Incidence and Risk Factors of Lower Limb Deep Vein Thrombosis in Psychiatric Inpatients by Applying Machine Learning to Electronic Health Records - A Retrospective Cohort Study

Aug 5, 2026

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Psychiatric inpatients face elevated deep vein thrombosis (DVT) risk due to psychiatric conditions and long-term medication effects, yet evidence for this population remains limited. A retrospective cohort study analyzed 17,434 inpatients using demographic, diagnostic, clinical, laboratory, and medication data to train machine learning models (logistic regression, random forest, SVM, XGBoost). DVT incidence was 1.6%. Models achieved strong discrimination, with logistic regression and random forest providing best overall performance. Key predictors included higher D-dimer, age, Alzheimer’s disease, and Madopar use, supporting early detection and personalized prevention.

LanguageEnglish

CategoryResearch & Report

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Pages13

Reading Time33 min

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Background

Methods

Results

Conclusion

Clinical Epidemiology downloaded from [https://www.dovepress.com/](https://www.dovepress.com/) For personal use only. Clinical Epidemiology Open Access Full Text Article ORIGINAL RESEARCH Incidence and Risk Factors of Lower Limb Deep Vein Thrombosis in Psychiatric Inpatients by Applying Machine Learning to Electronic Health Records: A Retrospective Cohort Study 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](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 | | --- | | Introduction
Recent years have seen a steady rise in the number of psychiatric inpatients, a trend linked to the aging population and the increasing incidence of mental illness.1 While psychiatric inpatient care primarily addresses psychiatric and behavioral symptoms, the physical health concerns associated with these patients, particularly the risk of deep vein thrombosis (DVT), have often been overlooked. DVT is a common and potentially serious complication in hospitalized patients, especially those who are bedridden or have limited mobility. In addition to raising healthcare costs during hospitalization, DVT can lead to severe complications, such as pulmonary embolism.2
The thrombotic risk for psychiatric inpatients is often exacerbated by the nature of their condition. For instance, longterm use of antipsychotic medications can lead to metabolic syndrome, while suicidal and self-harming behaviors, along with overall reduced physical activity, further heighten the risk of thrombosis.3–5 Additionally, psychiatric patients frequently have comorbid somatic conditions such as hypertension and diabetes, which are also associated with an increased risk of thrombosis.6
Existing literature highlights that factors such as D-dimer levels, age, and thrombotic history are strongly associated with the occurrence of DVT. However, most studies have primarily examined general medical and surgical populations, | Received: 15 October 2024 Accepted: 11 January 2025 Published: 25 February 2025 Clinical Epidemiology 2025:17 197–209 197 © 2025 Xu and Da. This work is published and licensed by Dove Medical Pre

FAQs

What was the DVT incidence among psychiatric inpatients in this study?

The DVT incidence among psychiatric inpatients was 1.6%.

Which machine learning models were used to predict DVT risk?

The study used logistic regression, random forest, support vector machine (SVM), and XGBoost, and assessed feature importance using the random forest model.

What factors were identified as significant predictors of DVT?

Elevated D-dimer levels, age, Alzheimer’s disease, and Madopar use were significant predictors of DVT.

Aug 5, 2026

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