Developing an Explainable Prognostic Model for Acute Ischemic Stroke: Combining Clinical and Inflammatory Biomarkers With Machine Learning

作者：Linlin Ma, Lang Ji, Zhe Cheng, Xiaokun Geng, Yuchuan Ding · 发表于：Brain and Behavior · 年份：2025 · DOI：10.1002/brb3.70673 · 被引用次数：3 · 研究领域：Acute Ischemic Stroke Management、Neuroinflammation and Neurodegeneration Mechanisms、Intracerebral and Subarachnoid Hemorrhage Research

BACKGROUND: Predicting the prognosis of patients with acute cerebral infarction (ACI) is crucial for clinical decision-making and personalized treatment. However, existing models often lack the comprehensive integration of clinical and biological indicators necessary for accurate and interpretable predictions. This study aims to develop and validate a predictive model using a combination of clinical assessments and inflammatory biomarkers to improve the prognostication of ACI patients. METHODS: This real-world, retrospective cohort study was conducted at Luhe Hospital, Beijing, and included 1,017 ACI patients admitted within 24 h of symptom onset. The dataset was randomly split into a training set (80%) and a validation set (20%). Twelve machine learning models were developed and evaluated, with the optimal model and feature set selected based on comprehensive performance metrics. To enhance interpretability, the Shapley Additive exPlanations (SHAP) method was employed to quantify and visualize the contribution of each feature to the model's predictions. RESULTS: The final model, utilizing the Logistic Regression (LR) algorithm, incorporated six key features: NIHSS at 24 h (NIHSS_24 h), NIHSS_change, D-dimer, neutrophil count (N), lymphocyte percentage at 24 h (L_pct_24 h), and length of stay (LOS). NIHSS_24 h emerged as a critical early prognostic indicator, effectively predicting three-month outcomes post-discharge. Inflammatory markers, including D-dimer, N, and L_pct_24 h...