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Published on: February 10, 2023
A Machine Learning-Based Prognostic Model for Lower Extremity Deep Vein Thrombosis Following Acute Stroke
Lingling Liu1, Juan Zhou2, Liping Li
1Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Summary
This study developed a machine learning model to predict deep vein thrombosis (DVT) risk in acute stroke patients, using limb function assessments. Age and lower limb Brunnstrom stage were key predictors for improved risk stratification.
Area of Science:
- Neurology
- Medical Informatics
- Vascular Medicine
Background:
- Deep vein thrombosis (DVT) is a significant complication following acute stroke.
- Accurate prediction of DVT risk is crucial for timely intervention and improved patient outcomes.
- Existing prognostic models may not fully incorporate detailed limb functional assessments.
Purpose of the Study:
- To develop and validate a machine learning (ML)-based prognostic model for predicting lower extremity DVT risk post-acute stroke.
- To emphasize the role of limb functional assessments in DVT risk prediction.
- To compare the performance of different ML models against traditional statistical methods.
Main Methods:
- Retrospective analysis of 225 acute stroke patients.
- Variable selection using LASSO regression.
- Comparison of Gradient Boosting Machine (GBM), Random Survival Forest (RSF), and Generalized Linear Model (GLM) survival models.
- Performance evaluation using concordance index (C-index), C/D AUC, and integrated Brier score (IBS).
Main Results:
- Six key predictors identified: age, stroke type, gender, muscle tension, Brunnstrom stage (lower limb), and sitting balance.
- The Random Survival Forest (RSF) model demonstrated superior performance (IBS: 0.081, C-index: 0.841).
- Age and lower limb Brunnstrom stage were the most influential predictors.
Conclusions:
- An effective ML-based prognostic model for predicting lower extremity DVT risk after acute stroke was developed.
- The model highlights the significance of age and lower limb functional status (Brunnstrom stage) in DVT risk.
- This tool offers potential for enhanced clinical risk stratification in stroke patients.
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