Johns Hopkins University scientists have invented a new tool for predicting which patients suffering from complex inflammatory heart disease are at risk of sudden cardiac arrest.
“This robust new personalized technology outperformed clinical metrics in forecasting future arrhythmia and could transform the management of cardiac sarcoidosis patients,” said senior author Natalia Trayanova, a Johns Hopkins professor.
Doctors don’t have an exact method to evaluate whether patients have cardiac sarcoidosis, a condition causing inflammation and scarring that can trigger irregular heartbeats, are likely to have a deadly arrhythmia, meaning that some patients won’t survive. A recent meta-analysis cited in the study found that roughly only one-third of CS patients receive sufficient treatment.
“There is an urgent clinical need for better predictive tools,” said Trayanova, who is also a professor at the Johns Hopkins School of Medicine. “Some CS patients perish, often in the prime of their life, while others have a defibrillator implanted unnecessarily and often deal with the complications, including infections, device malfunction, and inappropriate shocks, without receiving any real benefit.”
They collected high-dimensional data with the goal of better understanding how scarring and inflammation affect the heartbeat,” said Tray nova.
The team then combined data from the mechanistic simulations, along with additional patient and imaging data, to train an algorithm to predict the possibility of arrhythmia leading to cardiac arrest. The tool notably outperformed standard clinical metrics for predicting cardiac arrest in CS patients.
At last, the team compared their simulations against scans of lesions in the hearts of the patients who had subsequently undergone a method to reset their heartbeats, finding that their predictions were pretty close to the actual results.