[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126260-en":3,"doc-seo-126260-105":30,"detail-sidebar-cat-0-en-105":92},{"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":11,"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},126260,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Machine learning-based prediction of carotid intima–media thickness progression - a three-year prospective cohort study","Machine learning models are developed and validated to predict carotid intima–media thickness (CIMT) progression using routine clinical biomarkers. Using a three-year prospective cohort of 904 participants with three consecutive annual CIMT measurements, outcomes are defined by CIMT thickening or nonthickening. Seven algorithms are compared with discrimination, calibration (Platt scaling), and decision curve analysis. Elastic net shows the best performance and supports three-tier risk stratification with improved calibration and clinical net benefit across thresholds.","OPEN ACCESS  \nEDITED BY  \nTaminul Islam,  \nSouthern Illinois University Carbondale, United States  \nREVIEWED BY  \nGang Ye,  \nSichuan Agricultural University, China Qiaoqiao Xu,  \nThird Affiliated Hospital of Anhui Medical University, China  \n*CORRESPONDENCE  \nJiangang Wang  \n [395896584@qq.com](395896584@qq.com)[ ](395896584@qq.com)Wei-Dong Li  \n [liweidong98@tmu.edu.cn](liweidong98@tmu.edu.cn)  \n†These authors have contributed equally to this work  \nRECEIVED 14 March 2025  \nACCEPTED 28 May 2025  \nPUBLISHED 12 June 2025  \nCITATION  \nZhou A, Chen K, Wei Y, Ye Q, Xiao Y, Shi R, Wang J and Li W-D (2025) Machine learning-based prediction of carotid intima– media thickness progression: a three-year prospective cohort study.  \nFront. Med. 12:1593662 .  \ndoi: 10.3389/fmed.2025.1593662  \nCOPYRIGHT  \n© 2025 Zhou, Chen, Wei, Ye, Xiao, Shi, Wang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTYPE Original Research PUBLISHED 12 June 2025  \nDOI 10.3389/fmed.2025.1593662  \nMachine learning-based prediction of carotid intima– media thickness progression: a three-year prospective cohort study  \nAn Zhou 1†, Kui Chen 2,3†, Yonghui Wei 1†, Qu Ye 1,4†, Yuanming Xiao 2†, Rong Shi 1, Jiangang Wang 2* and Wei-Dong Li 1*  \n1 Department of Genetics, College of Basic Medical Sciences, Tianjin Medical University, Tianjin, China, 2 Health Management Medical Center, Third Xiangya Hospital, Central South University, Changsha, China, 3State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, China, 4 Department of Clinical Laboratory, Peking University First Hospital, Beijing, China  \nBackground: Early detection of subclinical atherosclerosis progression is crucial for preventing atherosclerotic cardiovascular disease (ASCVD) . Carotid intima– media thickness (CIMT) is a recognized surrogate marker for atherosclerosis, but accurate prediction of its progression remains challenging. This study aimed to develop and validate machine learning models for predicting CIMT progression via routine clinical biomarkers.  \nMethods: In this three-year prospective cohort study, we analyzed data from 904 participants from the Third Xiangya Hospital of Central South University Health Examination Cohort who underwent three consecutive annual CIMT measurements. The participants were categorized into CIMT thickening and nonthickening groups on the basis of a final CIMT ≥1.0 mm or an increase ≥0.1 mm across consecutive measurements. We evaluated seven machine learning algorithms: logistic regression, random forest, XGBoost, support vector machine (SVM), elastic net, decision tree, and neural network. Model performance was assessed through discrimination (AUC, sensitivity, specificity) and calibration metrics, with Platt scaling applied to optimize probability estimates. Clinical utility was evaluated through decision curve analysis.  \nResults: Compared with the more complex algorithms, the elastic net model demonstrated superior performance (AUC 0.754) . Baseline CIMT, absolute monocyte count, sex, age, and LDL-C were identified as the most influential predictors. After Platt scaling, the calibration improved significantly across all the models. Decision curve analysis revealed a positive net benefit across a wide threshold range (0 .01–0. 5) . On the basis of calibrated probabilities, we developed a three-tier risk stratification framework that identified distinct groups with progressively higher event rates: medium-risk (13 .9%), high-risk (50 .0%), and very-high-risk (60 .0%) . Subgroup a","cbCaidV1iA57jTku","https://ap.wps.com/l/cbCaidV1iA57jTku","pdf",7757063,1,17,"English","en",105,"# Background\n## CIMT as a surrogate marker for atherosclerosis\n## Limitations in current CIMT clinical application\n# Methods\n## Three-year prospective cohort and CIMT measurement protocol\n## Outcome definition for progression\n## Machine learning models and evaluation metrics\n## Calibration and clinical utility assessment\n# Results\n## Model comparison and key predictors\n## Calibration improvements after Platt scaling\n## Decision curve analysis and risk stratification\n## Subgroup findings\n# Conclusion\n## Clinical value of machine learning for CIMT progression prediction","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To develop and validate machine learning models that predict CIMT progression using routine clinical biomarkers in a three-year prospective cohort.\"},{\"question\":\"How is CIMT progression defined in the study?\",\"answer\":\"Participants are grouped as CIMT thickening or nonthickening based on a final CIMT threshold (≥1.0 mm) or an increase of at least 0.1 mm across consecutive measurements.\"},{\"question\":\"Which model performed best and what predictors were most influential?\",\"answer\":\"The elastic net model shows superior performance (AUC 0.754), and baseline CIMT, absolute monocyte count, sex, age, and LDL-C are identified as the most influential predictors.\"}]","Machine learning-based prediction of carotid intima–media thickness progression - a three-year prospective cohort study | PDF",1785904100,43,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-based-prediction-of-carotid-intimamedia-thickness-progression-a-three-year-prospective-cohort-study","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-based-prediction-of-carotid-intimamedia-thickness-progression-a-three-year-prospective-cohort-study/126260/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of this study?","Question",{"text":76,"@type":77},"To develop and validate machine learning models that predict CIMT progression using routine clinical biomarkers in a three-year prospective cohort.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is CIMT progression defined in the study?",{"text":81,"@type":77},"Participants are grouped as CIMT thickening or nonthickening based on a final CIMT threshold (≥1.0 mm) or an increase of at least 0.1 mm across consecutive measurements.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model performed best and what predictors were most influential?",{"text":85,"@type":77},"The elastic net model shows superior performance (AUC 0.754), and baseline CIMT, absolute monocyte count, sex, age, and LDL-C are identified as the most influential predictors.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]