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.

IF 3.4 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Clinical Epidemiology Pub Date : 2025-02-25 eCollection Date: 2025-01-01 DOI:10.2147/CLEP.S501062
Liang Xu, Miao Da
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引用次数: 0

Abstract

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.

应用机器学习技术研究精神科住院患者下肢深静脉血栓的发生率及危险因素:一项回顾性队列研究
背景:精神科住院患者由于其精神状况和药物治疗,其深静脉血栓形成(DVT)风险增加。然而,针对这一人群的研究仍然有限。方法:对湖州市第三市立医院住院精神病患者17434例进行统计分析,包括人口学统计、精神科诊断、躯体疾病、化验结果和用药情况。DVT的预测模型采用逻辑回归、随机森林、支持向量机(SVM)和XGBoost (Extreme Gradient Boosting)技术。使用随机森林模型评估特征重要性。结果:精神科住院患者DVT发生率为1.6%。以曲线下面积(AUC)衡量的预测模型性能显示,逻辑回归(0.900)、随机森林(0.885)、支持向量机(0.890)和XGBoost(0.889)表现良好。逻辑回归和随机森林模型表现出最佳的整体性能,而XGBoost在召回率方面表现出色。DVT的重要预测因素包括d -二聚体水平升高、年龄、阿尔茨海默病和美多巴的使用。结论:精神科住院患者需要警惕DVT风险,d -二聚体水平和年龄等因素是关键指标。机器学习模型可以有效地预测DVT风险,从而在临床实践中实现早期发现和个性化预防策略。
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来源期刊
Clinical Epidemiology
Clinical Epidemiology Medicine-Epidemiology
CiteScore
6.30
自引率
5.10%
发文量
169
审稿时长
16 weeks
期刊介绍: Clinical Epidemiology is an international, peer reviewed, open access journal. Clinical Epidemiology focuses on the application of epidemiological principles and questions relating to patients and clinical care in terms of prevention, diagnosis, prognosis, and treatment. Clinical Epidemiology welcomes papers covering these topics in form of original research and systematic reviews. Clinical Epidemiology has a special interest in international electronic medical patient records and other routine health care data, especially as applied to safety of medical interventions, clinical utility of diagnostic procedures, understanding short- and long-term clinical course of diseases, clinical epidemiological and biostatistical methods, and systematic reviews. When considering submission of a paper utilizing publicly-available data, authors should ensure that such studies add significantly to the body of knowledge and that they use appropriate validated methods for identifying health outcomes. The journal has launched special series describing existing data sources for clinical epidemiology, international health care systems and validation studies of algorithms based on databases and registries.
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