arXiv subjects

Mette S. Olufsen

Publications and source records attributed to Mette S. Olufsen.

At least 19 recordsLinked to original sources

arXiv2025

A One Dimensional (1D) Computational Fluid Dynamics Study of Fontan-Associated Liver Disease (FALD)

Fontan-Associated Liver Disease (FALD) is a disorder arising from hemodynamic changes and venous congestion in the liver. This disease is prominent in patients with hypoplastic left heart syndrome (HLHS). Although HLHS patients typically survive into adulthood, they have reduced cardiac output due to their univentricular physiology (i.e., a Fontan circuit). As a result, they have insufficient blood delivery to the liver. In comparison, patients with double outlet right ventricle (DORV), also having a univentricular circuit, have lower incidence of FALD. In this study, we use a patient-specific, one-dimensional computational fluid dynamics (1D-CFD) model to predict hemodynamics in the liver of an HLHS patient and compare predictions with an age- and size-matched DORV control patient. Additionally, we simulate FALD conditions in the HLHS patient to predict hemodynamic changes across various stages of disease progression. Our results show that the HLHS patient has a higher portal venous pressure compared to the DORV patient. This difference is exacerbated as FALD conditions progress. The wall shear stress (WSS) is also higher than normal for the HLHS patient, suggesting vascular remodeling. WSS decreases slightly under FALD conditions, consistent with the development of portal hypertension. Perfusion analysis gives insight into regions of liver tissue at risk for fibrosis development, showing increasing pressures and reduced flow throughout the liver tissue fed by the portal vein under FALD conditions. Our results provide insight into the specific hemodynamic changes in Fontan circulation that can cause FALD.

physics.med-ph↗

arXiv2024

Parameter selection and optimization of a computational network model of blood flow in single-ventricle patients

Hypoplastic left heart syndrome (HLHS) is a congenital heart disease responsible for 23% of infant cardiac deaths each year. HLHS patients are born with an underdeveloped left heart, requiring several surgeries to reconstruct the aorta and create a single ventricle circuit known as the Fontan circulation. While survival into early adulthood is becoming more common, Fontan patients suffer from reduced cardiac output, putting them at risk for a multitude of complications. These patients are monitored using chest and neck MRI imaging, but these scans do not capture energy loss, pressure, wave intensity, or hemodynamics beyond the imaged region. This study develops a framework for predicting these missing features by combining imaging data and computational fluid dynamics (CFD) models. Predicted features from models of HLHS patients are compared to those from control patients with a double outlet right ventricle (DORV). We use parameter inference to render the model patient-specific. In the calibrated model, we predict pressure, flow, wave-intensity (WI), and wall shear stress (WSS). Results reveal that HLHS patients have higher vascular stiffness and lower compliance than DORV patients, resulting in lower WSS and higher WI in the ascending aorta and increased WSS and decreased WI in the descending aorta.

q-bio.QM↗

arXiv2023

Characterization of differences in immune responses during bolus and continuous infusion endotoxin challenges using mathematical modeling

Endotoxin administration is commonly used to study the inflammatory response, and though traditionally given as a bolus injection, it can be administered as a continuous infusion over multiple hours. Several studies hypothesize that the latter better represents the prolonged and pronounced inflammation observed in conditions like sepsis. Yet, very few experimental studies have administered endotoxin using both strategies, leaving significant gaps in determining the underlying mechanisms responsible for their differing immune responses. We use mathematical modeling to analyze cytokine data from two studies administering a 2 ng/kg dose of endotoxin, one as a bolus and the other as a continuous infusion over four hours. Using our model, we simulate the dynamics of mean and subject-specific cytokine responses as well as the response to long-term endotoxin administration. Cytokine measurements reveal that the bolus injection leads to significantly higher peaks for IL-8, while IL-10 reaches significantly higher peaks during continuous administration. Moreover, the peak timing of all measured cytokines occurs later in the continuous infusion. We identify three model parameters that significantly differ between the two administration methods. Monocyte activation of IL-10 is greater during the continuous infusion, while recovery rates of IL-8 is faster for the bolus injection. This suggests that a continuous infusion elicits a stronger, longer-lasting systemic reaction through increased stimulation of monocyte anti-inflammatory mediator production and decreased recovery of pro-inflammatory catalysts. Furthermore, our continuous infusion model exhibits prolonged inflammation with recurrent peaks resolving within two days during long-term (20-32 hours) endotoxin administration.

q-bio.TO↗

arXiv2023

A unified model for the human response to lipopolysaccharide-induced inflammation

This study develops a unified model predicting the whole-body response to endotoxin. We simulate dynamics using differential equations examining the response to a lipopolysaccharide (LPS) injection. The model tracks pro- and anti-inflammatory cytokines (TNF-$α$, IL-6, IL-10), concentrations of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and cortisol in the hypothalamic-pituitary-adrenal (HPA) axis. Daily hormonal variations are integrated into the model by including circadian oscillations when tracking CRH. Additionally, the model tracks heart rate, blood pressure, body temperature, and pain perception. Studied quantities function on timescales ranging from minutes to days. To understand how endotoxin impacts the body over this vast span of timescales, we examine the response to variations in LPS administration methods (single dose, repeated dose, and continuous dose) as well as the timing of the administration and the amount of endotoxin released into the system. We calibrate the model to literature data for a 2 ng/kg LPS bolus injection. Results show that LPS administration during early morning or late evening generates a more pronounced hormonal response. Most of the LPS effects are eliminated from the body 24 hours after administration, the main impact of inflammation remains in the system for 48 hours, and repeated dose simulations show that residual effects remain more than 10 days after the initial injection. We also show that if the LPS administration method or total dosage is increased, the system response is amplified, posing a greater risk of hypotension and pyrexia.

q-bio.TO↗

arXiv2022

Parameter inference in a computational model of hemodynamics in pulmonary hypertension

Pulmonary hypertension (PH), defined by a mean pulmonary arterial pressure (mPAP) $>$ 20 mmHg, is characterized by increased pulmonary vascular resistance and decreased pulmonary arterial compliance. There are few measurable biomarkers of PH progression, but a conclusive diagnosis of the disease requires invasive right heart catheterization (RHC). Patient-specific computational models of the cardiovascular system are a potential noninvasive tool for determining additional indicators of disease severity. Using computational modeling, this study quantifies physiological parameters indicative of disease severity in nine PH patients. The model includes all four heart chambers and the pulmonary and systemic circulations. We consider two sets of calibration data: static (systolic \& diastolic values) RHC data and a combination of static and continuous, time-series waveform data. We determine a subset of identifiable parameters for model calibration using sensitivity analyses and multistart inference, and carry out uncertainty quantification post-inference. Results show that additional waveform data enables accurate calibration of the right atrial reservoir and pump function across the PH cohort. Model outcomes, including stroke work and pulmonary resistance-compliance relations, reflect typical right heart dynamics in PH phenotypes. Lastly, we show that estimated parameters agree with previous, non-modeling studies, supporting this type of analysis in translational PH research.

q-bio.QM↗

arXiv2022

A computational study of aortic reconstruction in single ventricle patients

Patients with hypoplastic left heart syndrome (HLHS) are born with an underdeveloped left heart. They typically receive a sequence of surgeries that result in a single ventricle physiology called the Fontan circulation. While these patients usually survive into early adulthood, they are at risk for medical complications, partially due to their lower than normal cardiac output, which leads to insufficient cerebral and gut perfusion. While clinical imaging data can provide detailed insight into cardiovascular function within the imaged region, it is difficult to use this data for assessing deficiencies in the rest of the body and for deriving blood pressure dynamics. Data from patients used in this paper include three dimensional, magnetic resonance angiograms (MRA), time-resolved phase contrast cardiac magnetic resonance images (4D-MRI) and sphygmomanometer blood pressure measurements. The 4D-MRI images provide detailed insight into velocity and flow in vessels within the imaged region, but they cannot predict flow in the rest of the body, nor do they provide values of blood pressure. To remedy these limitations, this study combines the MRA, 4D-MRI, and pressure data with 1D fluid dynamics models to predict hemodynamics in the major systemic arteries, including the cerebral and gut vasculature. To study effects post-surgery we compare simulations for an HLHS patient with simulations for a matched control patient that has double outlet right ventricle (DORV) physiology with a native aorta. Our results show that the HLHS patient has hypertensive pressures in the brain as well as reduced flow to the gut. Wave-intensity analysis suggests that the HLHS patient has irregular circulatory function during light upright exercise conditions and that predicted wall-shear stresses are lower than normal, suggesting the HLHS patient may have hypertension.

math.DS↗

arXiv2022

Application and reduction of a nonlinear hyperelastic wall model capturing ex vivo relationships between fluid pressure, area and wall thickness in normal and hypertensive murine left pulmonary arteries

Pulmonary hypertension is a cardiovascular disorder manifested by elevated arterial blood pressure together with vessel wall stiffening and thickening due to alterations in collagen, elastin and smooth muscle cells. Hypoxia-induced (type 3) pulmonary hypertension can be studied in animals exposed to a low oxygen environment for prolonged time periods leading to biomechanical alterations in vessel wall structure. This study formulates and systematically reduces a nonlinear elastic structural wall model for a large pulmonary artery, generating a novel pressure-area relation capturing remodeling in type 3 pulmonary hypertension. The model is calibrated using {\em ex vivo} measurements of vessel diameter and wall thickness changes, under controlled flow conditions, in left pulmonary arteries isolated from control and hypertensive mice. A two-layer, hyperelastic, anisotropic model incorporating residual stresses is formulated using the Holzapfel-Gasser-Ogden model. Complex relations predicting vessel area and wall thickness with increasing blood pressure are derived and calibrated using the data. Sensitivity analysis, parameter estimation and subset selection are used to systematically reduce the 16-parameter model to one in which a much smaller subset of identifiable parameters is estimated via solution of an inverse problem. Our final reduced model includes a single set of three elastic moduli. Estimated ranges of these parameters demonstrate that nonlinear stiffening is dominated by elastin in the control animals and by collagen in the hypertensive group. The novel pressure-area relation developed in this study has potential impact on one-dimensional fluids network models of vessel wall remodeling in the presence of cardiovascular disease.

q-bio.TO↗

arXiv2021

Postural Orthostatic Tachycardia Syndrome explained using abaroreflex response model

Recent studies have shown that Postural Orthostatic Tachycardia Syndrome (POTS) patients have abnormal low frequency $\approx 0.1$ Hz blood pressure and heart rate dynamics. These dynamics are attributed to the baroreflex and can give insight into the mechanistic causes which are the basis for proposed subgroups of POTS. In this study we develop a baroreflex model replicating the low-frequency dynamics observed in POTS patient data as well as represent subgroups of POTS in our model. We utilize signal processing to quantify the effects that model parameters have on low-frequency oscillations. Results show that key physiological parameters that represent the hypothesized causes are central to our model's ability to reproduce observed dynamics from patient data.

q-bio.TO↗

arXiv2021

A multiscale model of vascular function in chronic thromboembolic pulmonary hypertension

Chronic thromboembolic pulmonary hypertension (CTEPH) is caused by recurrent or unresolved pulmonary thromboemboli, leading to perfusion defects and increased arterial wave reflections. CTEPH treatment aims to reduce pulmonary arterial pressure and reestablish adequate lung perfusion, yet patients with distal lesions are inoperable by standard surgical intervention. Instead, these patients undergo balloon pulmonary angioplasty (BPA), a multi-session, minimally invasive surgery that disrupts the thromboembolic material within the vessel lumen using a catheter balloon. However, there still lacks an integrative, holistic tool for identifying optimal target lesions for treatment. To address this insufficiency, we simulate CTEPH hemodynamics and BPA therapy using a multiscale fluid dynamics model. The large pulmonary arterial geometry is derived from a computed tomography (CT) image, whereas a fractal tree represents the small vessels. We model ring- and web-like lesions, common in CTEPH, and simulate normotensive conditions and four CTEPH disease scenarios; the latter includes both large artery lesions and vascular remodeling. BPA therapy is simulated by simultaneously reducing lesion severity in three locations. Our predictions mimic severe CTEPH, manifested by an increase in mean proximal pulmonary arterial pressure above 20 mmHg and prominent wave reflections. Both flow and pressure decrease in vessels distal to the lesions and increase in unobstructed vascular regions. We use the main pulmonary artery (MPA) pressure, a wave reflection index, and a measure of flow heterogeneity to select optimal target lesions for BPA. In summary, this study provides a multiscale, image-to-hemodynamics pipeline for BPA therapy planning for inoperable CTEPH patients.

q-bio.TO↗

arXiv2021

Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model

In this study, we develop a methodology for model reduction and selection informed by global sensitivity analysis (GSA) methods. We apply these techniques to a control model that takes systolic blood pressure and thoracic tissue pressure data as inputs and predicts heart rate in response to the Valsalva maneuver (VM). The study compares four GSA methods based on Sobol' indices (SIs) quantifying the parameter influence on the difference between the model output and the heart rate data. The GSA methods include standard scalar SIs determining the average parameter influence over the time interval studied and three time-varying methods analyzing how parameter influence changes over time. The time-varying methods include a new technique, termed limited-memory SIs, predicting parameter influence using a moving window approach. Using the limited-memory SIs, we perform model reduction and selection to analyze the necessity of modeling both the aortic and carotid baroreceptor regions in response to the VM. We compare the original model to three systematically reduced models including (i) the aortic and carotid regions, (ii) the aortic region only, and (iii) the carotid region only. Model selection is done quantitatively using the Akaike and Bayesian Information Criteria and qualitatively by comparing the neurological predictions. Results show that it is necessary to incorporate both the aortic and carotid regions to model the VM.

q-bio.QM↗

arXiv2021

Numerical predictions of shear stress and cyclic stretch in the healthy pulmonary vasculature

Isolated post-capillary pulmonary hypertension (Ipc-PH) occurs due to left heart failure, which contributes to 1 out of every 9 deaths in the United States. In some patients, through unknown mechanisms, Ipc-PH transitions to combined pre-/post-capillary PH (Cpc-PH), diagnosed by an increase in pulmonary vascular resistance and associated with a dramatic increase in mortality. We hypothesize that altered mechanical forces and subsequent vasoactive signaling in the pulmonary capillary bed drive the transition from Ipc-PH to Cpc-PH. However, even in a healthy pulmonary circulation, the mechanical forces in the smallest vessels (the arterioles, venules, and capillary bed) have not been quantitatively defined. This study is the first to examine this question via a computational fluid dynamics model of the human pulmonary arteries, veins, arterioles, and venules. Using this model we predict temporal and spatial dynamics of cyclic stretch and wall shear stress. In the large vessels, numerical simulations show that shear stress increases coincides with larger flow and pressure. In the microvasculature, we found that as vessel radius decreases, shear stress increases and flow decreases. In arterioles, this corresponds with lower pressures; however, the venules and smaller veins have higher pressure than larger veins. Our model provides predictions for pressure, flow, shear stress, and cyclic stretch that provides a way to analyze and investigate hypotheses related to disease progression in the pulmonary circulation.

q-bio.TO↗

arXiv2020

Structural and hemodynamic properties in murine pulmonary arterial networks under hypoxia-induced pulmonary hypertension

Detection and monitoring of patients with pulmonary hypertension, defined as mean blood pressure in the main pulmonary artery above 25 mmHg, requires a combination of imaging and hemodynamic measurements. This study demonstrates how to combine imaging data from microcomputed tomography (micro-CT) images with hemodynamic pressure and flow waveforms from control and hypertensive mice. Specific attention is devoted to developing a tool that processes CT images, generating subject-specific arterial networks in which 1D fluid dynamics modeling is used to predict blood pressure and flow. Each arterial network is modeled as a directed graph representing vessels along the principal pathway to ensure perfusion of all lobes. The 1D model couples these networks with structured tree boundary conditions informed by the image data. Fluid dynamics equations are solved in this network and compared to measurements of pressure in the main pulmonary artery. Analysis of micro-CT images reveals that the branching ratio is the same in the control and hypertensive animals, but that the vessel length to radius ratio is significantly lower in the hypertensive animals. Fluid dynamics predictions show that in addition to changed network geometry, vessel stiffness is higher in the hypertensive animal models than in the control models.

q-bio.QM↗

arXiv2020

Characterization of blood pressure and heart rate oscillations of POTS patients via uniform phase empirical mode decomposition

Objective: Postural Orthostatic Tachycardia Syndrome (POTS) is associated with the onset of tachycardia upon postural change. The current diagnosis involves the measurement of heart rate (HR) and blood pressure (BP) during head-up tilt (HUT) or active standing test. A positive diagnosis is made if HR changes with more than 30 bpm (40 bpm in patients aged 12-19 years), ignoring all of the BP and most of the HR signals. This study examines 0.1 Hz oscillations in systolic arterial blood pressure (SBP) and HR signals providing additional metrics characterizing the dynamics of the baroreflex. Methods: We analyze data from 28 control subjects and 28 POTS patients who underwent HUT. We extract beat-to-beat HR and SBP during a 10 min interval including 5 minutes of baseline and 5 minutes of HUT. We employ Uniform Phase Empirical Mode Decomposition (UPEMD) to extract 0.1 Hz stationary modes from both signals and use random forest machine learning and k-means clustering to analyze the outcomes. Results show that the amplitude of the 0.1 Hz oscillations is higher in POTS patients and that the phase response between the two signals is shorter (p < 0.005). Conclusion: POTS is associated with an increase in the amplitude of SBP and HR 0.1 Hz oscillation and a shortening of the phase between the two signals. Significance: The 0.1 Hz phase response and oscillation amplitude metrics provide new markers that can improve POTS diagnostic augmenting the existing diagnosis protocol only analyzing the change in heart rate.

q-bio.QM↗

arXiv2019

Deep phenotyping of cardiac function in heart transplant patients using cardiovascular systems models

Heart transplant patients are followed with periodic right heart catheterizations (RHCs) to identify post-transplant complications and guide treatment. Post-transplant positive outcomes are associated with a steady reduction of right ventricular and pulmonary arterial pressures, toward normal levels of right-side pressure (about 20mmHg) measured by RHC. This study shows more information about patient progression is obtained by combining standard RHC measures with mechanistic computational cardiovascular systems models. This study shows: to understand how cardiovascular system models can be used to represent a patient's cardiovascular state, and to use these models to track post-transplant recovery and outcome. To obtain reliable parameter estimates comparable within and across datasets, we use sensitivity analysis, parameter subset selection, and optimization to determine patient specific mechanistic parameter that can be reliably extracted from the RHC data. Patient-specific models are identified for ten patients from their first post-transplant RHC and longitudinal analysis is done for five patients. Results of sensitivity analysis and subset selection show we can reliably estimate seven non-measurable quantities including ventricular diastolic relaxation, systemic resistance, pulmonary venous elastance, pulmonary resistance, pulmonary arterial elastance, pulmonary valve resistance and systemic arterial elastance. Changes in parameters and predicted cardiovascular function post-transplant are used to evaluate cardiovascular state during recovery in five patients. Of these five patients, only one patient showed inconsistent trends during recovery in ventricular pressure-volume relationships and power output. At a four-year recovery time point this patient exhibited biventricular failure along with graft dysfunction while the remaining four exhibited no cardiovascular complications.

q-bio.TO↗

arXiv2019

Persistent instability in a nonhomogeneous delay differential equation system of the Valsalva maneuver

Delay differential equations (DDEs) are widely used in mathematical modeling to describe physical and biological systems. Delays can impact model dynamics, resulting in oscillatory behavior. In physiological systems, this instability may signify (i) an attempt to return to homeostasis or (ii) system dysfunction. In this study, we analyze a nonlinear, nonautonomous, nonhomogeneous open-loop neurological control model describing the autonomic nervous system response to the Valsalva maneuver. Unstable modes have been identified as a result of parameter interactions between the sympathetic delay and time-scale. In a two-parameter bifurcation analysis, we examine both the homogeneous and nonhomogeneous systems. Discrepancies between solutions result from the presence of the forcing functions which stabilize the system. We use analytical methods to determine stability regions for the homogeneous system, identifying transcendental relationships between the parameters. We also use computational methods to determine stability regions for the nonhomogeneous system. The presence of a Hopf bifurcation within the system is discussed and solution types from the sink and stable focus regions are compared to two control patients and a patient with postural orthostatic tachycardia syndrome (POTS). The model and its analysis support the current clinical hypotheses that patients suffering from POTS experience altered nervous system activity.

math.DS↗

arXiv2019

A physiological model of the inflammatory-thermal-pain-cardiovascular interactions during a pathogen challenge

Uncontrolled, excessive production of pro-inflammatory mediators from immune cells and traumatized tissues can cause systemic inflammatory issues like sepsis, one of the ten leading causes of death in the United States and one of the three leading causes of death in the intensive care unit. Understanding the effects of inflammation on the autonomic control system can improve a patient's chance of recovery after an inflammatory event such as surgery. Though the effects of the autonomic response on the inflammatory system are well defined, there remains a gap in understanding the reverse response. Specifically, the impact of the inflammatory response on the autonomic control system remains unknown. In this study, we investigate hypothesized interactions of the inflammatory system with the thermal and cardiovascular regulatory systems in response to an endotoxin challenge using mathematical modeling. We calibrate the model to data from two independent studies: a) of the inflammatory response in healthy young men and b) a comparative study of the inflammatory response between mice and humans. Simulation analysis is used to explore how the model responds to pathological input and treatment, specifically antibiotics, antipyretics, vasopressors, and combination therapy. Our findings show that multimodal treatment that simultaneously targets both the pathogen and the infection symptoms gives the most favorable recovery outcome.

q-bio.TO↗

arXiv2019

Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries

Computational fluid dynamics (CFD) models are emerging as tools for assisting in diagnostic assessment of cardiovascular disease. Recent advances in image segmentation has made subject-specific modelling of the cardiovascular system a feasible task, which is particularly important in the case of pulmonary hypertension (PH), which requires a combination of invasive and non-invasive procedures for diagnosis. Uncertainty in image segmentation can easily propagate to CFD model predictions, making uncertainty quantification crucial for subject-specific models. This study quantifies the variability of one-dimensional (1D) CFD predictions by propagating the uncertainty of network geometry and connectivity to blood pressure and flow predictions. We analyse multiple segmentations of an image of an excised mouse lung using different pre-segmentation parameters. A custom algorithm extracts vessel length, vessel radii, and network connectivity for each segmented pulmonary network. We quantify uncertainty in geometric features by constructing probability densities for vessel radius and length, and then sample from these distributions and propagate uncertainties of haemodynamic predictions using a 1D CFD model. Results show that variation in network connectivity is a larger contributor to haemodynamic uncertainty than vessel radius and length.

q-bio.QM↗

arXiv2018

Sensitivity analysis and uncertainty quantification of 1D models of the pulmonary circulation

This study combines a one-dimensional (1D) model with micro-CT imaging and hemodynamic data to quantify uncertainty of flow and pressure predictions in the pulmonary arteries in a control and hypoxia induced hypertensive mouse. We use local and global sensitivity and correlation analysis to determine parameters that can be inferred from the model and available data. Least squares optimization is used to estimate mouse specific parameters, and Bayesian as well as asymptotic uncertainty quantification techniques are employed to determine confidence, credible, and prediction intervals for the model parameters and response. These techniques are used to examine the effects of network size and to understand how parameters change with disease (hypertension). Results showed that the peripheral vascular resistance is the most sensitive, and as the network size increases the parameter behavior changes. Correlation analysis revealed that in hypertension large vessel stiffness is correlated with proximal resistance in the boundary. We were able to estimate identifiable parameters using both deterministic and Bayesian techniques (the maxima of the parameter distributions determined using Bayesian analysis aligned with local optima). From these estimates we determined confidence and prediction intervals, which all were within physiological expectation. Analysis of estimated parameter values for the representative mice studied here showed that the hypertensive mouse has stiffer (but larger) vessels and that compliance is decreased both in the proximal and peripheral vasculature.

physics.flu-dyn↗