Development and Validation of the Procedure-Related Neurologic Complications Risk Score for Elderly Patients with Ruptured Intracranial Aneurysm Undergoing Endovascular Treatment.

Duan, Guoli; Wen, Wanling; Zuo, Qiao; Yang, Pengfei; Zhang, Lei; Hong, Bo; Xu, Yi; Liu, Jianmin et al. · World Neurosurg · 2017

retrospective_cohort · Level III

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Abstract

Our aim was to develop and validate a procedure-related neurologic complications (PNC) risk score for individual elderly patients with ruptured intracranial aneurysms undergoing endovascular treatment (EVT). Preoperatively collected data, including clinical, lesion, and procedure characteristics of consecutive elderly patients (≥60 years), were used to develop a PNC risk predictive score based on the coefficients (β) of a multivariable logistic regression analysis. The PNC included intraprocedural rupture, thromboembolic events, and rebleeding within 30 days after EVT. Overall, 520 elderly patients who underwent EVT were enrolled. At 30 days, the PNC rate was 13.08%. Six risk factors were independently associated with PNC and comprised the PNC score (PNC score, 0-16 points): hypertension (2 points), Hunt-Hess grade ≥4 (3 points), Fisher grade ≥3 (2 points), wide-necked aneurysm (2 points), with a bleb on the aneurysm sac (3 points), and aneurysm size (3-10 mm, 1 point; <3 mm, 4 points). The PNC score model predicted the risk of PNC at a sensitivity of 63.22% and specificity of 84.79%. Moreover, the PNC score demonstrated significant discrimination (area under curve, 0.799; P < 0.001) and calibration (Hosmer-Lemeshow test, P = 0.319). Excellent prediction, discrimination, and calibration properties were reproduced by the internal validation group with bootstrapping techniques. The PNC score can be an easily applicable tool for predicting the risk of PNC for individual elderly patients with ruptured intracranial aneurysms undergoing EVT. Our study provides large case-based evidence supporting the integration of individual clinical, lesion, and procedure characteristics to predict PNC risk.

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