Development of the Neutrophil-to-Platelet Ratio (NPR) Integrated with Machine Learning for Predicting Early Mortality...

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.

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.

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.

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.