4D Digital Twin Heart Model Improves CRT Outcomes

Suboptimal placement of pacing leads remains a persistent barrier to response. To help address this challenge, researchers have developed a patient-specific four-dimensional heart model built from cardiac MRI imaging. A new study shows that this model can be used in real patients to guide implantation decisions.

Developed at the University of Calgary (Alberta, Canada), the 4D heart model creates a digital twin of each patient’s heart using cardiac MRI images. The personalized “beating heart” representation depicts motion patterns that matter for resynchronization. Clinicians use the model to plan cardiac resynchronization therapy (CRT) by targeting lead locations intended to coordinate ventricular contraction and improve pump performance.

In the MAPIT-CRT clinical trial, 202 patients with heart failure were enrolled across seven Canadian centers. Participants were assigned to CRT guided by the virtual patient model or to standard care without model guidance. Outcomes were assessed six months after implantation.

Model-guided therapy produced a 10.8% increase in heart function compared with 5.8% with standard care. Sixty-six percent of patients in the model-guided group improved versus 52% with standard treatment. Investigators reported nearly double the improvement in left ventricular ejection fraction, a key measure of heart pumping ability, without added procedure time, complications, or recovery risks.

Findings were published in Circulation: Arrhythmia and Electrophysiology. The work originated at the Cumming School of Medicine and the Nelson Precision Medicine and Learning Health System (PULSE) Center for Innovation at the University of Calgary. The technology was also designed for practical implementation through an easy-to-adopt web-based platform.

“This new type of digital technology holds strong potential for guiding the treatment of heart failure, even beyond guiding pacemaker therapy. Our ability to generate accurate 4D digital representations of each patient's heart now allows us to explore new ways to detect disease and accurately predict future outcomes,” said James White, MD, professor at the Cumming School of Medicine and director of the Nelson Precision Medicine and Learning Health System (PULSE) Center for Innovation, University of Calgary.

“Previous approaches required complex software and integrations to implement, so we focused on making an easy-to-adopt solution. Complex problems can now be solved using easy-to-use web-based platforms, allowing innovation to scale much faster,” added Dr. White.

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Cumming School of Medicine, University of Calgary