21,339 research outputs found
Integral-based identification of patient specific parameters for a minimal cardiac model
A minimal cardiac model has been developed which accurately captures
the essential dynamics of the cardiovascular system (CVS). However, identifying patient specific parameters with the limited measurements often available, hinders the clinical application of the model for diagnosis and therapy
selection. This paper presents an integral based parameter identification
method for fast, accurate identification of patient specific parameters using
limited measured data. The integral method turns a previously non-linear
and non-convex optimization problem into a linear and convex identification
problem.
The model includes ventricular interaction and physiological valve dynamics. A healthy human state and two disease states, Valvular Stenosis
and Pulmonary Embolism, are used to test the method. Parameters for the
healthy and disease states are accurately identified using only discretized
flows into and out of the two cardiac chambers, the minimum and maximum volumes of the left and right ventricles, and the pressure waveforms
through the aorta and pulmonary artery. These input values can be readily
obtained non-invasively using echo-cardiography and ultra-sound, or invasively via catheters that are often used in Intensive Care.
The method enables rapid identification of model parameters to match
a particular patient condition in clinical real time (3-5 minutes) to within
a mean value of 4 – 8% in the presence of 5 – 15% uniformly distributed
measurement noise. The specific changes made to simulate each disease state
are correctly identified in each case to within 5% without false identification of any other patient specific parameters. Clinically, the resulting patient
specific model can then be used to assist medical staff in understanding,
diagnosis and treatment selection
Model-Based Prediction of the Patient-Specific Response to Adrenaline
A model for the cardiovascular and circulatory systems has previously been validated in simulated cardiac and circulatory disease states. It has also been shown to accurately capture the main hemodynamic trends in porcine models of pulmonary embolism and PEEP (positive end-expiratory pressure) titrations at different volemic levels. In this research, the existing model and parameter identification process are used to study the effect of different adrenaline doses in healthy and critically ill patient populations, and to develop a means of predicting the hemodynamic response to adrenaline. The hemodynamic effects on arterial blood pressures and stroke volume (cardiac index) are simulated in the model and adrenaline-specific parameters are identified. The dose dependent changes in these parameters are then related to adrenaline dose using data from studies published in the literature. These relationships are then used to predict the future, patient-specific response to a change in dose or over time periods from 1-12 hours. The results are compared to data from 3 published adrenaline dosing studies comprising a total of 37 data sets. Absolute percentage errors for the identified model are within 10% when re-simulated and compared to clinical data for all cases. All identified parameter trends match clinically expected changes. Absolute percentage errors for the predicted hemodynamic responses (N=15) are also within 10% when re-simulated and compared to clinical data. Clinically accurate prediction of the effect of inotropic circulatory support drugs, such as adrenaline, offers significant potential for this type of model-based application. Overall, this work represents a further clinical, proof of concept, of the underlying fundamental mathematical model, methods and approach, as well as providing a template for using the model in clinical titration of adrenaline in a decision support role in critical care. They are thus a further justification in support of upcoming human clinical trials to validate this model
The Impact of Parameter Identification Methods on Drug Therapy Control in an Intensive Care Unit
This paper investigates the impact of fast parameter identification methods, which do not require any forward simulations, on model-based glucose control, using retrospective data in the Christchurch Hospital Intensive Care Unit. The integral-based identification method has been previously clinically validated and extensively applied in a number of biomedical applications; and is a crucial element in the presented model-based therapeutics approach. Common non-linear regression and gradient descent approaches are too computationally intense and not suitable for the glucose control applications presented. The main focus in this paper is on better characterizing and understanding the importance of the integral in the formulation and the effect it has on model-based drug therapy control. As a comparison, a potentially more natural derivative formulation which has the same computation speed advantages is investigated, and is shown to go unstable with respect to modelling error which is always present clinically. The integral method remains robust
Numerical simulation of electrocardiograms for full cardiac cycles in healthy and pathological conditions
This work is dedicated to the simulation of full cycles of the electrical
activity of the heart and the corresponding body surface potential. The model
is based on a realistic torso and heart anatomy, including ventricles and
atria. One of the specificities of our approach is to model the atria as a
surface, which is the kind of data typically provided by medical imaging for
thin volumes. The bidomain equations are considered in their usual formulation
in the ventricles, and in a surface formulation on the atria. Two ionic models
are used: the Courtemanche-Ramirez-Nattel model on the atria, and the "Minimal
model for human Ventricular action potentials" (MV) by Bueno-Orovio, Cherry and
Fenton in the ventricles. The heart is weakly coupled to the torso by a Robin
boundary condition based on a resistor- capacitor transmission condition.
Various ECGs are simulated in healthy and pathological conditions (left and
right bundle branch blocks, Bachmann's bundle block, Wolff-Parkinson-White
syndrome). To assess the numerical ECGs, we use several qualitative and
quantitative criteria found in the medical literature. Our simulator can also
be used to generate the signals measured by a vest of electrodes. This
capability is illustrated at the end of the article
Pathophysiologic correlates of exercise intolerance in adults with pulmonary hypertension and congenital heart disease
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Improvements In computed tomography perfusion output using complex singular value decomposition and the maximum slope algorithm
OBJECTIVE: Determine if complex singular value decomposition (cSVD) used as preprocessing in the maximum slope algorithm reduces image noise of resultant physiologic parametric images. Noise will be decreased in the parametric maps of cerebral blood flow (CBF), cerebral blood volume (CBV) as compared to the same algorithm and data set with no cSVD applied.
MATERIALS AND METHODS: A set of 10 patients (n=15) underwent a total combined 15 CT perfusion studies upon presenting with stroke symptoms. It was determined these patients suffered from occlusions resulting in a prolonged arrival time of blood to the brain. DICOM data files of these patients scans were selected based on this increased arrival delay. We compared the output of estimation calculations for cerebral blood flow (CBF), and cerebral blood volume (CBV), using preprocessing cSVD against the same scan data with no preprocessing cSVD. Image noise was assessed through the calculation of the standard deviation within specific regions of interest copied to specific areas of grey and white matter as well as CSF space. A decrease in the standard deviation values will indicate improvement in the noise level of the resultant images.. Results for the mean value within the regions of interest are expected to be similar between the groups calculated using cSVD and those calculated under the standard method. This will indicate the presence of minimal bias.
RESULTS: Between groups of the standard processing method and the cSVD method standard deviation (SD) reductions were seen in both CBF and CBV values across all three ROIs. In grey matter measures of CBV, SD was reduced an average of 0.0034 mL/100g while measures of CBF saw SD reduced by an average of 0.073 mL/100g/min. In samples of white matter, standard deviations of CBV values were reduced on average by 0.0041mL/100g while CBF SD's were reduced by 0.073 mL/100g/min. CSF ROIs in CBV calculations saw SD reductions averaging 0.0047 mL/100g and reductions of 0.074 mL/100g/min in measures of CBF. Bias within CBV calculations was at most minimal as determined by no significant changes in mean calculated values. Calculations of CBF saw large downward bias in the mean values.
CONCLUSIONS: The application of the cSVD method to preprocessing of CT perfusion imaging studies produces an effective method of noise reduction. In calculations of CBV, cSVD noise reduction results in overall improvement. In calculations of CBF, cSVD, while effective in noise reduction, caused mean values to be statistically lower than the standard method. It should be noted that there is currently no evaluation of which values can be considered more accurate physiologically. Simulations of the effect of noise on CBF showed a positive correlation suggesting that the CBF algorithm itself is sensitive to the level of noise
Integral-based filtering of continuous glucose sensor measurements for glycaemic control in critical care
Hyperglycaemia is prevalent in critical illness and increases the risk of further
complications and mortality, while tight control can reduce mortality up to 43%.
Adaptive control methods are capable of highly accurate, targeted blood glucose
regulation using limited numbers of manual measurements due to patient discomfort
and labour intensity. Therefore, the option to obtain greater data density using
emerging continuous glucose sensing devices is attractive. However, the few such
systems currently available can have errors in excess of 20-30%. In contrast, typical
bedside testing kits have errors of approximately 7-10%. Despite greater measurement
frequency larger errors significantly impact the resulting glucose and patient specific
parameter estimates, and thus the control actions determined creating an important
safety and performance issue. This paper models the impact of the Continuous
Glucose Monitoring System (CGMS, Medtronic, Northridge, CA) on model-based
parameter identification and glucose prediction. An integral-based fitting and filtering
method is developed to reduce the effect of these errors. A noise model is developed
based on CGMS data reported in the literature, and is slightly conservative with a
mean Clarke Error Grid (CEG) correlation of R=0.81 (range: 0.68-0.88) as compared to a reported value of R=0.82 in a critical care study. Using 17 virtual patient profiles
developed from retrospective clinical data, this noise model was used to test the
methods developed. Monte-Carlo simulation for each patient resulted in an average
absolute one-hour glucose prediction error of 6.20% (range: 4.97-8.06%) with an
average standard deviation per patient of 5.22% (range: 3.26-8.55%). Note that all the
methods and results are generalisable to similar applications outside of critical care,
such as less acute wards and eventually ambulatory individuals. Clinically, the results
show one possible computational method for managing the larger errors encountered
in emerging continuous blood glucose sensors, thus enabling their more effective use
in clinical glucose regulation studies
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