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Heart transplant patients are followed with periodic right heartcatheterizations (RHCs) to identify post-transplant complications and guidetreatment. Post-transplant positive outcomes are associated with a steadyreduction of right ventricular and pulmonary arterial pressures, toward normallevels of right-side pressure (about 20mmHg) measured by RHC. This study showsmore information about patient progression is obtained by combining standardRHC measures with mechanistic computational cardiovascular systems models. Thisstudy shows: to understand how cardiovascular system models can be used torepresent a patient's cardiovascular state, and to use these models to trackpost-transplant recovery and outcome. To obtain reliable parameter estimatescomparable within and across datasets, we use sensitivity analysis, parametersubset selection, and optimization to determine patient specific mechanisticparameter that can be reliably extracted from the RHC data. Patient-specificmodels are identified for ten patients from their first post-transplant RHC andlongitudinal analysis is done for five patients. Results of sensitivityanalysis and subset selection show we can reliably estimate sevennon-measurable quantities including ventricular diastolic relaxation, systemicresistance, pulmonary venous elastance, pulmonary resistance, pulmonaryarterial elastance, pulmonary valve resistance and systemic arterial elastance.Changes in parameters and predicted cardiovascular function post-transplant areused to evaluate cardiovascular state during recovery in five patients. Ofthese five patients, only one patient showed inconsistent trends duringrecovery in ventricular pressure-volume relationships and power output. At afour-year recovery time point this patient exhibited biventricular failurealong with graft dysfunction while the remaining four exhibited nocardiovascular complications.

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