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

Abstract

Heart transplant patients are followed with periodic right heart catheterizations (RHCs) to identify post-transplant complications and guide treatment. Post-transplant positive outcomes are associated with a steady reduction of right ventricular and pulmonary arterial pressures, toward normal levels of right-side pressure (about 20mmHg) measured by RHC. This study shows more information about patient progression is obtained by combining standard RHC measures with mechanistic computational cardiovascular systems models. This study shows: to understand how cardiovascular system models can be used to represent a patient's cardiovascular state, and to use these models to track post-transplant recovery and outcome. To obtain reliable parameter estimates comparable within and across datasets, we use sensitivity analysis, parameter subset selection, and optimization to determine patient specific mechanistic parameter that can be reliably extracted from the RHC data. Patient-specific models are identified for ten patients from their first post-transplant RHC and longitudinal analysis is done for five patients. Results of sensitivity analysis and subset selection show we can reliably estimate seven non-measurable quantities including ventricular diastolic relaxation, systemic resistance, pulmonary venous elastance, pulmonary resistance, pulmonary arterial elastance, pulmonary valve resistance and systemic arterial elastance. Changes in parameters and predicted cardiovascular function post-transplant are used to evaluate cardiovascular state during recovery in five patients. Of these five patients, only one patient showed inconsistent trends during recovery in ventricular pressure-volume relationships and power output. At a four-year recovery time point this patient exhibited biventricular failure along with graft dysfunction while the remaining four exhibited no cardiovascular complications.