NYMC Faculty Publications

Yield of Diagnostic Imaging in Atraumatic Convexity Subarachnoid Hemorrhage

DOI

10.1136/neurintsurg-2019-014781

Journal Title

Journal of Neurointerventional Surgery

First Page

1222

Last Page

1226

Document Type

Article

Publication Date

December 2019

Department

Neurosurgery

Abstract

INTRODUCTION: Atraumatic convexity subarachnoid hemorrhage is a subtype of spontaneous subarachnoid hemorrhage that often presents a diagnostic challenge. Common etiologies include cerebral amyloid angiopathy, vasculopathies, and coagulopathy; however, aneurysm is rare. Given the broad differential of causes of convexity subarachnoid hemorrhage, we assessed the diagnostic yield of common tests and propose a testing strategy. METHODS: We performed a single-center retrospective study on consecutive patients with atraumatic convexity subarachnoid hemorrhage over a 2-year period. We obtained and reviewed each patient's imaging and characterized the frequency with which each test ultimately diagnosed the cause. Additionally, we discuss clinical features of patients with convexity subarachnoid hemorrhage with respect to the mechanism of hemorrhage. RESULTS: We identified 70 patients over the study period (mean (SD) age 64.70 (16.9) years, 35.7% men), of whom 58 patients (82%) had a brain MRI, 57 (81%) had non-invasive vessel imaging, and 27 (38.5%) underwent catheter-based angiography. Diagnoses were made using only non-invasive imaging modalities in 40 patients (57%), while catheter-based angiography confirmed the diagnosis in nine patients (13%). Further clinical history and laboratory testing yielded a diagnosis in an additional 17 patients (24%), while the cause remained unknown in four patients (6%). CONCLUSION: The etiology of convexity subarachnoid hemorrhage may be diagnosed in most cases via non-invasive imaging and a thorough clinical history. However, catheter angiography should be strongly considered when non-invasive imaging fails to reveal the diagnosis or to better characterize a vascular malformation. Larger prospective studies are needed to validate this algorithm.

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