Lin ShiTeng, Lin XinPing, Zhang Juan, Wan Meng, Chen Chen, Jie Qiong, Wu YueZhang, Qiu RunZe, Cui XiaoLi, Jiang ChunLian, Zou JianJun, Zhao ZhiHong
School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Front Neurol. 2022 Aug 26;13:968037. doi: 10.3389/fneur.2022.968037. eCollection 2022.
Futile recanalization occurs in a significant proportion of patients with basilar artery occlusion (BAO) after endovascular thrombectomy (EVT). Therefore, our goal was to develop a visualized nomogram model to early identify patients with BAO who would be at high risk of futile recanalization, more importantly, to aid neurologists in selecting the most appropriate candidates for EVT.
Patients with BAO with EVT and the Thrombolysis in Cerebral Infarction score of ≥2b were included in the National Advanced Stroke Center of Nanjing First Hospital (China) from October 2016 to June 2021. The exclusion criteria were lacking the 3-month Modified Rankin Scale (mRS), age <18 years, the premorbid mRS score >2, and unavailable baseline CT imaging. Potential predictors were selected for the construction of the nomogram model and the predictive and calibration capabilities of the model were assessed.
A total of 84 patients with BAO were finally enrolled in this study, and patients with futile recanalization accounted for 50.0% (42). The area under the curve (AUC) of the nomogram model was 0.866 (95% CI, 0.786-0.946). The mean squared error, an indicator of the calibration ability of our prediction model, was 0.025. A web-based nomogram model for broader and easier access by clinicians is available online at https://trend.shinyapps.io/DynNomapp/.
We constructed a visualized nomogram model to accurately and online predict the risk of futile recanalization for patients with BAO, as well as assist in the selection of appropriate candidates for EVT.
在血管内血栓切除术(EVT)治疗基底动脉闭塞(BAO)的患者中,有相当比例会出现无效再通。因此,我们的目标是建立一个可视化列线图模型,以早期识别有无效再通高风险的BAO患者,更重要的是,帮助神经科医生选择最合适的EVT候选患者。
2016年10月至2021年6月期间,来自中国南京第一医院国家高级卒中中心的BAO患者接受了EVT治疗,且脑梗死溶栓评分≥2b。排除标准为缺乏3个月改良Rankin量表(mRS)评分、年龄<18岁、病前mRS评分>2以及无法获得基线CT影像。选择潜在预测因素构建列线图模型,并评估该模型的预测和校准能力。
本研究最终纳入84例BAO患者,无效再通患者占50.0%(42例)。列线图模型的曲线下面积(AUC)为0.866(95%CI,0.786 - 0.946)。我们预测模型校准能力的指标均方误差为0.025。临床医生可通过https://trend.shinyapps.io/DynNomapp/在线访问基于网络的列线图模型,以便更广泛、更便捷地使用。
我们构建了一个可视化列线图模型,用于准确在线预测BAO患者无效再通的风险,并协助选择合适的EVT候选患者。