Yang Shijie, Zhao Kaixuan, Xi Huan, Xiao Zaixing, Li Wei, Zhang Yichuan, Fan Zhiqiang, Li Changqing, Chai Erqing
The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Gansu University of Chinese Medicine, Lanzhou, Gansu, People's Republic of China.
Clinical Medical College, Ningxia Medical University, Yinchuan, Ningxia, People's Republic of China.
Risk Manag Healthc Policy. 2021 Oct 29;14:4439-4446. doi: 10.2147/RMHP.S317834. eCollection 2021.
This study aimed to determine the risk factors associated with the number of thrombectomy device passes and establish a nomogram for predicting the number of device pass attempts in patients with successful endovascular thrombectomy (EVT).
We enrolled patients from a signal comprehensive stroke center (CSC) who underwent EVT because of large vessel occlusion stroke. Multivariate logistic regression analysis was used to develop the best-fit nomogram for predicting the number of thrombectomy device passes. The discrimination and calibration of the nomogram were estimated using the area under the receiver operating characteristic curve (AUC-ROC) and a calibration plot with a bootstrap of 1000 resamples. A decision curve analysis (DCA) was used to measure the availability and effect of this predictive tool.
In total, 130 patients (mean age 64.9 ± 11.1 years; 83 males) were included in the final analysis. Age (odds ratio [OR], 1.085; 95% confidence interval [CI], 1.005-1.172; = 0.036), baseline Alberta Stroke Program Early computed tomography (ASPECTS) score (OR, 0.237; 95% CI, 0.115-0.486; < 0.001), and homocysteine level (OR, 1.090; 95% CI, 1.028-1.155; = 0.004) were independent predictors of device pass number and were thus incorporated into the nomogram. The AUC-ROC determined the discrimination ability of the nomogram, which was 0.921 (95% CI, 0.860-0.980), which indicated good predictive power. Moreover, the calibration plot revealed good predictive accuracy of the nomogram. The DCA demonstrated that when the threshold probabilities of the cohort ranged between 5.0% and 98.0%, the use of the nomogram to predict a device pass number > 3 provided greater net benefit than did "treat all" or "treat none" strategies.
The nomogram comprised age, baseline ASPECTS score, and homocysteine level, can predict a device pass number >3 in acute ischemic stroke (AIS) patients who are undergoing EVT.
本研究旨在确定与取栓装置通过次数相关的危险因素，并建立一种列线图，用于预测成功进行血管内血栓切除术（EVT）患者的装置通过尝试次数。
我们纳入了来自一家大型综合卒中中心（CSC）因大血管闭塞性卒中接受EVT治疗的患者。采用多因素逻辑回归分析来制定预测取栓装置通过次数的最佳拟合列线图。使用受试者工作特征曲线下面积（AUC-ROC）和带有1000次重采样自助法的校准图来评估列线图的区分度和校准度。采用决策曲线分析（DCA）来衡量该预测工具的可用性和效果。
最终分析共纳入130例患者（平均年龄64.9±11.1岁；男性83例）。年龄（比值比[OR]，1.085；95%置信区间[CI]，1.005 - 1.172；P = 0.036）、基线阿尔伯塔卒中项目早期计算机断层扫描（ASPECTS）评分（OR，0.237；95% CI，0.115 - 0.486；P < 0.001）和同型半胱氨酸水平（OR，1.090；95% CI，1.028 - 1.155；P = 0.004）是装置通过次数的独立预测因素，因此被纳入列线图。AUC-ROC确定了列线图的区分能力，为0.921（95% CI，0.860 - 0.980），表明具有良好的预测能力。此外，校准图显示列线图具有良好的预测准确性。DCA表明，当队列的阈值概率在5.0%至98.0%之间时，使用列线图预测装置通过次数>3比“全部治疗”或“不治疗”策略提供了更大的净效益。
该列线图包含年龄、基线ASPECTS评分和同型半胱氨酸水平，可预测正在接受EVT治疗的急性缺血性卒中（AIS）患者的装置通过次数>3。
