Machine Learning Algorithm to Guide Cardiac Resynchronization Therapy Personalization
Inventors: Will Richardson, Anamul Haque, William Douglas Stubbs Jr.
Keywords: Sustainable Manufacturing, Biotechnology, Manufacturing
Market Overview
This patient risk-assessment machine learning algorithm predicts patient response to cardiac resynchronization therapy (CRT) intervention enabling more informed selection criteria and improved rates of therapy success. Approximately 90,000 patients receive cardiac resynchronization therapy per year, with only 50% of that population benefitting. This directly results from the uncertainties surrounding which patients will actually benefit from the procedure. Providing insights into patient-response prior to intervention could significantly improve CRT success rates while minimizing the occurrence of unnecessary adverse effects. Clemson University researchers have developed a machine learning patient risk-assessment algorithm possessing strong predictive capability for patient response to cardiac resynchronization therapy.
Applications:
Cardiac Resynchronization, Manufacturing, Equipment, Learning Algorithms, Dataset Analysis
Technical Summary:
The patient-response assessment machine learning algorithm is a good predictor of patient benefit, or lack thereof, from cardiac resynchronization therapy. This algorithm utilizes patient characteristics including but not limited to: demographics, comorbidities, medication history, circulating protein biomarkers, and imaging data. A clearer picture of patient response to CRT is generated that, when coupled with existing methods to inform patient candidacy, can improve outcomes. These outcomes include both increased intervention success rates and reduced unnecessary adverse event occurrences. This algorithm has been proven to correctly predict patient outcomes in previously conducted CRT clinical trials.
Advantages:
- Patient-specific characteristics inform algorithm, personalizing inclusion criteria.
- Personalized risk-assessment, improving intervention success rates.
- Minimal hardware requirements, accelerated patient-evaluation on any system.
Technology Overview
State of Development
TRL 6: Alpha Prototype
Patent Type
N/A
Category
Serial Number
63/240,146
CURF Reference No.
2021-027
Inventors
Will Richardson, Anamul Haque, William Douglas Stubbs Jr.
For More Info, Contact:
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