Digital twin for cardiovascular surgery planning
This technology is an algorithm that uses patient-specific data to evaluate potential cardiovascular surgical options.
Unmet Need: Optimal treatment strategy for positive cardiovascular surgery outcomes
Cardiologists are often faced with difficult decisions regarding optimal treatment strategies for neonates with congenital heart defects like borderline left ventricles (BLV). Ventricle size plays an important role in the decision between procedures to create adequate circulation with only one ventricle or to restore two-ventricle circulation. Determining which method to pursue has serious and lifelong implications for the infant’s heart function and quality of life, as both procedures pose mortality and morbidity risks. A method to help surgeons predict post-surgical outcomes can enable physicians to choose the procedure that yields the most favorable hemodynamic outcome for the patient.
The Technology: Algorithm using patient data for cardiovascular surgery insights
This technology is an algorithm that uses patient-specific data to help physicians perform ‘virtual surgery’ to determine the best surgical intervention for BLV patients. The algorithm uses patient-specific geometric data from imaging and hemodynamic data (including arterial pressures) from echocardiographic reports, along with an optimization method to estimate the remaining model parameters. These are used to model a patient’s pre-operative state and post-operative predicted hemodynamic outcomes after ‘virtual surgery’. Together, this model can serve as a predictive pre-operative tool for clinicians to help determine the most favorable surgical outcome.
This technology has been verified using clinical outcomes from patients.
Applications:
- Clinical tool for modeling patient surgical outcomes
- Research tool for studying cardiac defects
- Tool for modeling medical device response
- Model for the development of cardiac medical devices
Advantages:
- Uses patient-specific data
- Mechanistically-derived algorithm
- Predictive of post-surgical blood flow patterns
- Personalized medicine
Lead Inventor:
David Kalfa, M.D.
Patent Information:
Patent Pending
Related Publications:
Tech Ventures Reference:
IR CU2524
Licensing Contact: Kristin Neuman