In a new study from King’s College London, researchers developed a new way to detect early warning signs of sudden cardiac arrest, by analyzing the shape of a person’s heart with machine learning methods.
The team analyzed MRI images from 156 patients with dilated cardiomyopathy, a deadly heart muscle disease.
The patients who later experienced sudden cardiac arrest could be detected by subtle changes to their heart shape, thought to be signs of worsening disease.
Data shows that only 50 percent of sudden cardiac arrest cases involve prior symptoms. Many of these patients’ lives could be saved with implantable defibrillators if only clinicians knew that they were at risk.
Given to the right patients, implantable defibrillators prevent cardiac arrest by automatically shocking the heart back into a normal rhythm.
However, finding out which patients are actually in need of an implantable defibrillator is a difficult challenge.
Each defibrillator implantation costs around €46,000 over the lifetime of the patient and brings a risk of very powerful unnecessary shocks.
The team says with further testing and more patient data, they hope to develop their heart shape analysis method into a practical tool for cardiologists to select the right patients for treatment with implantable defibrillators.
An online service providing access to the new heart shape analysis method is now being released.
Cardiologists and other researchers only need to upload the borders of the heart, which is anonymous, and safe to share.
In return, the service provides a 3D shape model and a sudden cardiac arrest risk score, so that the newly developed risk prediction techniques can be further tested by other groups.
The researchers say that the new heart shape analysis could also be used for other diseases because the heart changes shape for other reasons as well, such as hypertension or high blood pressure.
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The study is published in EP Europace. One author of the study is Dr. Pablo Lamata.
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