CBC Talk on Patient-specific Computational Models of Dyssynchronous Heart Failure and Cardiac Resynchronization Therapy for Clinical Diagnosis and Decision Support - June 18, 2015
Total number of participants: 25
Total number of guests outside of CBC: 17
Number of different nationalities represented: 10
Total number of speakers: 1
Total number of talks: 1
Speaker: Dr. Chris Villongco (Cardiac Mechanics Research Group, University of California San Diego)
Title: Patient-specific Computational Models of Dyssynchronous Heart Failure and Cardiac Resynchronization Therapy for Clinical Diagnosis and Decision Support
Date/Time: Thursday June 18th 12:00
Abstract: Dyssynchronous heart failure (DHF) is a severe clinical syndrome where conduction block in the left bundle branch causes dis-coordinated ventricular contraction and markedly reduced cardiac output. Cardiac resynchronization therapy (CRT) is the most cost-effective treatment for improving DHF symptoms and survival rates. However, CRT fails to effect objective physiological response (i.e. reverse remodeling) linked to long-term survival in 40% of patients. Despite its use for the last 20 years, the chronic therapeutic mechanisms of CRT are not well understood. Moreover, current clinical guidelines remain inadequate for making prudent recommendations for CRT use on an individual patient basis.
In this work, we constructed computational models of DHF and CRT physiology which integrate an individual patient's anatomical, electrophysiological, biomechanical, and hemodynamic clinical data to quantitatively assess inter-patient physiological differences that relate to long-term reverse remodeling. We found that the severity (during DHF) and improvement (during CRT) of the myocardial mechanical work distribution in the ventricles are implicated in reverse remodeling. In addition, we demonstrate that model-derived biomarkers of electrical function during DHF and CRT can potentially serve as useful predictors of long-term outcomes and therapy optimization.
Center for Biomedical Computing (CBC) aims to develop and apply novel simulation technologies to reach new understanding of complex physical processes affecting human health. We target selected medical problems where insight from mathematical modeling can contribute to changing clinical practice.