Supplementary Material for: Development and validation of a prediction model for failure of the transfemoral approach of endovascular treatment for large vessel occlusion acute ischemic stroke.

Introduction: Extracranial vascular characteristics determine the accessibility of the large vessel intracranial occlusion for endovascular treatment (EVT) in acute ischemic stroke. We developed and validated a prediction model for failure of the transfemoral approach to aid clinical decision making regarding EVT.
Methods: A prediction model was developed from data of patients included in the Dutch multicenter MR CLEAN Registry (March 18th 2014 until June 15th 2016) with penalized logistic regression. Predictor variables were available prior to the EVT procedure and included age, hypertension and extracranial vascular characteristics assessed on baseline CTA. The prediction model was internally validated, temporally validated within a second MR CLEAN Registry cohort (June 15th 2016 until November 1st 2017) and updated by re-estimating the coefficients using the combined cohort.
Results: Failure of the transfemoral approach occurred in 7% of patients, in both cohorts (derivation cohort: n=887, median age 71 years, interquartile range [IQR] 60-80, 52% men; validation cohort: n=1111, median age 73 years, IQR 62-81, 51% men). The prediction model had a c-statistic of 0.81 (95%CI: 0.76-0.86) in the derivation cohort, 0.69 (95%CI: 0.62-0.75) at temporal validation, and 0.75 (95%CI: 0.71-0.79) in the final prediction model, with the following penalized β-coefficients for predictors age (per decade): 0.26, hypertension: -0.16, severe aortic arch elongation: 1.45, bovine aortic arch: 0.44, elongation of the supra-aortic arteries: 0.72, cervical ICA elongation: 0.44, and high-grade stenosis of the cervical ICA: 0.78.
Conclusion: Our prediction model showed good performance for prediction of failure to reach the intracranial occlusion by the transfemoral approach.

引言：颅外血管特征决定了在大血管颅内闭塞（EVT）治疗急性缺血性卒中时的可及性。本研究开发并验证了一个预测模型，以预测经股动脉途径治疗失败，从而辅助临床决策。方法：从荷兰多中心MR CLEAN注册数据库（2014年3月18日至2016年6月15日）的数据中，采用惩罚性逻辑回归方法开发预测模型。预测变量在EVT手术前即可获得，包括年龄、高血压和基于基线CTA评估的颅外血管特征。预测模型进行了内部验证，并在第二个MR CLEAN注册队列（2016年6月15日至2017年11月1日）中进行时间验证，并通过联合队列重新估计系数进行更新。结果：在两个队列中，经股动脉途径治疗失败的发生率为7%（推导队列：n=887，中位年龄71岁，四分位间距[IQR] 60-80，男性52%；验证队列：n=1111，中位年龄73岁，IQR 62-81，男性51%）。在推导队列中，预测模型的c统计量为0.81（95%CI：0.76-0.86），时间验证时为0.69（95%CI：0.62-0.75），最终预测模型的c统计量为0.75（95%CI：0.71-0.79），预测变量的惩罚β系数如下：年龄（每十年）：0.26，高血压：-0.16，严重主动脉弓延长：1.45，牛型主动脉弓：0.44，主动脉弓上动脉延长：0.72，颈内动脉延长：0.44，以及颈内动脉重度狭窄：0.78。结论：我们的预测模型在预测经股动脉途径治疗失败达到颅内闭塞方面表现出良好的性能。