Development and Identification of a Closed-Loop Model of the Cardiovascular System Including the Atria

peer reviewe

Electrocardiogram R-wave is an Unreliable Indicator of Pulse Wave Initialization

Pulse wave velocity (PWV) measurements are commonly used to evaluate a patient’s arterial stiffness, an indicator of cardiovascular dysfunction. PWV is usually calculated by measuring the pulse transit time (PTT) over a known distance through the arteries. In an experimental study on animals, it is straight forward to measure the PTT using two pressure catheters a known distance apart in the central arteries. However, in a clinical setting it is uncommon for such a direct invasive method to be used. This study aims to identify whether a surrogate measure of PTT could be found without the need for an external device and without being additionally invasive. The aim is to use the time between the R-wave of an electrocardiogram (ECG), and the pulse wave passing one pressure catheter (rPTT), both of which are common in critical care. The analysis was performed using data from four porcine experiments (Pietrain Pigs, 20-29kg) in which ECG, aortic arch pressure and abdominal aortic pressure were measured simultaneously over a range of induced hemodynamic conditions including recruitment manoeuvres (RM), fluid admission and dobutamine admission. From the measured data, the correlation of rPTT and PTT was calculated for each pig and condition. The overall results showed varied correlations across the pigs (r2 = 0.07 to 0.79). The variability is suspected to be due to two main causes, the first being pig specific response to the interventions. The second cause leading to poor correlation is suspected to be the pre-ejection period (PEP), the time following the ECG R-wave but before ejection of blood from the ventricle. The analysis showed that rPTT was an unreliable measure of PTT and a poor surrogate

Next-generation, personalised, model-based critical care medicine : a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

© 2018 The Author(s). Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care

Evaluation of a Desktop 3D Printed Rigid Refractive-Indexed-Matched Flow Phantom for PIV Measurements on Cerebral Aneurysms

Purpose Fabrication of a suitable flow model or phantom is critical to the study of biomedical fluid dynamics using optical flow visualization and measurement methods. The main difficulties arise from the optical properties of the model material, accuracy of the geometry and ease of fabrication. Methods Conventionally an investment casting method has been used, but recently advancements in additive manufacturing techniques such as 3D printing have allowed the flow model to be printed directly with minimal post-processing steps. This study presents results of an investigation into the feasibility of fabrication of such models suitable for particle image velocimetry (PIV) using a common 3D printing Stereolithography process and photopolymer resin. Results An idealised geometry of a cerebral aneurysm was printed to demonstrate its applicability for PIV experimentation. The material was shown to have a refractive index of 1.51, which can be refractive matched with a mixture of de-ionised water with ammonium thiocyanate (NH4SCN). The images were of a quality that after applying common PIV pre-processing techniques and a PIV cross-correlation algorithm, the results produced were consistent within the aneurysm when compared to previous studies. Conclusions This study presents an alternative low-cost option for 3D printing of a flow phantom suitable for flow visualization simulations. The use of 3D printed flow phantoms reduces the complexity, time and effort required compared to conventional investment casting methods by removing the necessity of a multi-part process required with investment casting techniques

Minimally invasive, patient specific, beat-by-beat estimation of left ventricular time varying elastance.

peer reviewedBACKGROUND: The aim of this paper was to establish a minimally invasive method for deriving the left ventricular time varying elastance (TVE) curve beat-by-beat, the monitoring of which's inter-beat evolution could add significant new data and insight to improve diagnosis and treatment. The method developed uses the clinically available inputs of aortic pressure, heart rate and baseline end-systolic volume (via echocardiography) to determine the outputs of left ventricular pressure, volume and dead space volume, and thus the TVE curve. This approach avoids directly assuming the shape of the TVE curve, allowing more effective capture of intra- and inter-patient variability. RESULTS: The resulting TVE curve was experimentally validated against the TVE curve as derived from experimentally measured left ventricular pressure and volume in animal models, a data set encompassing 46,318 heartbeats across 5 Pietrain pigs. This simulated TVE curve was able to effectively approximate the measured TVE curve, with an overall median absolute error of 11.4% and overall median signed error of -2.5%. CONCLUSIONS: The use of clinically available inputs means there is potential for real-time implementation of the method at the patient bedside. Thus the method could be used to provide additional, patient specific information on intra- and inter-beat variation in heart function

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Early detection of abnormal left ventricular relaxation in acute myocardial ischemia with a quadratic model

Aims: The time constant of left ventricular (LV) relaxation derived from a monoexponential model is widely 12 used as an index of LV relaxation rate, although this model does not reflect the non-uniformity of ventricular 13 relaxation. This study investigates whether the relaxation curve can be better fitted with a “quadratic” model 14 than with the “conventional” monoexponential model and if changes in the LV relaxation waveform due to 15 acute myocardial ischemia could be better detected with the quadratic model. 16 Methods and results: Isovolumic relaxation was assessed with quadratic and conventional models during acute 17 myocardial ischemia performed in 6 anesthetized pigs. Mathematical development indicates that one parameter 18 (Tq) of the quadratic model reflects the rate of LV relaxation, while the second parameter (K) modifies the 19 shape of the relaxation curve. Analysis of experimental data obtained in anesthetized pigs showed that the shape 20 of LV relaxation consistently deviates from the conventional monoexponential decay. During the early phase of 21 acute myocardial ischemia, the rate and non-uniformity of LV relaxation, assessed with the quadratic function, 22 were significantly enhanced. Tq increased by 16% (p < 0.001) and K increased by 12% (p < 0.001) within 30 23 and 60 minutes, respectively, after left anterior descending (LAD) coronary artery occlusion. However, no 24 significant changes were observed with the conventional monoexponential decay within 60 minutes of ischemia. 25 Conclusions: The quadratic model better fits LV isovolumic relaxation than the monoexponential model and can 26 detect early changes in relaxation due to acute myocardial ischemia that are not detectable with conventional 27 methods

A novel method for computing the derivatives of the mean and amplitude of physiological variables with respect to the parameters of a cardiovascular system model

While studying the cardiovascular system (CVS), it is frequent that only the mean and amplitude of physiological variables (pressures and volumes) are available. Computing the derivative of this discrete data with respect to the parameters of a CVS model is a necessary step to identify these parameters. Currently, such derivatives are computed through forward difference approximations, hence requiring two model simulations per derivative. In this work, we develop a method aiming to compute the derivatives along with the model simulation

Structural identifiability analysis of a cardiovascular system model

A simple experimentally validated cardiovascular system model has been shown to be able to track the evolution of various diseases. The model has previously been made patient-specific by adjustment of its parameters on the basis of a minimal set of hemodynamic measurements. However, this model has not yet been shown to be structurally identifiable, which means that the adjusted model parameters may not be unique. The model equations were manipulated to show that, from a theoretical point of view, all of their parameters can be exactly retrieved from a restricted set of model outputs. However, this set of model outputs is still too large for a clinical application, because it includes left and right ventricular pressures. Consequently, further hypotheses that determine some model parameter values have to be made for the model to be clinically applicable