Deep phenotyping of cardiac function in heart transplant patients using cardiovascular systems models.

Heart transplant patients are followed for up to a year with periodic right heart catheterizations (RHCs) to identify post-transplant complications and guide treatment. Positive outcomes are associated with a steady reduction of right ventricular and pulmonary artery pressure post-transplant toward normal levels of right-side pressure (about 20mmHg) as measured by right heart catheterization. However, standard RHC measures have the potential to identify much more about the progression of the cardiovascular state during recovery if combined with mechanistic computational cardiovascular system models. The purpose of this study is two-fold: to understand how cardiovascular system models can be used to represent a patient's cardiovascular state and to use these models on a subset of patients where we have longitudinal RHCs over the span of the first year to track post-transplant recovery and outcome. The cardiovascular systems model used here and its underlying mechanistic parameters are evaluated using sensitivity analysis, parameter subset selection and parameter identifiability to understand what information about the cardiovascular state can be reliably extracted from RHC data. Patient-specific models are then identified for ten patients from their first post-transplant RHC. Parameters representing ventricular diastolic filling, systemic resistance, pulmonary vein elastance, pulmonary resistance, pulmonary artery elastance, pulmonary valve resistance and aortic elastance are identified for each patient using this approach. Multiple RHCs from five of these patients are analyzed longitudinally to visualize progression of recovery using simulated left and right ventricular pressure-volume loops exhibiting the potential for this method to understand post-transplant remodeling and recovery of the entire cardiovascular system.