Assessment of cardiovascular function by combining clinical data with a computational model of the cardiovascular system

ObjectiveA sufficient understanding of patients' cardiovascular status is necessary for doctors to make the best decisions with regard to the treatment of cardiovascular disease; however, it is often not available because of the limitation of clinical measurements. The objective of this study was to examine whether cardiovascular function can be assessed quantitatively and for specific patients by combining clinical data with a computational model of the cardiovascular system.MethodsSeven consecutive patients undergoing off-pump coronary artery bypass grafting were enrolled in this study. The clinical data were collected both during the preoperative diagnosis and during the operation. Sensitivity analysis was performed to select the major model parameters most relevant to the measured data. The major model parameters were then estimated through a data-fitting procedure, enabling a patient-specific quantitative assessment of various aspects of cardiovascular function.ResultsThe results revealed the prevalence of left ventricular diastolic dysfunction in the patients, although the severity of dysfunction exhibits significant interpatient variability (the estimated left ventricular passive elastance varies from 194% to 540% of its reference value). Moreover, 4 of the 7 patients studied had impaired left ventricular systolic function.ConclusionsThe current study demonstrates the feasibility of assessing cardiovascular function quantitatively by combining clinical data with a cardiovascular model. In particular, the assessment utilizes the measurements already in use or available in clinical settings, enhancing the clinical potential of the proposed method.