In a new study from Johns Hopkins University, researchers have developed a new tool for predicting which patients suffering from complex inflammatory heart disease are at risk of sudden cardiac arrest.
Their method is the first to combine models of patients’ hearts built from multiple images with the power of machine learning.
Currently, doctors don’t have precise ways to find which patients with cardiac sarcoidosis, a condition causing inflammation and scarring that can trigger irregular heartbeats, are likely to have a sudden death.
Roughly only one-third of such patients receive adequate treatment.
In the study, the team created digital three-dimensional models of the hearts of 45 heart patients.
To do this, they took the novel approach of combining data from two different kinds of heart scans: Contrast-enhanced cardiac MRIs, which detect fibrosis, or scarring, and PET scans, which detect inflammation.
The team used computer simulations to apply a series of electrical signals at various locations throughout each of the models and gathered millions of data points measuring each heart’s reaction.
They then combined data from the mechanistic simulations, along with additional patient and imaging data, to develop and train an algorithm to predict the likelihood of arrhythmia leading to cardiac arrest.
The tool strongly outperformed standard clinical metrics for predicting cardiac arrest in heart patients.
Lastly, the team compared their simulations against scans of lesions in the hearts of the patients who had subsequently undergone a procedure to reset their heartbeats, finding that their predictions were consistent with actual outcomes.
They hope their tool could transform the management of patients with inflammatory heart disease, lowering the number of unnecessary defibrillator device implantations while ensuring that patients at risk of sudden cardiac death are protected.
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The study is published in Science Advances. One author of the study is Natalia Trayanova.
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