What type of study is this?

This is a Cohort Study study (also classified as: Quantitative Study).

October 3, 2025Open Access

Prediction of hemorrhagic transformation after thrombolysis based on machine learning models combined with platelet distribution width-to-count ratio

XLXiaosheng LiKunming Medical University BLBo LeiKunming Medical University HXHongyun XuJianghan University

Key Points

The logistic regression model achieved optimal predictive performance, with an AUC of 0.919 for predicting hemorrhagic transformation.
Predictive features identified include age, diabetes, malignancy, onset-to-treatment time, baseline NIHSS score, and platelet distribution width.
Use of machine learning in predicting outcomes allows for better risk assessment in acute ischemic stroke patients after thrombolysis.
The study highlights the need for interpretable models, with the logistic regression model being effective for clinical decision-making.

Abstract

Background Hemorrhagic transformation (HT) is a common and potentially serious complication following intravenous thrombolysis (IVT) in patients with acute ischemic stroke (AIS). Despite its high incidence, there remains a lack of simple and effective tools for predicting HT risk. Objective This study aimed to develop an interpretable machine learning (ML) model incorporating the platelet distribution width to platelet count ratio (PPR) to predict HT occurrence in AIS patients after IVT. Methods We included AIS patients who underwent IVT at the First Affiliated Hospital of Kunming Medical University between July 2019 and April 2024. Four ML models—logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (Xgboost)—were constructed using 5-fold cross-validation, with HT after IVT as the outcome. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and balanced F-score. The best-performing model was selected for interpretability analysis, and feature importance was assessed. Results LASSO regression identified six predictive features with non-zero coefficients: age, diabetes, malignancy, onset-to-treatment time (OTT), baseline National Institutes of Health Stroke Scale (NIHSS) score, and PPR. Among the models, LR demonstrated the highest predictive performance, achieving an optimal AUC of 0.919, along with average accuracy, sensitivity, and specificity of 0.825, 0.830, and 0.832, respectively. Feature importance in the LR model ranked as follows: baseline NIHSS score, diabetes, PPR, malignancy, age, and OTT. Conclusion The LR-based model incorporating PPR effectively predicts HT risk in AIS patients after IVT, providing clinicians with a rapid and accurate tool to assess thrombolytic hemorrhage risk and support treatment decision-making.

Ask AI

Helpful

Bookmark

View Full Paper