Age‐Specific Differences in Inflammatory Biomarkers and Their Impact on Futile Recanalization After Mechanical Thrombectomy: An Inverse Probability Weighting Analysis

• DOI: 10.1111/ene.70182
• Publication Date: 2025-05-12T12:04:05Z
• Authors: Gabriele Prandin; Mariarosaria Valente; Liqun Zhang; Paresh Malhotra; Simona Sacco; Matteo Foschi; Raffaele Ornello; Edoardo Pirera; Francesco Toraldo; Domenico Maisano; Caterina Del Regno; Filippo Komauli; Adelaida Gartner ...; Hakam AL‐Karadsheh; Hamza Zahid; Piers Klein; Mohamad Abdalkader; Paolo Manganotti; Kyriakos Lobotesis; Thanh N. Nguyen; Soma Banerjee; Gian Luigi Gigli; Giovanni Merlino; Lucio D'Anna

ABSTRACTBackgroundMechanical 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.MethodsThis 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.ResultsAmong 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.ConclusionsInflammatory 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.