International journal of general medicine · 2026
Machine Learning-Based Prediction of Lower Extremity Deep Vein Thrombosis in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease.
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Zhou Li, Wang Hui, Yao Di, Chen Yilin, Li Shanshan, Liu Qianfei
Аннотация
OBJECTIVE: Patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) have an increased risk of lower-extremity deep vein thrombosis (DVT) due to inflammation, immobility, and coagulation abnormalities. This study aimed to develop a machine learning model for predicting DVT risk in AECOPD patients and identify important predictors.
METHODS: This single-center retrospective study included 1,500 patients with AECOPD who were classified into DVT (n = 126) and non-DVT groups (n = 1,374) according to lower-limb venous ultrasonography findings. Clinical characteristics and laboratory parameters were collected. The Boruta algorithm was applied for feature selection, and three machine learning models, including decision tree (DT), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost), were developed. Model performance was assessed using the receiver operating characteristic (ROC) curve, area under the curve (AUC), and multiple classification metrics. TOPSIS was used for comprehensive model evaluation, and SHAP analysis was performed to interpret the optimal model.
RESULTS: Compared with the non-DVT group, patients with DVT had higher levels of C-reactive protein, D-dimer, and systemic inflammation-coagulation index (SCI) (all P < 0.05). The Boruta algorithm identified 14 key features. XGBoost showed the best performance in the testing set, with an AUC of 0.707, compared with MLP (0.684) and DT (0.667). TOPSIS analysis ranked XGBoost highest, with an overall score of 0.806. SHAP analysis identified D-dimer as the most influential predictor, followed by SCI, albumin, mean corpuscular volume, and C-reactive protein.
CONCLUSION: The XGBoost model showed potential for DVT risk assessment in patients with AECOPD. SCI, as an integrated marker of inflammation and coagulation, may provide complementary information for risk stratification. However, further validation in independent cohorts is required before its potential clinical application.
METHODS: This single-center retrospective study included 1,500 patients with AECOPD who were classified into DVT (n = 126) and non-DVT groups (n = 1,374) according to lower-limb venous ultrasonography findings. Clinical characteristics and laboratory parameters were collected. The Boruta algorithm was applied for feature selection, and three machine learning models, including decision tree (DT), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost), were developed. Model performance was assessed using the receiver operating characteristic (ROC) curve, area under the curve (AUC), and multiple classification metrics. TOPSIS was used for comprehensive model evaluation, and SHAP analysis was performed to interpret the optimal model.
RESULTS: Compared with the non-DVT group, patients with DVT had higher levels of C-reactive protein, D-dimer, and systemic inflammation-coagulation index (SCI) (all P < 0.05). The Boruta algorithm identified 14 key features. XGBoost showed the best performance in the testing set, with an AUC of 0.707, compared with MLP (0.684) and DT (0.667). TOPSIS analysis ranked XGBoost highest, with an overall score of 0.806. SHAP analysis identified D-dimer as the most influential predictor, followed by SCI, albumin, mean corpuscular volume, and C-reactive protein.
CONCLUSION: The XGBoost model showed potential for DVT risk assessment in patients with AECOPD. SCI, as an integrated marker of inflammation and coagulation, may provide complementary information for risk stratification. However, further validation in independent cohorts is required before its potential clinical application.