ArticleJournal of inflammation research2026
Development of the Neutrophil-to-Platelet Ratio (NPR) Integrated with Machine Learning for Predicting Early Mortality After Mechanical Thrombectomy in Acute Ischemic Stroke.
Article in Journal of inflammation research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: This study aimed to evaluate the predictive value of inflammatory markers, particularly the neutrophil-to-platelet ratio (NPR), combined with clinical parameters for early mortality following mechanical thrombectomy (MT) in patients with large artery occlusive acute ischemic stroke (LAO-AIS), to guide timely clinical interventions. Patients and methods: This retrospective study analyzed 320 LAO-AIS patients who underwent MT between January 2023 and January 2025. Missing data (<15%) were imputed. Boruta feature selection identified variables for multiple logistic regression. The dataset was randomly divided into training and test sets (7:3). A nomogram was constructed, and four machine learning algorithms-Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Naive Bayes (NB)-were developed and validated. Results: Early mortality occurred in 67 cases. Multivariate analysis identified six independent predictors: standardized NPR (NPR_std; OR = 4.51, P < 0.001), age (OR = 1.10, P < 0.001), decompressive craniectomy (DC; OR = 0.19, P < 0.001), responsible artery location (OR = 0.34, P = 0.006), lymphocyte count (LYM; OR = 2.14, P = 0.008), and prothrombin time (PT; OR = 1.31, P = 0.011). The nomogram showed high reliability. XGBoost achieved superior predictive performance, with SHapley Additive exPlanations (SHAP) analysis confirming NPR_std as the most important predictor. Conclusion: The neutrophil-to-platelet ratio (NPR) is an independent predictor of early mortality after MT in LAO-AIS patients. The predictive model provides valuable guidance for clinicians to adjust treatment strategies early.
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