• World Neurosurg · Nov 2016

    A prognostic model for early post-treatment outcome of elderly patients with aneurysmal subarachnoid hemorrhage.

    • Qi Yue, Yingjun Liu, Bing Leng, Bin Xu, Yuxiang Gu, Liang Chen, Wei Zhu, and Ying Mao.
    • Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
    • World Neurosurg. 2016 Nov 1; 95: 253-261.

    ObjectiveThe optimal treatment for aneurysmal subarachnoid hemorrhage (SAH) in elderly patients is controversial. Focusing on this specific population, we analyzed early outcomes after endovascular or neurosurgical treatment and developed a prognostic model to aid in clinical decision-making processes.MethodsSAH patients aged 60 years or older who underwent active treatment between 2010 and 2013 were retrospectively enrolled in the primary cohort, whereas those treated since 2014 were enrolled in the validation cohort. The Rankin scale and Clavien-Dindo classification for complications were used to measure outcome at 3 months after treatment. The prognostic model for unfavorable outcome was developed based on independent factors derived from multivariate analysis of the primary cohort and examined for prediction accuracy in the validation cohort.ResultsThe primary and validation cohorts were composed of 109 and 64 elderly patients, respectively. There were no significant differences between the 2 cohorts' demographics. An age of 80 years or older, hypertension, frailty, smoking, a high Hunt and Hess grade, multiple aneurysms, and a large aneurysm size served as independent prognostic factors for unfavorable outcome in the primary cohort. The results from the validation cohort demonstrated that these factors were good predictors in the prognostic model. When the risk score was moderate, endovascular intervention presented a more favorable outcome than neurosurgical treatment.ConclusionsBecause it was proven to be valid in quantitatively predicting treatment efficacy in elderly patients with ruptured aneurysm, the prognostic model is expected to enhance clinical decision-making processes by predicting the treatment-related risk for specific patients.Copyright © 2016 Elsevier Inc. All rights reserved.

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