Finite state machine implementation for left ventricle modeling and control

Impact of Concomitant Mitral Regurgitation on the Hemodynamic Indicators of Aortic Stenosis

Background In patients with aortic stenosis (AS), the presence of mitral regurgitation (MR) can lead to underestimation of AS severity and worse clinical outcomes. The objective of this study was to characterize the magnitude of the effects of concomitant MR on hemodynamic indicators of AS severity using clinical data and a computational cardiovascular simulation. Methods and Results Echocardiographic data from 1427 patients with severe AS were used to inform a computational cardiovascular system model, and varying degrees of MR and AS were simulated. Hemodynamic data, including left ventricular and aortic pressure waveforms, were generated for all simulations. Simulated reduction in mean transaortic pressure gradient (MPG) associated with MR was then used to calculate the adjusted MPG in the clinical cohort. MR was present in 861 (60%) patients. Compared with patients without MR, patients with MR had a lower aortic-valve area (0.83±0.2 cm2 versus 0.75±0.2; P<0.001) and were more likely to have a low-gradient pattern (MPG <40 mm Hg) (45% versus 54%; P<0.001). Simulations showed that the presence of concomitant mild, moderate, and severe MR with AS was accompanied by a mean reduction in MPG of 10%, 29%, and 40%, respectively. For patients with MR, their calculated adjusted MPG was on average 24% higher than their MPG (52±22 versus 42±16 mm Hg). Of the 467 patients with low-gradient AS and MR, 240 (51%) would reclassify as high gradient based on their adjusted MPG. Conclusions Concomitant MR results in lower MPG and reduced forward flow compared with isolated AS. Careful quantitation of MR should be factored into the assessment of AS severity to mitigate for potential underestimation.

Monitoring of cardiovascular physiology augmented by a patient-specific biomechanical model during general anesthesia. A proof of concept study

During general anesthesia (GA), direct analysis of arterial pressure or aortic flow waveforms may be inconclusive in complex situations. Patient-specific biomechanical models, based on data obtained during GA and capable to perform fast simulations of cardiac cycles, have the potential to augment hemodynamic monitoring. Such models allow to simulate Pressure-Volume (PV) loops and estimate functional indicators of cardiovascular (CV) system, e.g. ventricular-arterial coupling (Vva), cardiac efficiency (CE) or myocardial contractility, evolving throughout GA. In this prospective observational study, we created patient-specific biomechanical models of heart and vasculature of a reduced geometric complexity for n = 45 patients undergoing GA, while using transthoracic echocardiography and aortic pressure and flow signals acquired in the beginning of GA (baseline condition). If intraoperative hypotension (IOH) appeared, diluted norepinephrine (NOR) was administered and the model readjusted according to the measured aortic pressure and flow signals. Such patients were a posteriori assigned into a so-called hypotensive group. The accuracy of simulated mean aortic pressure (MAP) and stroke volume (SV) at baseline were in accordance with the guidelines for the validation of new devices or reference measurement methods in all patients. After NOR administration in the hypotensive group, the percentage of concordance with 10% exclusion zone between measurement and simulation was >95% for both MAP and SV. The modeling results showed a decreased Vva (0.64±0.37 vs 0.88±0.43; p = 0.039) and an increased CE (0.8±0.1 vs 0.73±0.11; p = 0.042) in hypotensive vs normotensive patients. Furthermore, Vva increased by 92±101%, CE decreased by 13±11% (p < 0.001 for both) and contractility increased by 14±11% (p = 0.002) in the hypotensive group post-NOR administration. In this work we demonstrated the application of fast-running patient-specific biophysical models to estimate PV loops and functional indicators of CV system using clinical data available during GA. The work paves the way for model-augmented hemodynamic monitoring at operating theatres or intensive care units to enhance the information on patient-specific physiology.