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  • Reliable Identification of Benign Clinical Course in Aneurysmal Subarachnoid Hemorrhage: A Simple and Qualitative Clinical Algorithm

    Final Number:
    1103

    Authors:
    Yifei Duan MD; Berje Haroutuon Shammassian MD; Christina Huang Wright MD; Nicholas C. Bambakidis MD

    Study Design:
    Other

    Subject Category:

    Meeting: Congress of Neurological Surgeons 2016 Annual Meeting

    Introduction: While a number of grading systems have been previously described to help guide clinical decision making in the setting of aneurysmal subarachnoid hemorrhage (aSAH), a method to reliably predict low vasospasm risk in aSAH patients has not been proposed. We developed a simple qualitative clinical algorithm that combines admission clinical severity, defined by Hunt Hess grade, and subarachnoid blood distribution, based on the Hijdra sum scoring system, to reliably identify patients at low risk of clinical vasospasm.

    Methods: Clinical severity, admission non-contrasted head computed tomography scans (CTH), and incidence of radiographic and clinical vasospasm among 214 aSAH patients treated at our institution were evaluated. Admission CTH’s were systematically assessed for several different distributions of cisternal and ventricular blood. A final clinical algorithm was developed. Patients who satisfied all of the following 4 criteria experienced considerably lower risk of vasospasm. 1) Hunt Hess grade 1-2. 2) Lack of thick subarachnoid blood filling two adjacent cisterns. 3) Lack of thick interhemispheric blood. 4) Lack of biventricular IVH.

    Results: Eighty-nine patients (41.6%) developed clinically silent vasospasm, seventy-one patients (33.2%) developed vasospasm with neurological deficit, and forty-five patients expired (21%). Adjacent cistern blood (OR 4.13, 95% confidence interval [CI] 2.1-8.09), interhemispheric thick blood (OR 6.39, 95% CI 3.17-12.87), and biventricular IVH (OR 2.05, 95% CI 1.04-4.04) were all statistically significant risk factors. Retrospective application of our proposed clinical algorithm yielded a sensitivity of 40% (95% CI 28.47-52.41%), specificity of 100% (95% CI 83.89-100%), positive predictive value of 100% (95% CI 87.66-100%), and negative predictive value of 33.3% (95% CI 21.95-46.34%). Inter-observer variability was substantial at k0.79.

    Conclusions: Application of the final clinical algorithm produced successful identification of aSAH patients who experience effectively zero risk of clinical vasospasm. Our algorithm is simple to apply with high reliability. Prospective application of our algorithm has considerable clinical and economic implications.

    Patient Care: We developed a clinical algorithm that is simple to apply with high reliability. We anticipate that prospective application of our algorithm will generate considerable clinical and economic benefits, including shorter duration of ICU stays, lower frequencies of ICU related complications, and improved patient satisfaction.

    Learning Objectives: By the conclusion of this session, participants should be able to 1) Understand the derivation of the proposed clinical algorithm and 2) Apply the proposed clinical algorithm at home institutions for identification of subgroups of low-risk vasospasm patients.

    References:

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