Delayed Venous Drainage in Ruptured Arteriovenous Malformations Based on Quantitative Color-coded Digital Subtraction Angiography.

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Delayed Venous Drainage in Ruptured Arteriovenous Malformations Based on Quantitative Color-coded Digital Subtraction Angiography.

World Neurosurg. 2017 Apr 27;:

Authors: Burkhardt JK, Chen X, Winkler EA, Cooke DL, Kim H, Lawton MT

Abstract
OBJECTIVE: Clinically applicable hemodynamic risk factors to predict arteriovenous malformation (AVM) rupture are missing in the literature. The aim of this study was to identify hemodynamic parameters in ruptured and unruptured AVMs based on catheter angiography.
METHODS: Patients with frontal and temporal AVMs and Siemens syngo iFlow color-coding angiography were included in this study (n=71). Clinical and radiological data (age, gender, Spetzler-Martin (SM) and supplemented SM grade, and rupture status), and hemodynamic features (contrast mean transit time (MTT) of feeding arteries, draining veins, AVM nidus, and the cerebral circulation time (CCT)) were analyzed. Univariable analysis was performed to compare findings between ruptured and unruptured AVMs.
RESULTS: In total, 35 ruptured and 36 unruptured AVMs were analyzed. Clinical characteristics and AVM grades were comparable between ruptured and unruptured AVMs. Ipsilateral CCT (5.2 vs 4.0sec, p=0.035), MTT between Tmax of ipsilateral internal carotid artery (ICA) and Tmax of sinus entry of the draining vein (3.2 vs 2.0 sec, p<0.001), and MTT between Tmax at the start of venous outflow and Tmax of sinus entry (1.6 vs 0.7 sec, p<0.0001) were significantly longer in ruptured compared to unruptured AVMs. MTTs of nidus, between ICA and feeding artery, of venous sinus passage as well as contralateral CCT and controlled ipsilateral CCT were comparable between both groups.
CONCLUSIONS: Ruptured AVMs have significantly prolonged venous drainage times compared to unruptured AVMs, which suggest restricted AVM drainage and increased blood pressure within the AVM nidus. Prospective studies are needed to determine the predictive power of these measurements.

PMID: 28457930 [PubMed - as supplied by publisher]

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