Liang Xu, Miao Da

Department of Psychiatry, Huzhou Third Municipal Hospital, the Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, People’s Republic of China

Correspondence: Miao Da, Department of Psychiatry, Huzhou Third Municipal Hospital, the Affiliated Hospital of Huzhou University, 2088 East Tiaoxi Road, Huzhou, Zhejiang, People’s Republic of China, Tel +860572 2290561, Email dm2315891089@163.com

Background: Psychiatric inpatients face an increased risk of deep vein thrombosis (DVT) due to their psychiatric conditions and pharmacological treatments. However, research focusing on this population remains limited.
Methods: This study analyzed 17,434 psychiatric inpatients at Huzhou Third Municipal Hospital, incorporating data on demographics, psychiatric diagnoses, physical illnesses, laboratory results, and medication use. Predictive models for DVT were developed using logistic regression, random forest, support vector machine (SVM), and XGBoost (Extreme Gradient Boosting). Feature importance was assessed using the random forest model.
Results: The DVT incidence among psychiatric inpatients was 1.6%. Predictive model performance, measured by the area under the curve (AUC), showed logistic regression (0.900), random forest (0.885), SVM (0.890), and XGBoost (0.889) performed well. Logistic regression and random forest models exhibited optimal overall performance, while XGBoost excelled in recall. Significant predictors of DVT included elevated D-dimer levels, age, Alzheimer’s disease, and Madopar use.
Conclusion: Psychiatric inpatients require vigilance for DVT risk, with factors like D-dimer levels and age serving as critical indicators. Machine learning models effectively predict DVT risk, enabling early detection and personalized prevention strategies in clinical practice.

Keywords: psychiatric inpatients, deep vein thrombosis, machine learning, risk factors, predictive modelling