Development and Validation of Multiple Machine Learning Models Integrating Neutrophil-Lymphocyte Ratio for Prediction of Hemorrhagic Transformation After Intravenous Thrombolysis in Acute Ischemic Stroke.

ABSTRACT:

Background

Hemorrhagic transformation (HT) is a critical complication of intravenous thrombolysis (IVT) in acute ischemic stroke (AIS). This study developed and validated machine learning (ML) models integrating inflammatory biomarkers with clinical indicators to predict post-IVT HT.

Methods

In 1272 IVT-treated AIS patients, the least absolute shrinkage and selection operator (LASSO) regression identified five predictors from 17 variables, which were subsequently utilized to construct eight ML models. The models were trained (70% data) and tested (30% data). Furthermore, external validation conducted on an independent cohort substantiated the generalizability of the optimal model. The SHapley Additive exPlanations (SHAP) method explained feature importance.

Results

LA

SUBMITTER: Bu F 

PROVIDER: S-EPMC12700843 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature