Prediction of aneurysm location based on pattern of bleed on CT scan

  • Binod Rajbhandari Dr
  • Mohan Raj Sharma

Abstract

Background: 
Computed tomography (CT) is the ‘‘gold standard’’ for detecting subarachnoid hemorrhage (SAH) and digital subtraction angiography (DSA) for visualizing the vascular pathology. There is some correlation between the pattern of bleed on CT scan and eventual location of cerebral aneurysm(s). Our aim was to assess the correlation between quantity and distribution of hemorrhage on the initial CT scan and the location of the ruptured aneurysm at our institution.
Materials and Methods:
This retrospective review of prospectively collected data consisted of 50 patients with SAH over 8 months period. CT scan of patients performed within 72 hours after the ictus with suspected SAH were included in the study. Four neurosurgeons, blind to the CT angiogram and DSA results, analyzed and scored independently the quantity and distribution of the hemorrhage on CT and predicted the site of the ruptured aneurysm.
Results: 
Overall accuracy of prediction was 71.6 % (68.2-75%). Parenchymal cerebral hematoma was an excellent predictor for the site of a ruptured aneurysm but was present in only a few cases (16%). The next valid predictor was blood distribution on CT for ruptured anterior communicating artery(ACom) and middle cerebral artery (MCA) aneurysms (89.4% and 85% respectively). CT together with CT angiogram was found to be reliable for identifying the ruptured aneurysm in patients with multiple aneurysms inall cases.
Conclusion:
The quantity and pattern of the blood on CT is a fairly reliable and quick tool for locating a ruptured MCA or ACom aneurysms. It is not, however, reliable for locating other ruptured aneurysms. 
Keywords: aneurysm, CT, DSA, location, prediction
Published
2018-11-03
How to Cite
RAJBHANDARI, Binod; SHARMA, Mohan Raj. Prediction of aneurysm location based on pattern of bleed on CT scan. Journal of Society of Surgeons of Nepal (JSSN), [S.l.], v. 21, n. 1, p. 21 - 28, nov. 2018. ISSN 2392-4772. Available at: <http://jssn.org.np/index.php?journal=jssn&page=article&op=view&path%5B%5D=325>. Date accessed: 17 dec. 2018.
Section
Original Article