear exams in identifying the severity of the coronary artery disease.

"We've known for some time that CMR is effective at diagnosing coronary artery disease, but it's still not commonly used and represents less than one percent of stress tests used in this country," said senior author Robert Judd, co-director of the Duke Cardiovascular Magnetic Resonance Center in the US. For the study, researchers analyzed data from more than 9,000 patients who underwent CMR.

The findings, published in JAMA Cardiology, showed that for patients without any history of heart disease and at low-risk based on traditional clinical criteria, those with an abnormal CMR scan were 3.4 times more likely to die compared to patients with a normal CMR scan.

For the entire patient population, there was a strong association between an abnormal stress CMR and mortality, even after adjusting for patient age, sex and cardiac risk factors, the team found.

While non-invasive cardiac stress testing is a cornerstone in the clinical management of patients with known or suspected coronary artery disease, CMR works as well or better than other exams at identifying heart wall motion, cell death and the presence of low blood flow, said the study.

Furthermore, technology does not require any radiation exposure, which is essential in nuclear stress tests. "With the findings from this study suggesting that stress CMR is effective in predicting mortality, we provide a strong basis for a head-to-head study between stress CMR and other modalities," Judd noted.

(IANS Inputs)

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