Development and External Validation of a Machine Learning Model to Predict Venous Thromboembolism Risk in Hospitalized Chinese Patients

Objectives To develop and externally validate a machine learning (ML) model of VTE in hospitalized Chinese patients. Methods We retrospectively reviewed structured data from patients with and without VTE (N = 1126 in each group) at Beijing Shijitan Hospital between January 2012 and December 2021. ML algorithms, including logistic regression (LR), decision tree (DT), or gradient boosting (GBoost) were used to establish prediction models. Patients were prospectively enrolled at Beijing Shijitan Hospital (N = 2916) or Beijing Chaoyang Hospital (N = 1339) for internal and external model validation, respectively. Results Several clinical features were had high weighted correlation with VTE in ML models: age, D-dimer level, platelet count, hemoglobin level, coronary heart disease, cancer, male sex, and comorbidity. The two highest weighted features were age and D-dimer. Optimal cut-off values indicated that age >65 years, white blood cell count >8.69 (10 9 /L), hemoglobin level >126 (g/L), platelet count >197 (10 9 /L), neutrophil percentage >72%, and D-dimer level >965 (ng/ml, D-dimer units) were significantly correlated with VTE occurrence. The AUC value of the GBoost model (0.88 ± 0.03) was significantly higher than the DT (0.70 ± 0.03) or LR (0.64 ± 0.03) models. GBoost also had a higher sensitivity, specificity, Youden index and Matthews correlation coefficient (MCC) than DT or LR (P < 0.05). In the internal validation cohort, the AUC value of the GBoost model (0.85 ± 0.03) was higher than the DT (0.76 ± 0.03) and LR (0.67 ± 0.03) models and the Caprini Risk Assessment Model (RAM) (0.75 ± 0.03). GBoost also had a greater sensitivity, specificity, Youden index and MCC than DT and LR (P < 0.05). In the external validation cohort, the AUC value of the GBoost model (0.81 ± 0.03) was also better than the DT (0.72 ± 0.03) and LR (0.71 ± 0.03) models and the Caprini RAM (0.73 ± 0.03). The GBoost model also had a higher sensitivity, specificity, Youden index and MCC than DT and LR (P < 0.05). Conclusion The GBoost ML model was better at identifying hospitalized Chinese patients at risk of VTE than other ML algorithms and the Caprini RAM.