Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Department of Rehabilitation Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
Aging Dis. 2024 Apr 25;15(6):2852-2862. doi: 10.14336/AD.2024.0127.
Our study aimed to construct a predictive model for identifying instances of futile recanalization in patients with anterior circulation occlusion acute ischemic stroke (AIS) who achieved complete reperfusion following endovascular therapy. We included 173 AIS patients who attained complete reperfusion, as indicated by a Modified Thrombolysis in Cerebral Infarction (mTICI) scale score of 3. Our approach involved a thorough analysis of clinical factors, imaging biomarkers, and potential no-reflow biomarkers through both univariate and multivariate analyses to identify predictors of futile recanalization. The comprehensive model includes clinical factors such as age, presence of diabetes, admission NIHSS score, and the number of stent retriever passes; imaging biomarkers like poor collaterals; and potential no-reflow biomarkers, notably disrupted blood-brain barrier (OR 4.321, 95% CI 1.794-10.405; p = 0.001), neutrophil-to-lymphocyte ratio (NLR; OR 1.095, 95% CI 1.009-1.188; p = 0.030), and D-dimer (OR 1.134, 95% CI 1.017-1.266; p = 0.024). The model demonstrated high predictive accuracy, with a C-index of 0.901 (95% CI 0.855-0.947) and 0.911 (95% CI 0.863-0.954) in the original and bootstrapping validation samples, respectively. Notably, the comprehensive model showed significantly improved predictive performance over models that did not include no-reflow biomarkers, evidenced by an integrated discrimination improvement of 8.86% (95% CI 4.34%-13.39%; p < 0.001) and a categorized reclassification improvement of 18.38% (95% CI 3.53%-33.23%; p = 0.015). This model, which leverages the potential of no-reflow biomarkers, could be especially beneficial in healthcare settings with limited resources. It provides a valuable tool for predicting futile recanalization, thereby informing clinical decision-making. Future research could explore further refinements to this model and its application in diverse clinical settings.
我们的研究旨在构建一个预测模型，以识别接受血管内治疗后完全再通的前循环闭塞急性缺血性脑卒中（AIS）患者中无效再通的病例。我们纳入了 173 名 AIS 患者，这些患者的再通程度完全，根据改良脑梗死溶栓（mTICI）评分达到 3 分。我们的方法包括通过单变量和多变量分析，对临床因素、影像学生物标志物和潜在无再流生物标志物进行全面分析，以确定无效再通的预测因素。综合模型包括临床因素，如年龄、是否存在糖尿病、入院 NIHSS 评分和支架取栓器通过次数；影像学生物标志物如侧支循环不良；以及潜在的无再流生物标志物，特别是血脑屏障破坏（OR 4.321，95%CI 1.794-10.405；p = 0.001）、中性粒细胞与淋巴细胞比值（NLR；OR 1.095，95%CI 1.009-1.188；p = 0.030）和 D-二聚体（OR 1.134，95%CI 1.017-1.266；p = 0.024）。该模型表现出较高的预测准确性，在原始和自举验证样本中的 C 指数分别为 0.901（95%CI 0.855-0.947）和 0.911（95%CI 0.863-0.954）。值得注意的是，与不包括无再流生物标志物的模型相比，综合模型的预测性能显著提高，综合判别改善 8.86%（95%CI 4.34%-13.39%；p<0.001），分类再分类改善 18.38%（95%CI 3.53%-33.23%；p=0.015）。该模型利用了无再流生物标志物的潜力，对于资源有限的医疗环境尤其有益。它为预测无效再通提供了有价值的工具，从而为临床决策提供信息。未来的研究可以进一步探讨对该模型的改进及其在不同临床环境中的应用。
