Ogilvy Christopher S, Chua Michelle H, Fusco Matthew R, Reddy Arra S, Thomas Ajith J
*Neurosurgical Service, Beth Israel Deaconess Medical Center, Brain Aneurysm Institute, Harvard Medical School, Boston, Massachusetts; ‡Harvard Medical School, Boston, Massachusetts.
Neurosurgery. 2015 Apr;76(4):390-5; discussion 395. doi: 10.1227/NEU.0000000000000651.
With the increasing use of endovascular techniques in the treatment of both ruptured and unruptured intracranial aneurysms, the issue of obliteration efficacy has become increasingly important.
To systematically develop a comprehensive model for predicting retreatment with various types of endovascular treatment.
We retrospectively reviewed medical records that were prospectively collected for 305 patients who received endovascular treatment for intracranial aneurysms from 2007 to 2013. Multivariable logistic regression was performed on candidate predictors identified by univariable screening analysis to detect independent predictors of retreatment. A composite risk score was constructed based on the proportional contribution of independent predictors in the multivariable model.
Size (>10 mm), aneurysm rupture, stent assistance, and posttreatment degree of aneurysm occlusion were independently associated with retreatment, whereas intraluminal thrombosis and flow diversion demonstrated a trend toward retreatment. The Aneurysm Recanalization Stratification Scale was constructed by assigning the following weights to statistically and clinically significant predictors: aneurysm-specific factors: size (>10 mm), 2 points; rupture, 2 points; presence of thrombus, 2 points. Treatment-related factors were stent assistance, -1 point; flow diversion, -2 points; Raymond Roy occlusion class 2, 1 point; Raymond Roy occlusion class 3, 2 points. This scale demonstrated good discrimination with a C-statistic of 0.799.
Surgical decision making and patient-centered informed consent require comprehensive and accessible information on treatment efficacy. We constructed the Aneurysm Recanalization Stratification Scale to enhance this decision-making process. This is the first comprehensive model that has been developed to quantitatively predict the risk of retreatment after endovascular therapy.
随着血管内技术在破裂和未破裂颅内动脉瘤治疗中的应用日益增加,闭塞疗效问题变得越来越重要。
系统地建立一个综合模型,用于预测各种类型血管内治疗后的再治疗情况。
我们回顾性分析了2007年至2013年期间接受颅内动脉瘤血管内治疗的305例患者的前瞻性收集的病历。对单变量筛选分析确定的候选预测因素进行多变量逻辑回归,以检测再治疗的独立预测因素。基于多变量模型中独立预测因素的比例贡献构建复合风险评分。
动脉瘤大小(>10mm)、破裂、支架辅助和治疗后动脉瘤闭塞程度与再治疗独立相关,而腔内血栓形成和血流导向显示出再治疗的趋势。通过对具有统计学和临床意义的预测因素赋予以下权重构建动脉瘤再通分层量表:动脉瘤特异性因素:大小(>10mm),2分;破裂,2分;血栓存在,2分。治疗相关因素为支架辅助,-1分;血流导向,-2分;Raymond Roy闭塞分级2级,1分;Raymond Roy闭塞分级3级,2分。该量表显示出良好的辨别力,C统计量为0.799。
手术决策和以患者为中心的知情同意需要关于治疗疗效的全面且易于获取的信息。我们构建了动脉瘤再通分层量表以加强这一决策过程。这是首个开发用于定量预测血管内治疗后再治疗风险的综合模型。