Evaluating Patient-Specific Computational Models of Dyssynchronous Heart Failure

Cardiac resynchronization therapy (CRT) is an effective treatment for dyssynchronous heart failure. Most patients perform better during clinical tests of cardiac function and may even have their hearts reverse remodel to a more normal state. As many as 30-40% of patients, however, do not respond. A focus of current research is identifying which patients will respond to CRT, though current clinical indications for CRT are based on dyssynchrony and heart failure and not on validated predictors of CRT response. In this thesis, five patient-specific computational models based on non-invasive data were developed and compared with eight previous patient-specific models that were based on more detailed and invasive clinical information to test whether the dyssynchrony metrics from the original study were still predictive in the new group. Model properties such as the volume fraction of negative work (VFNW) and coefficient of variation of work (COVW), that had correlated with patient outcomes in the original cohort, did not correlate with reverse remodeling as measured by reduction in end-systolic volume in the new group. The sensitivity analysis showed that these quantities were sensitive to the parameters of ventricular filling mechanics that could not be included in patient-specific models based on non-invasive data. Non-invasive estimates of filling parameters are available, but their reliability has been questioned. We conclude that it is likely to be necessary to obtain invasive measurements of diastolic pressures for patient-specific models to predict CRT outcomes. However, these measurements are available by cardiac catheterization, which could be justified for CRT patients.