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Theoretical modelling of arteriovenous malformation rupture risk: a feasibility and validation study
E Gao1, W L Young, G J Hademenos
1Department of Electrical Engineering, Columbia University, New York, NY 10027, USA.
Purpose:
To explore the feasibility of using a theoretical computational model to simulate the risk of spontaneous arteriovenous malformation (AVM) haemorrhage.
Methods:
Data from 12 patients were collected from a prospective databank which documented the angioarchitecture and morphological characteristics of the AVM and the feeding mean arterial pressure (FMAP) measured during initial superselective angiography prior to any treatment. Using the data, a computational model of the cerebral circulation and the AVM was constructed for each patient (patient-specific model). Two model risk (Risk(model)) calculations (haemodynamic- and structural-weighted estimates) were performed by using the patient-specific models. In our previously developed method of haemodynamic-weighted estimate, Risk(model) was calculated with the simulated intranidal pressures related to its maximal and minimal values. In the method of structural-weighted estimate developed and described in this paper, the vessel mechanical properties and probability calculation were considered in more detail than in the haemodynamic-weighted estimate. Risk(model) was then compared to experimentally determined risk which was calculated using a statistical method for determining the relative risk of having initially presented with AVM haemorrhage, termed Risk(exp).
Results:
The Risk(model) calculated by both haemodynamic- and structural-weighted estimates correlated with experimental risks with chi2 = 6.0 and 0.64, respectively. The risks of the structural-weighted estimate were more correlated to experimental risks.
Conclusions:
Using two different approaches to the calculation of AVM haemorrhage risk, we found a general agreement with independent statistical estimates of haemorrhagic risk based on patient data. Computational approaches are feasible; future work can focus on specific pathomechanistic questions. Detailed patient-specific computational models can also be developed as an adjunct to individual patient risk assessment for risk-stratification purposes.