Credit: Hermenegild Arevalo, Natalia Trayanova via Johns Hopkins)
New York, May 11 (IANS) A team of researchers from Johns Hopkins University has developed a non-invasive 3D virtual heart to help doctors determine whether a patient faces the risk of a life-threatening arrhythmia and needs a defibrillator implant.
When electrical waves in the heart run amok in a condition called arrhythmia, sudden death can occur.
To save the life of a patient at risk, doctors currently implant a small defibrillator to sense the onset of arrhythmia and jolt the heart back to a normal rhythm.
But how should doctors decide which patients truly need an invasive, costly electrical implant that is not without health risks of its own?
In a proof-of-concept study published in the journal Nature Communications, the team reported that its new digital approach yielded more accurate predictions than the imprecise blood pumping measurement now used by most physicians.
Our virtual heart test significantly outperformed several existing clinical metrics in predicting future arrhythmic events, said Natalia Trayanova, the Murray B. Sachs professor of biomedical engineering.
This non-invasive and personalised virtual heart-risk assessment could help prevent sudden cardiac deaths and allow patients who are not at risk to avoid unnecessary defibrillator implantations, Trayanova explained.
For the study, Trayanova's team formed its predictions by using the distinctive magnetic resonance imaging (MRI) records of patients who had survived a heart attack but were left with damaged cardiac tissue that predisposes the heart to deadly arrhythmias.
The study involved data from 41 patients who had survived a heart attack and had an ejection fraction - a measure of how much blood is being pumped out of the heart - of less than 35 percent.
All 41 patients in the study received the implants because of their ejection fraction scores.
The Johns Hopkins team invented an alternative to these scores by using pre-implant MRI scans of the recipients' hearts to build patient-specific digital replicas of the organs.
Using computer-modeling techniques, the geometrical replica of each patient's heart was brought to life by incorporating representations of the electrical processes in the cardiac cells and the communication among cells.
In some cases, the virtual heart developed an arrhythmia and in others it did not.
The result, a non-invasive way to gauge the risk of sudden cardiac death due to arrhythmia, was dubbed as virtual-heart arrhythmia risk predictor (VARP).
The VARP results were compared to the defibrillator recipients' post-implantation records.
Patients who tested positive for arrhythmia risk by VARP were four times more likely to develop arrhythmia than those who tested negative.
Furthermore, VARP predicted arrhythmia occurrence in patients four-to-five times better than the ejection fraction and other existing clinical risk predictors, both non-invasive and invasive.
"We demonstrated that VARP is better than any other arrhythmia prediction method that is out there," Trayanova noted.
By accurately predicting which patients are at risk of sudden cardiac death, the VARP approach will provide the doctors with a tool to identify those patients who truly need the costly implantable device, the authors pointed out.
In addition to eliminating unnecessary device implantations, Trayanova noted that this new risk prediction methodology can also be applied to patients who had prior heart damage but whose ejection fraction score did not target them for therapy under current clinical recommendations.
Thus, VARP has the potential to save the lives of a much larger number of at-risk patients.
