Influence of non-Newtonian blood flow models on drug deposition in the arterial wall

Pulmonary embolism (PE) is the most common cause of acute pulmonary hypertension, yet it is commonly undiagnosed, with risk of death if not recognized promptly and managed accordingly. Patients typically present with hypoxemia and hypocapnia, although the presentation varies greatly, being confounded by co-mordidities such as pre-existing cardio-respiratory disease. Previous studies have demonstrated variable patient outcomes in spite of similar extent and distribution of pulmonary vascular occlusion, but the pathophysiological determinants of outcome remain unclear. Computational models enable exact control over many of the compounding factors leading to functional outcomes and therefore provide a useful tool to understand and assess these mechanisms. We review the current state of pulmonary blood flow models. We present a pilot study within 10 patients presenting with acute PE, where patient-derived vascular occlusions are imposed onto an existing model of the pulmonary circulation enabling predictions of resultant haemodynamics after embolus occlusion. Results show that mechanical obstruction alone is not sufficient to cause pulmonary arterial hypertension, even when up to 65 per cent of lung tissue is occluded. Blood flow is found to preferentially redistribute to the gravitationally non-dependent regions. The presence of an additional downstream occlusion is found to significantly increase pressures.

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Optimization of topological complexity for one-dimensional arterial blood flow models

Journal of The Royal Society Interface ◽

10.1098/rsif.2018.0546 ◽

2018 ◽

Vol 15 (149) ◽

pp. 20180546 ◽

Cited By ~ 9

Author(s):

Fredrik E. Fossan ◽

Jorge Mariscal-Harana ◽

Jordi Alastruey ◽

Leif R. Hellevik

Keyword(s):

Blood Flow ◽

Pulse Pressure ◽

Arterial System ◽

Arterial Blood Flow ◽

Patient Specific ◽

Flow Measurements ◽

One Dimensional ◽

Flow Models ◽

Arterial Blood ◽

Blood Flow Models

As computational models of the cardiovascular system are applied in modern personalized medicine, maximizing certainty of model input becomes crucial. A model with a high number of arterial segments results in a more realistic description of the system, but also requires a high number of parameters with associated uncertainties. In this paper, we present a method to optimize/reduce the number of arterial segments included in one-dimensional blood flow models, while preserving key features of flow and pressure waveforms. We quantify the preservation of key flow features for the optimal network with respect to the baseline networks (a 96-artery and a patient-specific coronary network) by various metrics and quantities like average relative error, pulse pressure and augmentation pressure. Furthermore, various physiological and pathological states are considered. For the aortic root and larger systemic artery pressure waveforms a network with minimal description of lower and upper limb arteries and no cerebral arteries, sufficiently captures important features such as pressure augmentation and pulse pressure. Discrepancies in carotid and middle cerebral artery flow waveforms that are introduced by describing the arterial system in a minimalistic manner are small compared with errors related to uncertainties in blood flow measurements obtained by ultrasound.

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