Pressure, Flow Rate and Operating Speed Characteristics of a Continuous Flow Left Ventricular Assist Device during Varying Speed Support

Hydraulic performance of Continuous Flow Left Ventricular Assist Devices depends on their pressure head and flow rate relations. Hydraulic characteristics of these devices are expressed by pressure head and flow rate loops in a pulsatile environment as they are implanted between left ventricular apex and aorta. Nonetheless, constant speed Continuous Flow Left Ventricular Assist Device support causes complications due to altered blood flow in the patients’ body. Therefore, beat-to-beat varying speed Continuous Flow Left Ventricular Assist Device support algorithms have been proposed to operate these devices in synchronized co-pulsating or counter-pulsating modes which can generate more physiological blood flow in the circulatory system. However, the effect of speed variation on the pressure head and flow rate loops remains unclear during varying speed Continuous Flow Left Ventricular Assist Device support. In this study, pressure head, flow rate and operating speed relations during co-pulsating and counter-pulsating pump support in a Continuous Flow Left Ventricular Assist Device were analyzed utilizing numerical simulations. Simulation results show that pressure head - flow rate - operating speed loops can express Continuous Flow Left Ventricular Assist Device characteristics better during varying speed heart pump support. Moreover, pump flow rate - operating speed and pressure head - operating speed diagrams show the dynamic behavior of a heart pump, including also the speed. Therefore, understanding the relationship between speed, flow rate and the pressure difference across a pump may help to develop novel beat-to-beat operating modes to improve Continuous Flow Left Ventricular Assist Device support

Mathematical modeling of cardiac function to evaluate clinical cases in adults and children

Time-varying elastance models can simulate only the pressure and volume signals in the heart chambers while the diagnosis of clinical cases and evaluation of different treatment techniques require more information. In this study, an extended model utilizing the geometric dimensions of the heart chambers was developed to describe the cardiac function. The new cardiac model was evaluated by simulating a healthy and dilated cardiomyopathy (DCM) condition for adults and children. The left ventricular ejection fraction, end-diastolic volume, end-diastolic diameter and diastolic sphericity index were 53.60%, 125 mL, 5.08 cm and 1.82 in the healthy adult cardiovascular system model and 23.70%, 173 mL, 6.60 cm and 1.40 in the DCM adult cardiovascular system model. In the healthy child cardiovascular system model, the left ventricular ejection fraction, end-diastolic volume, end-diastolic diameter and diastolic sphericity index were 59.70%, 92 mL, 4.10 cm and 2.26 respectively and 30.70%, 125 mL, 4.94 cm and 1.87 in the DCM child cardiovascular system model. The developed cardiovascular system model simulates the hemodynamic variables and clinical diagnostic indicators within the physiological range for healthy and DCM conditions proving the feasibility of this new model to evaluate clinical cases in adults and children

Computational simulation of cardiac function and blood flow in the circulatory system under continuous flow left ventricular assist device support during atrial fibrillation

Prevalence of atrial fibrillation (AF) is high in heart failure patients supported by a continuous flow left ventricular assist device (CF-LVAD); however, the long term effects remain unclear. In this study, a computational model simulating effects of AF on cardiac function and blood flow for heart failure and CF-LVAD support is presented. The computational model describes left and right heart, systemic and pulmonary circulations and cerebral circulation, and utilises patient-derived RR interval series for normal sinus rhythm (SR). Moreover, AF was simulated using patient-derived unimodal and bimodal distributed RR interval series and patient specific left ventricular systolic functions. The cardiovascular system model simulated clinically-observed haemodynamic outcomes under CF-LVAD support during AF, such as reduced right ventricular ejection fraction and elevated systolic pulmonary arterial pressure. Moreover, relatively high aortic peak pressures and middle arterial peak flow rates during AF with bimodal RR interval distribution, reduced to similar levels as during normal SR and AF with unimodal RR interval distribution under CF-LVAD support. The simulation results suggest that factors such as distribution of RR intervals and systolic left ventricular function may influence haemodynamic outcome of CF-LVAD support during AF

Automated surgical planning in spring-assisted sagittal craniosynostosis correction using finite element analysis and machine learning

Sagittal synostosis is a condition caused by the fused sagittal suture and results in a narrowed skull in infants. Spring-assisted cranioplasty is a correction technique used to expand skulls with sagittal craniosynostosis by placing compressed springs on the skull before six months of age. Proposed methods for surgical planning in spring-assisted sagittal craniosynostosis correction provide information only about the skull anatomy or require iterative finite element simulations. Therefore, the selection of surgical parameters such as spring dimensions and osteotomy sizes may remain unclear and spring-assisted cranioplasty may yield sub-optimal surgical results. The aim of this study is to develop the architectural structure of an automated tool to predict post-operative surgical outcomes in sagittal craniosynostosis correction with spring-assisted cranioplasty using machine learning and finite element analyses. Six different machine learning algorithms were tested using a finite element model which simulated a combination of various mechanical and geometric properties of the calvarium, osteotomy sizes, spring characteristics, and spring implantation positions. Also, a statistical shape model representing an average sagittal craniosynostosis calvarium in 5-month-old patients was used to assess the machine learning algorithms. XGBoost algorithm predicted post-operative cephalic index in spring-assisted sagittal craniosynostosis correction with high accuracy. Finite element simulations confirmed the prediction of the XGBoost algorithm. The presented architectural structure can be used to develop a tool to predict the post-operative cephalic index in spring-assisted cranioplasty in patients with sagittal craniosynostosis can be used to automate surgical planning and improve post-operative surgical outcomes in spring-assisted cranioplasty

Patient-Specific Modelling and Parameter Optimisation to Simulate Dilated Cardiomyopathy in Children

PURPOSE: Lumped parameter modelling has been widely used to simulate cardiac function and physiological scenarios in cardiovascular research. Whereas several patient-specific lumped parameter models have been reported for adults, there is a limited number of studies aiming to simulate cardiac function in children. The aim of this study is to simulate patient-specific cardiovascular dynamics in children diagnosed with dilated cardiomyopathy, using a lumped parameter model. METHODS: Patient data including age, gender, heart rate, left and right ventricular end-systolic and end-diastolic volumes, cardiac output, systolic and diastolic aortic pressures were collected from 3 patients at Great Ormond Street Hospital for Children, London, UK. Ventricular geometrical data were additionally retrieved from cardiovascular magnetic resonance images. 23 parameters in the lumped parameter model were optimised to simulate systolic and diastolic pressures, end-systolic and end-diastolic volumes, cardiac output and left and right ventricular diameters in the patients using a direct search optimisation method. RESULTS: Difference between the haemodynamic parameters in the optimised cardiovascular system models and clinical data was less than 10%. CONCLUSION: The simulation results show the potential of patient-specific lumped parameter modelling to simulate clinical cases. Modelling patient specific cardiac function and blood flow in the paediatric patients would allow us to evaluate a variety of physiological scenarios and treatment options