Guan Jincheng, Wang Qiong, Hu Jiajia, Hu Yepeng, Lan Qiaoyu, Xiao Guoqiang, Zhou Borong, Guan Haitao
Department of Neurology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, People's Republic of China.
Department of Psychiatry, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, People's Republic of China.
Neuropsychiatr Dis Treat. 2023 Apr 13;19:879-894. doi: 10.2147/NDT.S400463. eCollection 2023.
Futile recanalization (FRC) is common among large artery occlusion (LAO) patients after endovascular therapy (EVT). We developed nomogram models to identify LAO patients at a high risk of FRC pre- and post-EVT to help neurologists select the optimal candidates for EVT.
From April 2020 to July 2022, EVT and mTICI score ≥2b LAO patients were recruited. Nomogram models was developed by two-step approach for predicting the outcomes of LAO patients. First, the least absolute shrinkage and selection operator (LASSO) regression analysis was to optimize variable selection. Then, a multivariable analysis was to construct an estimation model with significant indicators from the LASSO. The accuracy of the model was verified using receiver operating characteristic (ROC), calibration curve, and decision curve analyses (DCA), along with validation cohort (VC).
Using LASSO, age, sex, hypertension history, baseline NIHSS, ASPECTS and baseline SBP upon admission were identified from the pre-EVT variables. Model 1 (pre-EVT) showed good predictive performance, with an area under the ROC curve (AUC) of 0.815 in the training cohort (TrC) and 0.904 in VC. Under the DCA, the generated nomogram was clinically applicable where risk cut-off was between 15%-85% in the TrC and 5%-100% in the VC. Moreover, age, ASPECTS upon admission, onset duration, puncture-to-recanalization (PTR) duration, and lymphocyte-to-monocyte ratio (LMR) were screened by LASSO. Model 2 (post-EVT) also demonstrated good predictive performance with AUCs of 0.888 and 0.814 for TrC and VC, respectively. Under the DCA, the generated nomogram was clinically applicable if the risk cut-off was between 13-100% in the TrC and 22-85% of VC.
In this study, two nomogram models were generated that showed good discriminative performance, improved calibration, and clinical benefits. These nomograms can potentially accurately predict the risk of FRC in LAO patients pre- and post-EVT and help to select appropriate candidates for EVT.
在接受血管内治疗（EVT）的大动脉闭塞（LAO）患者中，无效再通（FRC）很常见。我们开发了列线图模型，以识别EVT前后FRC高风险的LAO患者，帮助神经科医生选择EVT的最佳候选者。
从2020年4月至2022年7月，招募接受EVT且改良脑梗死溶栓分级（mTICI）评分≥2b的LAO患者。采用两步法开发列线图模型来预测LAO患者的预后。首先，使用最小绝对收缩和选择算子（LASSO）回归分析来优化变量选择。然后，进行多变量分析，以构建一个包含LASSO分析中显著指标的估计模型。使用受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）以及验证队列（VC）来验证模型的准确性。
通过LASSO分析，从EVT前的变量中确定了年龄、性别、高血压病史、基线美国国立卫生研究院卒中量表（NIHSS）评分、脑梗死溶栓治疗前脑CT评分（ASPECTS）以及入院时的基线收缩压（SBP）。模型1（EVT前）显示出良好的预测性能，在训练队列（TrC）中的ROC曲线下面积（AUC）为0.815，在VC中的AUC为0.904。在DCA分析中，生成的列线图在临床上适用，TrC中的风险截断值在15%-85%之间，VC中的风险截断值在5%-100%之间。此外，通过LASSO分析筛选出年龄、入院时的ASPECTS评分、发病持续时间、穿刺至再通（PTR）持续时间以及淋巴细胞与单核细胞比值（LMR）。模型2（EVT后）也表现出良好的预测性能，TrC和VC的AUC分别为0.888和0.814。在DCA分析中，如果TrC中的风险截断值在13%-100%之间，VC中的风险截断值在22%-85%之间，生成的列线图在临床上适用。
在本研究中，生成了两个列线图模型，它们显示出良好的鉴别性能、改进的校准和临床益处。这些列线图可以潜在地准确预测LAO患者在EVT前后FRC的风险，并有助于选择合适的EVT候选者。
