{"title":"基于多元 Logistic 回归模型探讨合并肺栓塞的下肢深静脉血栓患者的风险因素","authors":"Jiahong Zu, Tao Yang","doi":"10.1177/10760296241258230","DOIUrl":null,"url":null,"abstract":"<p><p>Valuable data on deep vein thrombosis (DVT) patients with coexisting pulmonary embolism (PE) is scarce. This study aimed to identify risk factors associated with these patients and develop logistic regression models to select high-risk DVT patients with coexisting PE. We retrospectively collected data on 150 DVT patients between July 15, 2022, and June 15, 2023, dividing them into groups based on the presence of coexisting PE. Univariate and multivariate logistic regression analyses were performed to identify significant risk factors and construct predictive models. Discrimination and calibration statistics evaluated the validation and accuracy of the developed models. Of the 130 patients analyzed, 40 (30.77%) had coexisting PE. Univariate analysis revealed four significant predictors of DVT patients with coexisting PE: sex (OR 3.83, 95% CI: [1.76; 8.59], <i>P</i> = 0.001), body mass index (BMI) (OR 1.50, 95% CI: [1.28; 1.75], <i>P</i> < 0.001), chronic disease (OR 5.15, 95% CI: [2.32; 11.8], <i>P</i> < 0.001), and high-density lipoprotein (HDL) (OR 0.03, 95% CI: [0.01; 0.20], <i>P</i> < 0.001). Additionally, BMI > 24 kg/m<sup>2</sup> (OR 9.70, 95% CI: [2.70; 67.5], <i>P</i> < 0.001) and BMI > 28 kg/m<sup>2</sup> (OR 4.80, 95% CI: [2.15; 11.0], <i>P</i> < 0.001) were associated with concurrent PE. Three multiple regression models were constructed, with areas under the receiver-operating characteristic curves of 0.925 (95% CI: [0.882; 0.968]), 0.908 (95% CI: [0.859; 0.957]), and 0.890 (95% CI: [0.836; 0.944]), respectively. Sex, BMI, chronic disease, and HDL levels are significant predictors of DVT patients with coexisting PE.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11131404/pdf/","citationCount":"0","resultStr":"{\"title\":\"Exploring Risk Factors for Lower Extremity Deep Vein Thrombosis Patients with Co-existing Pulmonary Embolism Based on Multiple Logistic Regression Model.\",\"authors\":\"Jiahong Zu, Tao Yang\",\"doi\":\"10.1177/10760296241258230\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Valuable data on deep vein thrombosis (DVT) patients with coexisting pulmonary embolism (PE) is scarce. This study aimed to identify risk factors associated with these patients and develop logistic regression models to select high-risk DVT patients with coexisting PE. We retrospectively collected data on 150 DVT patients between July 15, 2022, and June 15, 2023, dividing them into groups based on the presence of coexisting PE. Univariate and multivariate logistic regression analyses were performed to identify significant risk factors and construct predictive models. Discrimination and calibration statistics evaluated the validation and accuracy of the developed models. Of the 130 patients analyzed, 40 (30.77%) had coexisting PE. Univariate analysis revealed four significant predictors of DVT patients with coexisting PE: sex (OR 3.83, 95% CI: [1.76; 8.59], <i>P</i> = 0.001), body mass index (BMI) (OR 1.50, 95% CI: [1.28; 1.75], <i>P</i> < 0.001), chronic disease (OR 5.15, 95% CI: [2.32; 11.8], <i>P</i> < 0.001), and high-density lipoprotein (HDL) (OR 0.03, 95% CI: [0.01; 0.20], <i>P</i> < 0.001). Additionally, BMI > 24 kg/m<sup>2</sup> (OR 9.70, 95% CI: [2.70; 67.5], <i>P</i> < 0.001) and BMI > 28 kg/m<sup>2</sup> (OR 4.80, 95% CI: [2.15; 11.0], <i>P</i> < 0.001) were associated with concurrent PE. Three multiple regression models were constructed, with areas under the receiver-operating characteristic curves of 0.925 (95% CI: [0.882; 0.968]), 0.908 (95% CI: [0.859; 0.957]), and 0.890 (95% CI: [0.836; 0.944]), respectively. Sex, BMI, chronic disease, and HDL levels are significant predictors of DVT patients with coexisting PE.</p>\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11131404/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/10760296241258230\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/10760296241258230","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
摘要
关于合并肺栓塞(PE)的深静脉血栓形成(DVT)患者的宝贵数据非常稀少。本研究旨在确定与这些患者相关的风险因素,并建立逻辑回归模型来选择合并肺栓塞的高风险深静脉血栓患者。我们回顾性地收集了2022年7月15日至2023年6月15日期间150名深静脉血栓患者的数据,并根据是否合并有PE将其分为几组。通过单变量和多变量逻辑回归分析来确定重要的风险因素并构建预测模型。判别和校准统计评估了所建模型的验证性和准确性。在分析的 130 名患者中,有 40 人(30.77%)合并有 PE。单变量分析显示,以下四个因素对合并 PE 的深静脉血栓患者有显著的预测作用:性别(OR 3.83,95% CI:[1.76; 8.59],P = 0.001)、体重指数(BMI)(OR 1.50,95% CI:[1.28; 1.75],P P P 24 kg/m2(OR 9.70,95% CI:[2.70; 67.5],P 28 kg/m2(OR 4.80,95% CI:[2.15; 11.0],P
Exploring Risk Factors for Lower Extremity Deep Vein Thrombosis Patients with Co-existing Pulmonary Embolism Based on Multiple Logistic Regression Model.
Valuable data on deep vein thrombosis (DVT) patients with coexisting pulmonary embolism (PE) is scarce. This study aimed to identify risk factors associated with these patients and develop logistic regression models to select high-risk DVT patients with coexisting PE. We retrospectively collected data on 150 DVT patients between July 15, 2022, and June 15, 2023, dividing them into groups based on the presence of coexisting PE. Univariate and multivariate logistic regression analyses were performed to identify significant risk factors and construct predictive models. Discrimination and calibration statistics evaluated the validation and accuracy of the developed models. Of the 130 patients analyzed, 40 (30.77%) had coexisting PE. Univariate analysis revealed four significant predictors of DVT patients with coexisting PE: sex (OR 3.83, 95% CI: [1.76; 8.59], P = 0.001), body mass index (BMI) (OR 1.50, 95% CI: [1.28; 1.75], P < 0.001), chronic disease (OR 5.15, 95% CI: [2.32; 11.8], P < 0.001), and high-density lipoprotein (HDL) (OR 0.03, 95% CI: [0.01; 0.20], P < 0.001). Additionally, BMI > 24 kg/m2 (OR 9.70, 95% CI: [2.70; 67.5], P < 0.001) and BMI > 28 kg/m2 (OR 4.80, 95% CI: [2.15; 11.0], P < 0.001) were associated with concurrent PE. Three multiple regression models were constructed, with areas under the receiver-operating characteristic curves of 0.925 (95% CI: [0.882; 0.968]), 0.908 (95% CI: [0.859; 0.957]), and 0.890 (95% CI: [0.836; 0.944]), respectively. Sex, BMI, chronic disease, and HDL levels are significant predictors of DVT patients with coexisting PE.