César Gálvez-Barrón, Sara Gamarra-Calvo, José Ramón Blanco Ramos, Isabel Sanjoaquín Conde, Carlos Pérez-López, Antonio Miñarro, Guillermo Verdejo-Muñoz
{"title":"抗逆转录病毒治疗HIV患者亚临床动脉粥样硬化检测的预测模型。","authors":"César Gálvez-Barrón, Sara Gamarra-Calvo, José Ramón Blanco Ramos, Isabel Sanjoaquín Conde, Carlos Pérez-López, Antonio Miñarro, Guillermo Verdejo-Muñoz","doi":"10.2174/011570162X373384250529110832","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Patients living with HIV (PLHIV) have a higher cardiovascular risk than others, which is why the early detection of atherosclerosis in this population is important. The present study reports predictive models of subclinical atherosclerosis for this population of patients, made up of variables that are easily collected in the clinic.</p><p><strong>Methods: </strong>The study design is a cross-sectional observational study. PLHIV without established cardiovascular disease were recruited for this study. Predictive models of subclinical atherosclerosis (Doppler ultrasound) were developed by testing sociodemographic variables, pathological history, data related to HIV infection, laboratory parameters, and capillaroscopy as potential predictors. Logistic regression with internal validation (bootstrapping) and machine learning techniques were used to develop the models.</p><p><strong>Results: </strong>Data from 96 HIV patients were analysed, 19 (19.8%) of whom had subclinical atherosclerosis. The predictors that went into both machine learning models and the regression model were hypertension, dyslipidaemia, protease inhibitors, triglycerides, fibrinogen, and alkaline phosphatase. Age and C-reactive protein were also part of the machine learning models. The logistic regression model had an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.84-0.99), which became 0.80 after internal validation by bootstrapping. The ma-chine learning techniques produced models with AUCs ranging from 0.73 to 0.86.</p><p><strong>Conclusion: </strong>We report predictive models for subclinical atherosclerosis in PLHIV, demonstrating relevant predictive performance based on easily accessible parameters, making them potentially useful as a screening tool. However, given the study's limitations-primarily the sample size-external validation in larger cohorts is warranted.</p>","PeriodicalId":10911,"journal":{"name":"Current HIV Research","volume":" ","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predictive Model for the Detection of Subclinical Atherosclerosis in HIV Patients on Antiretroviral Treatment.\",\"authors\":\"César Gálvez-Barrón, Sara Gamarra-Calvo, José Ramón Blanco Ramos, Isabel Sanjoaquín Conde, Carlos Pérez-López, Antonio Miñarro, Guillermo Verdejo-Muñoz\",\"doi\":\"10.2174/011570162X373384250529110832\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>Patients living with HIV (PLHIV) have a higher cardiovascular risk than others, which is why the early detection of atherosclerosis in this population is important. The present study reports predictive models of subclinical atherosclerosis for this population of patients, made up of variables that are easily collected in the clinic.</p><p><strong>Methods: </strong>The study design is a cross-sectional observational study. PLHIV without established cardiovascular disease were recruited for this study. Predictive models of subclinical atherosclerosis (Doppler ultrasound) were developed by testing sociodemographic variables, pathological history, data related to HIV infection, laboratory parameters, and capillaroscopy as potential predictors. Logistic regression with internal validation (bootstrapping) and machine learning techniques were used to develop the models.</p><p><strong>Results: </strong>Data from 96 HIV patients were analysed, 19 (19.8%) of whom had subclinical atherosclerosis. The predictors that went into both machine learning models and the regression model were hypertension, dyslipidaemia, protease inhibitors, triglycerides, fibrinogen, and alkaline phosphatase. Age and C-reactive protein were also part of the machine learning models. The logistic regression model had an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.84-0.99), which became 0.80 after internal validation by bootstrapping. The ma-chine learning techniques produced models with AUCs ranging from 0.73 to 0.86.</p><p><strong>Conclusion: </strong>We report predictive models for subclinical atherosclerosis in PLHIV, demonstrating relevant predictive performance based on easily accessible parameters, making them potentially useful as a screening tool. However, given the study's limitations-primarily the sample size-external validation in larger cohorts is warranted.</p>\",\"PeriodicalId\":10911,\"journal\":{\"name\":\"Current HIV Research\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2025-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current HIV Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2174/011570162X373384250529110832\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"IMMUNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current HIV Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/011570162X373384250529110832","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"IMMUNOLOGY","Score":null,"Total":0}
Predictive Model for the Detection of Subclinical Atherosclerosis in HIV Patients on Antiretroviral Treatment.
Objective: Patients living with HIV (PLHIV) have a higher cardiovascular risk than others, which is why the early detection of atherosclerosis in this population is important. The present study reports predictive models of subclinical atherosclerosis for this population of patients, made up of variables that are easily collected in the clinic.
Methods: The study design is a cross-sectional observational study. PLHIV without established cardiovascular disease were recruited for this study. Predictive models of subclinical atherosclerosis (Doppler ultrasound) were developed by testing sociodemographic variables, pathological history, data related to HIV infection, laboratory parameters, and capillaroscopy as potential predictors. Logistic regression with internal validation (bootstrapping) and machine learning techniques were used to develop the models.
Results: Data from 96 HIV patients were analysed, 19 (19.8%) of whom had subclinical atherosclerosis. The predictors that went into both machine learning models and the regression model were hypertension, dyslipidaemia, protease inhibitors, triglycerides, fibrinogen, and alkaline phosphatase. Age and C-reactive protein were also part of the machine learning models. The logistic regression model had an area under the receiver operating characteristic curve (AUC) of 0.91 (95% CI: 0.84-0.99), which became 0.80 after internal validation by bootstrapping. The ma-chine learning techniques produced models with AUCs ranging from 0.73 to 0.86.
Conclusion: We report predictive models for subclinical atherosclerosis in PLHIV, demonstrating relevant predictive performance based on easily accessible parameters, making them potentially useful as a screening tool. However, given the study's limitations-primarily the sample size-external validation in larger cohorts is warranted.
期刊介绍:
Current HIV Research covers all the latest and outstanding developments of HIV research by publishing original research, review articles and guest edited thematic issues. The novel pioneering work in the basic and clinical fields on all areas of HIV research covers: virus replication and gene expression, HIV assembly, virus-cell interaction, viral pathogenesis, epidemiology and transmission, anti-retroviral therapy and adherence, drug discovery, the latest developments in HIV/AIDS vaccines and animal models, mechanisms and interactions with AIDS related diseases, social and public health issues related to HIV disease, and prevention of viral infection. Periodically, the journal invites guest editors to devote an issue on a particular area of HIV research of great interest that increases our understanding of the virus and its complex interaction with the host.