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Development and Validation of an Interpretable Machine Learning Model for Predicting venous Thromboembolism in ICU
Hao Qi1, Lingli Li2, Juan Fang3
1Department of Neurosurgery, The 904th Hospital of PLA, Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu Province, China.
Objective:
To develop and validate a machine learning model predicting venous thromboembolism (VTE) risk in intensive care unit (ICU) patients with traumatic brain injury (TBI).
Methods:
Utilizing data from 1564 TBI patients in MIMIC-IV (2008-2021), models were trained (80%) and tested (20%). We used 304 TBI patients from the 904th Hospital of the Joint Support Force of the Chinese People's Liberation Army's ICU (2021-2024) as an external validation cohort. Feature selection employed Boruta and Least Absolute Shrinkage and Selection Operatoř regression. Four machine learning algorithms, including support vector machine (SVM) and logistic regression (LR), were trained. Performance was evaluated using area under the curve and other metrics. The Shapley Additive explanations method interpreted feature importance.
Results:
Independent risk factors for VTE were identified as length of hospital stay, systolic blood pressure, lower limb fracture, and lung infection. Six variables were selected: ICU stay duration, length of hospital stay, lung infection, heart rate, partial thromboplastin time, and lower limb fracture. Among the 4 ML models, the LR model demonstrated excellent performance, with area under the curve values of 0.723 for the test set, and 0.759 for the external validation set. The nomogram based on the LR model showed good performance in the calibration curve and the clinical decision curve. Furthermore, Shapley Additive explanations analysis highlights that the length of ICU stay and lung infection are the primary determinants influencing the prediction.
Conclusions:
The logistic regression model demonstrates strong predictive capability for early VTE following TBI, which may potentially contribute to reducing complications associated with VTE and improving patient outcomes.
