[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122242-en":3,"doc-seo-122242-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122242,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Effect of Machine Learning on Risk Stratification for Antiretroviral Treatment Failure in People Living with HIV","Despite the widespread use of antiretroviral therapy (ART), HIV virologic failure remains a major global public health challenge. This study develops and validates a nomogram-based scoring system to predict virologic failure incidence and determinants in people living with HIV (PLWH), supporting timely interventions and reducing unnecessary switches to second-line regimens. Data from 9879 patients were used to train, internally validate, and externally validate a model with logistic regression and LASSO-selected variables. Key predictors and performance metrics are reported.","Infection and Drug Resistance downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nInfection and Drug Resistance  \nOpen Access Full Text Article ORIGINAL RESEARCH  \nEffect of Machine Learning on Risk Stratification for Antiretroviral Treatment Failure in People Living with HIV  \nWenyuan Zhang 1 , *, Lehao Ren2 , *, Kai Yang 1 , *, Jisong Yan 3 , Qi Yu 1 , Shixuan Qi 1 , Huijing Ruan 1 , Dingyuan Zhao4 , Lianguo Ruan 1  \n1Department of Infectious Diseases, Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology; Hubei Clinical Research Center for Infectious Diseases; Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences; Joint Laboratory of Infectious Diseases and Health, Wuhan Institute of Virology and Wuhan Jinyintan Hospital, Chinese Academy of Sciences, Wuhan, Hubei, 430023, People’s Republic of China; 2Department of Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, People’s Republic of China; 3Department of Respiratory Diseases, Wuhan Jinyintan Hospital, Tongji Medical College of Huazhong University of Science and Technology; Hubei Clinical Research Center for Infectious Diseases; Wuhan Research Center for Communicable Disease Diagnosis and Treatment, Chinese Academy of Medical Sciences; Joint Laboratory of Infectious Diseases and Health, Wuhan Institute of Virology and Wuhan Jinyintan Hospital, Chinese Academy of Sciences, Wuhan, Hubei, 430023, People’s Republic of China; 4Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei, 430070, People’s Republic of China  \n*These authors contributed equally to this work  \nCorrespondence: Lianguo Ruan; Dingyuan Zhao, Email [2020jy0004@hust.edu.cn](2020jy0004@hust.edu.cn); [532648915@qq.com](532648915@qq.com)  \n\n| Objective: Despite the widespread use of antiretroviral therapy (ART), HIV virologic failure remains a significant global public health challenge. This study aims to develop and validate a nomogram-based scoring system to predict the incidence and determinants of virologic failure in people living with HIV (PLWH), facilitating timely interventions and reducing unnecessary transitions to second-line regimens.\u003Cbr>Methods: A total of 9879 patients with HIV/AIDS were included. The predictive model was developed using a training cohort (N = 5,189) and validated internally (N = 2,228) and externally (N = 2,462) with independent cohorts. Multivariable logistic regression, with variables selected through least absolute shrinkage and selection operator (LASSO) regression, was employed. The final model was presented as a nomogram and transformed into a user-friendly scoring system.\u003Cbr>Results: Key predictors in the scoring system included delayed ART initiation (6 points), poor adherence (7 points), ART discontinuation (6 points), side effects (9 points), CD4+ T cell count (10 points), and follow-up safety index (FSI) (9 points) . With a cutoff of 15.5 points, the area under the curve (AUC) for the training and validation sets was 0.807, 0.784, and 0.745, respectively. The scoring system demonstrated robust diagnostic performance across cohorts.\u003Cbr>Conclusion: This novel model provides an accurate, well-calibrated tool for predicting virologic failure at the individual level, offering valuable clinical utility in optimizing HIV management.\u003Cbr>Keywords: HIV, virologic failure, nomogram, predictive scoring system |\n| --- |\n| Introduction\u003Cbr>To date, acquired immune deficiency syndrome (AIDS) is still a global public health problem, representing a substantial peril with no available cure.1,2 According to the latest data released by the Joint United Nations Programme on HIV/ AIDS (UNAIDS) in late 2022, there were 1.3 million new HIV infections and 0.63 million deaths attributed to AIDSrelated illnesses worldwide.3 Fortunately, the introduction of antiret","cbCainxOAktUKtcU","https://ap.wps.com/l/cbCainxOAktUKtcU","pdf",926910,1,12,"English","en",105,"# Objective\n# Methods\n# Results\n# Conclusion\n# Keywords","[{\"question\":\"What is the goal of the study on machine learning and HIV treatment outcomes?\",\"answer\":\"To develop and validate a nomogram-based scoring system that predicts the incidence and determinants of HIV virologic failure in people living with HIV (PLWH), enabling earlier and more targeted clinical interventions.\"},{\"question\":\"How was the predictive model developed and validated?\",\"answer\":\"A total of 9879 patients were included, with a training cohort and both internal and external validation cohorts. Multivariable logistic regression was used, with variables selected via LASSO regression, and results were presented as a nomogram scoring system.\"},{\"question\":\"Which factors are included as key predictors in the scoring system?\",\"answer\":\"The scoring system highlights delayed ART initiation, poor adherence, ART discontinuation, side effects, CD4+ T cell count, and a follow-up safety index (FSI) as major predictors.\"}]","Effect of Machine Learning on Risk Stratification for Antiretroviral Treatment Failure in People Living with HIV | PDF",1785809610,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"effect-of-machine-learning-on-risk-stratification-for-antiretroviral-treatment-failure-in-people-living-with-hiv","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/effect-of-machine-learning-on-risk-stratification-for-antiretroviral-treatment-failure-in-people-living-with-hiv/122242/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the goal of the study on machine learning and HIV treatment outcomes?","Question",{"text":75,"@type":76},"To develop and validate a nomogram-based scoring system that predicts the incidence and determinants of HIV virologic failure in people living with HIV (PLWH), enabling earlier and more targeted clinical interventions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the predictive model developed and validated?",{"text":80,"@type":76},"A total of 9879 patients were included, with a training cohort and both internal and external validation cohorts. Multivariable logistic regression was used, with variables selected via LASSO regression, and results were presented as a nomogram scoring system.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors are included as key predictors in the scoring system?",{"text":84,"@type":76},"The scoring system highlights delayed ART initiation, poor adherence, ART discontinuation, side effects, CD4+ T cell count, and a follow-up safety index (FSI) as major predictors.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]