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<p>Patients with intracerebral hemorrhage (ICH) are at high risk of venous thromboembolism (VTE). Current risk assessment tools are limited and not tailored for neurocritical care populations. This study aimed to develop and validate machine learning-based models to predict VTE in ICH patients. Clinical data of 872 ICH patients admitted to the Neurosurgical ICU of Huashan Hospital from June 2018 to July 2023 were analysed. After univariate analysis, feature selection was performed using Random Forest Importance Ranking and LASSO regression. Three machine learning models (random forest, logistic regression, and LASSO logistic regression) were trained using 10-fold cross-validation. The dataset was randomly split into training (80%) and validation (20%) sets. Model performance was evaluated using accuracy, sensitivity, specificity, F1 score, AUC, calibration curves, and decision curve analysis. Among 421 patients included in the final analysis, 215 (51.1%) developed VTE. Five independent predictors (BMI, D-dimer, homocysteine, triglycerides, albumin) were identified. All three models showed strong discriminatory performance, with the random forest model achieving the highest AUC (0.98) and PR-AUC (0.98), followed by logistic regression (AUC 0.94, PR-AUC 0.91) and LASSO-LR (AUC 0.93, PR-AUC 0.91). Machine learning-based models incorporating metabolic and clinical predictors can accurately stratify VTE risk in ICH patients. The random forest model demonstrated superior performance and clinical applicability, highlighting potential for guiding early prophylactic interventions.</p>

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Prediction of venous thromboembolism after spontaneous intracerebral hemorrhage based on machine learning

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Lei Yang,
  • Fengyuan Zhou,
  • Yiling Xia,
  • Haijun Yao,
  • Yanjie Chen,
  • Yan Shangguan,
  • Gang Wu,
  • Jin Hu,
  • Mei-Hua Wang

摘要

Patients with intracerebral hemorrhage (ICH) are at high risk of venous thromboembolism (VTE). Current risk assessment tools are limited and not tailored for neurocritical care populations. This study aimed to develop and validate machine learning-based models to predict VTE in ICH patients. Clinical data of 872 ICH patients admitted to the Neurosurgical ICU of Huashan Hospital from June 2018 to July 2023 were analysed. After univariate analysis, feature selection was performed using Random Forest Importance Ranking and LASSO regression. Three machine learning models (random forest, logistic regression, and LASSO logistic regression) were trained using 10-fold cross-validation. The dataset was randomly split into training (80%) and validation (20%) sets. Model performance was evaluated using accuracy, sensitivity, specificity, F1 score, AUC, calibration curves, and decision curve analysis. Among 421 patients included in the final analysis, 215 (51.1%) developed VTE. Five independent predictors (BMI, D-dimer, homocysteine, triglycerides, albumin) were identified. All three models showed strong discriminatory performance, with the random forest model achieving the highest AUC (0.98) and PR-AUC (0.98), followed by logistic regression (AUC 0.94, PR-AUC 0.91) and LASSO-LR (AUC 0.93, PR-AUC 0.91). Machine learning-based models incorporating metabolic and clinical predictors can accurately stratify VTE risk in ICH patients. The random forest model demonstrated superior performance and clinical applicability, highlighting potential for guiding early prophylactic interventions.