Dynamic simulation of aortic valve stenosis using a lumped parameter cardiovascular system model with flow regime dependent valve pressure loss characteristics

Laubscher, Ryno; Merwe, Johan van der; Liebenberg, Jacques; Herbst, Philip

doi:10.1016/j.medengphy.2022.103838

Cited by 14 publications

(20 citation statements)

References 25 publications

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“…Each loop is modelled using 5 components that consist of inductive, capacitive, and resistive components as seen in Figure 2. For the sake of brevity only the systemic loop equations will be provided; for more detail on the model equations, please see [24]. The flow rate through the aortic sinus and associated sinus inlet pressure are calculated using the following ODEs.…”

Section: Parameters Left Heart Right Heart Atriumsmentioning

confidence: 99%

“…Further, ̅ is the parameter vector containing all the selected important parameters, is the vector of model predictions for measurement and is the vector of synthetic measurements for measurement . The loss function minimized by the computer model is then simply the summation of the different measurement losses , as seen in Equation (24), where is the number of measurement streams (5 and 9 for D1 and D2 respectively, as mentioned in Section 2.2).…”

Section: Parameter Optimizationmentioning

confidence: 99%

“…The scaling ̅ and momentum matrices are initialized to 0 at the start of the Adam training algorithm, is the iteration counter, ϵ = 1 ⋅ 10 is the smoothing term, β is the momentum decay hyperparameter and is set to 0.9 and β is the scaling hyperparameter and is set to 0.999. In Equation (24), ∇ (θ ) are the gradients of the cost function with respect to the optimization parameters. For the optimization runs, the learning rate parameter η is fixed to a value of 0.005.…”

Section: Parameter Optimizationmentioning

confidence: 99%

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Inference of Pulmonary Arterial Pressure using Non-invasive Measurements and Scientific Machine Learning Techniques

Laubscher¹,

Merwe²,

Herbst³

et al. 2022

Preprint

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Reliable quantification of pulmonary arterial pressure is essential in the diagnostic and prognostic assessment of a range of cardiovascular pathologies including rheumatic heart disease, yet an accurate and routinely available method for its quantification remains elusive. This work proposes an approach to infer pulmonary arterial pressure based on scientific machine learning techniques and non-invasive, clinically available measurements. A 0-D multicompartment model of the cardiovascular system was optimized using several optimization algorithms, subject to forward-mode automatic differentiation. Measurement data were synthesized from known parameters to represent the healthy, mitral regurgitant, aortic stenosed and combined valvular disease situations with and without pulmonary hypertension. Eleven model parameters were selected for optimization based on 95 % explained variation in mean pulmonary arterial pressure. A hybrid Adam and limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer yielded the best results with input data including valvular flow rates, heart chamber volume changes and systematic arterial pressure. Mean absolute percentage errors ranged from 1.8 % to 3.78 % over the simulated test cases. The model was able to capture pressure dynamics under hypertensive conditions with pulmonary arterial systole, diastole, and mean pressure average percentage errors of 1.12 %, 2.49 % and 2.14 %, respectively. The relatively low errors highlight the potential of the proposed model to recover pulmonary pressures for diseased heart valve and pulmonary hypertensive conditions.

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Section: Parameters Left Heart Right Heart Atriumsmentioning

confidence: 99%

Section: Parameter Optimizationmentioning

confidence: 99%

Section: Parameter Optimizationmentioning

confidence: 99%

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Inference of Pulmonary Arterial Pressure using Non-invasive Measurements and Scientific Machine Learning Techniques

Laubscher¹,

Merwe²,

Herbst³

et al. 2022

Preprint

Self Cite

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“…However, the assessment of the displaced volume by the leaflets is not easily accessible in vivo, and it makes this method challenging to apply. Recently, Laubscher et al [ 26 ] proposed a more advanced dynamic valve model by including the effect of Reynold’s number on the TPG and then compared it with the previous model of Korakianitis et al [ 27 ]. Both models solved the differential equations of valve dynamics, and the opening of a valve was modeled as a function of the angular position of the leaflets [ 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Laubscher et al [ 26 ] proposed a more advanced dynamic valve model by including the effect of Reynold’s number on the TPG and then compared it with the previous model of Korakianitis et al [ 27 ]. Both models solved the differential equations of valve dynamics, and the opening of a valve was modeled as a function of the angular position of the leaflets [ 26 , 27 , 28 ]. These models require various geometrical valve parameters, such as the angular position and thickness of the leaflets, and this is difficult to be obtained with conventional imaging techniques.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Left Ventricular Performance and Afterload on the Evaluation of Aortic Valve Stenosis: A 1D Mathematical Modeling Approach

et al. 2023

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The transaortic valvular pressure gradient (TPG) plays a central role in decision-making for patients suffering from severe aortic stenosis. However, the flow-dependence nature of the TPG makes the diagnosis of aortic stenosis challenging since the markers of cardiac performance and afterload present high physiological interdependence and thus, isolated effects cannot be measured directly in vivo. We used a validated 1D mathematical model of the cardiovascular system, coupled with a model of aortic stenosis, to assess and quantify the independent effect of the main left ventricular performance parameters (end-systolic (Ees) and end-diastolic (Eed) elastance) and principal afterload indices (total vascular resistance (TVR) and total arterial compliance (TAC)) on the TPG for different levels of aortic stenosis. In patients with critical aortic stenosis (aortic valve area (AVA) ≤ 0.6 cm2), a 10% increase of Eed from the baseline value was associated with the most important effect on the TPG (−5.6 ± 0.5 mmHg, p < 0.001), followed by a similar increase of Ees (3.4 ± 0.1 mmHg, p < 0.001), in TAC (1.3 ±0.2 mmHg, p < 0.001) and TVR (−0.7 ± 0.04 mmHg, p < 0.001). The interdependence of the TPG left ventricular performance and afterload indices become stronger with increased aortic stenosis severity. Disregarding their effects may lead to an underestimation of stenosis severity and a potential delay in therapeutic intervention. Therefore, a comprehensive evaluation of left ventricular function and afterload should be performed, especially in cases of diagnostic challenge, since it may offer the pathophysiological mechanism that explains the mismatch between aortic severity and the TPG.

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Forest Supply Chains During Digitalization: Current Implementations and Prospects in Near Future

Palander,

Tokola,

Borz

et al. 2024

Curr. For. Rep.

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Purpose of Review The forest industry has deployed systems of information and communication technology to improve the productivity of forest supply chains. In this sense, the digitalization of data flows significantly impacts wood supply chains, from forest to mills, which must react flexibly to market fluctuations of forest products. The goal of this study was to conduct a literature review on data flow implementations in management systems of wood supply chains and to evaluate their applicability in supply chains to analyze the opportunities for improving them in practice. Recent Findings We utilized the ScienceDirect database, Scopus, and Web of Science in order to document data flows in systems and actual applications. Due to ongoing outsourcing, the wood procurement chains and the wood supply chains were identified in the forest industry. In addition to industrial wood data, several different data collection technologies can be implemented in wood supply chains to digitalize the forest data depending on the specific needs of organizations. In this regard, the digitalization of big data causes significant changes in available data elements of practical operations that are integrated and standardized in the optimization and simulation systems. Summary A modeling guide is suggested for accurate dynamic solution of problems of forest logistics. Then, the opportunities in digitalization for connecting different data flows, technologies, and stakeholders are discussed thoroughly. We conclude that data-driven dynamic optimization and locally implemented digitalization contribute to wood supply modeling in the ongoing establishment of standards for cloud-based platforms because modeling time-related and sequential measures will ensure successful forest logistics through planning and monitoring wood supply chains.

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Dynamic simulation of aortic valve stenosis using a lumped parameter cardiovascular system model with flow regime dependent valve pressure loss characteristics

Cited by 14 publications

References 25 publications

Inference of Pulmonary Arterial Pressure using Non-invasive Measurements and Scientific Machine Learning Techniques

Inference of Pulmonary Arterial Pressure using Non-invasive Measurements and Scientific Machine Learning Techniques

The Impact of Left Ventricular Performance and Afterload on the Evaluation of Aortic Valve Stenosis: A 1D Mathematical Modeling Approach

Forest Supply Chains During Digitalization: Current Implementations and Prospects in Near Future

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