炎症生物标志物的年龄特异性差异及其对机械取栓术后无效再通的影响：一项逆概率加权分析。
Age-Specific Differences in Inflammatory Biomarkers and Their Impact on Futile Recanalization After Mechanical Thrombectomy: An Inverse Probability Weighting Analysis.
作者信息
Prandin Gabriele, Valente Mariarosaria, Zhang Liqun, Malhotra Paresh, Sacco Simona, Foschi Matteo, Ornello Raffaele, Pirera Edoardo, Toraldo Francesco, Maisano Domenico, Del Regno Caterina, Komauli Filippo, Jaramillo Adelaida Gartner, Al-Karadsheh Hakam, Zahid Hamza, Klein Piers, Abdalkader Mohamad, Manganotti Paolo, Lobotesis Kyriakos, Nguyen Thanh N, Banerjee Soma, Gigli Gian Luigi, Merlino Giovanni, D'Anna Lucio
机构信息
Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, University Hospital and Health Services of Trieste, ASUGI, University of Trieste, Trieste, Italy.
Department of Brain Sciences, Imperial College London, London, UK.
出版信息
Eur J Neurol. 2025 May;32(5):e70182. doi: 10.1111/ene.70182.
BACKGROUND
Mechanical thrombectomy (MT) is the standard treatment for large vessel occlusion (LVO) stroke. However, a substantial proportion of patients experience poor functional outcomes despite successful reperfusion, namely futile recanalization (FR). This study aimed to evaluate the predictive value of inflammatory biomarkers, measured on admission and at 24 h, in identifying the risk of FR and to assess age-specific differences influencing this outcome.
METHODS
This international, multicenter, observational study included patients with anterior circulation LVO stroke treated with MT. Strict inclusion criteria were applied to minimize confounding factors related to inflammation. Inflammatory biomarkers were assessed at admission and 24 h post-procedure. Inverse probability weighting (IPW) was utilized to balance baseline characteristics between patients with FR and effective recanalization (ER). Least absolute shrinkage and selection operator (LASSO) regression was applied to identify independent predictors, and restricted cubic splines were used to determine optimal biomarker cut-offs.
RESULTS
Among 885 patients, 470 (53%) experienced FR. In multivariate analysis, 24-h CRP (OR 1.01, 95% CI 1.01-1.02, p = 0.018) and 24-h NLR (OR 1.11, 95% CI 1.02-1.22, p = 0.019) were significant predictors of FR, with cut-offs of 8.55 and 4.58, respectively. In patients aged < 80 years, 24-h CRP and NLR were most predictive (cut-offs: 17.09 and 5.59). In patients aged ≥ 80 years, admission SIRI emerged as the most significant predictor (OR 1.24, 95% CI 1.06-1.50, p = 0.015), with an optimal cut-off value of 2.53.
CONCLUSIONS
Inflammatory biomarkers exhibit significant predictive value for FR following MT, with distinct age-specific patterns. These findings underscore the importance of tailoring predictive models and interventions to optimize clinical outcomes.
背景
机械取栓术（MT）是大血管闭塞（LVO）性卒中的标准治疗方法。然而，尽管成功再灌注，仍有相当一部分患者功能结局不佳，即无效再通（FR）。本研究旨在评估入院时和24小时时测量的炎症生物标志物在识别FR风险方面的预测价值，并评估影响这一结局的年龄特异性差异。
方法
这项国际多中心观察性研究纳入了接受MT治疗的前循环LVO性卒中患者。应用严格的纳入标准以尽量减少与炎症相关的混杂因素。在入院时和术后24小时评估炎症生物标志物。采用逆概率加权（IPW）来平衡FR患者和有效再通（ER）患者之间的基线特征。应用最小绝对收缩和选择算子（LASSO）回归来确定独立预测因素，并使用限制立方样条来确定最佳生物标志物临界值。
结果
在885例患者中，470例（53%）出现FR。在多变量分析中，24小时C反应蛋白（OR 1.01，95%CI 1.01 - 1.02，p = 0.018）和24小时中性粒细胞与淋巴细胞比值（NLR）（OR 1.11，95%CI 1.02 - 1.22，p = 0.019）是FR的显著预测因素，临界值分别为8.55和4.58。在年龄<80岁的患者中，24小时C反应蛋白和NLR的预测性最强（临界值：17.09和5.59）。在年龄≥80岁的患者中，入院时全身炎症反应指数（SIRI）成为最显著的预测因素（OR 1.24，95%CI 1.06 - 1.50，p = 0.015），最佳临界值为2.53。
结论
炎症生物标志物对MT术后的FR具有显著预测价值，且具有明显的年龄特异性模式。这些发现强调了定制预测模型和干预措施以优化临床结局的重要性。
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