Computer modelling of arteriovenous fistula maturation 1=Healthcare technologies 2=Medical Imaging (inc medical image and vision computing)
Lead Research Organisation:
University of Warwick
Department Name: Sch of Engineering
Abstract
End stage renal disease (ESRD) patients depend on haemodialysis (HD) as the primary treatment to act as an external kidney to filter their blood and remove metabolic waste. For haemodialysis patients, the recommended primary surgical procedure is to create an arteriovenous fistula (AVF), artificially connecting a vein and an artery together to increase blood flow rates for haemodialysis.
After creating a fistula, various physiological processes occur forcing the blood vessels to react and adapt. The high blood pressure causes an increase in blood flow through the venous side due to the new pressure gradient, and this causes a vascular remodelling response in both vessels. This fistula maturation takes up to several months before haemodialysis can start. However, fistula maturation is prone to various complications and an eventual failure, causing a significant burden on wellbeing and healthcare expenditure. Recent studies report that up to 60% of newly created fistulas fail to mature successfully or require an intervention within one year to maintain their clinical patency.
To understand possible fistula maturation failure mechanisms, computer modelling will be performed based on patient-specific medical images of fistulas. Computational flow dynamics (CFD) will be used to study flow within blood vessels by producing a computer model from medical imaging data. While the exact mechanisms for fistula maturation failure are not clearly understood, adverse flow conditions within blood vessels (such as turbulent or disturbed flow) are considered a contributing factor in damaging a blood vessel wall which leads to narrowing or stenoses and ultimately blockage. A computer generated model allows for the study of flow under many differing conditions such speed of flow (velocity), pressure, vessel wall compliance and blood viscosity. The best models are derived from real life imaging studies of both normal and pathological blood vessels to assess the differing flow conditions. Although both MRI and ultrasound (US) imaging modalities are being used in CFD, ultrasound scans are ideal to minimise potential health risk.
The research project is to develop a medical image-based CFD modelling to predict the efficacy of patient-specific medical treatment. This is to support operation planning as well as post-operation patient care. The modelling will be applied to AVF cases to predict whether or not a fistula will mature and meet the requirements needed for haemodialysis. A longitudinal study for the maturation a patient's AVF is very timely to provide personalised healthcare treatment based on patient-specific US images.
After creating a fistula, various physiological processes occur forcing the blood vessels to react and adapt. The high blood pressure causes an increase in blood flow through the venous side due to the new pressure gradient, and this causes a vascular remodelling response in both vessels. This fistula maturation takes up to several months before haemodialysis can start. However, fistula maturation is prone to various complications and an eventual failure, causing a significant burden on wellbeing and healthcare expenditure. Recent studies report that up to 60% of newly created fistulas fail to mature successfully or require an intervention within one year to maintain their clinical patency.
To understand possible fistula maturation failure mechanisms, computer modelling will be performed based on patient-specific medical images of fistulas. Computational flow dynamics (CFD) will be used to study flow within blood vessels by producing a computer model from medical imaging data. While the exact mechanisms for fistula maturation failure are not clearly understood, adverse flow conditions within blood vessels (such as turbulent or disturbed flow) are considered a contributing factor in damaging a blood vessel wall which leads to narrowing or stenoses and ultimately blockage. A computer generated model allows for the study of flow under many differing conditions such speed of flow (velocity), pressure, vessel wall compliance and blood viscosity. The best models are derived from real life imaging studies of both normal and pathological blood vessels to assess the differing flow conditions. Although both MRI and ultrasound (US) imaging modalities are being used in CFD, ultrasound scans are ideal to minimise potential health risk.
The research project is to develop a medical image-based CFD modelling to predict the efficacy of patient-specific medical treatment. This is to support operation planning as well as post-operation patient care. The modelling will be applied to AVF cases to predict whether or not a fistula will mature and meet the requirements needed for haemodialysis. A longitudinal study for the maturation a patient's AVF is very timely to provide personalised healthcare treatment based on patient-specific US images.
Organisations
People |
ORCID iD |
| Yongmann Chung (Primary Supervisor) | |
| Adam NAWAZ (Student) |