[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122032-en":3,"doc-seo-122032-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},122032,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables - Research","Contrast agents used in radiological examinations are a major cause of acute kidney injury, driving the need for improved risk stratification tools. This study develops and validates machine-learning and clinical scoring models to predict contrast-induced nephropathy while addressing limitations of traditional approaches. A retrospective cohort of percutaneous coronary intervention cases is used to train a gradient boosting model and derive a simpler seven-variable model from variable importance. Performance is assessed with internal and external validations to support bedside decision-making.","doi: 10.2169/internalmedicine.1459-22  \nIntern Med 63: 773-780, 2024  \n[http://internmed.jp](http://internmed.jp)  \n【ORIGINAL ARTICLE】  \nApplicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization  \nVariables  \nHeejung Choi 1, Byungjin Choi 2, Sungdam Han 3, Minjeong Lee 1, Gyu-Tae Shin 1, Heungsoo Kim 1, Minkook Son 4, Kyung-Hee Kim 5, Joon-myoung Kwon 6-8, Rae Woong Park 2,9  \nand Inwhee Park 1  \nAbstract:  \nObjective Contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI) . We developed and validated a machine learning and clinical scoring prediction model to stratify the risk of contrast-induced nephropathy, considering the limitations of current classical and machine learning models.  \nMethods This retrospective study included 38,481 percutaneous coronary intervention cases from 23,703 patients in a tertiary hospital. We divided the cases into development and internal test sets (8:2) . Using the development set, we trained a gradient boosting machine prediction model (complex model) . We then developed a simple model using seven variables based on variable importance. We validated the performance of the models using an internal test set and tested them externally in two other hospitals.  \nResults The complex model had the best area under the receiver operating characteristic (AUROC) curve at 0.885 [95% confidence interval (CI) 0.876-0.894] in the internal test set and 0.837 (95% CI 0.819-0.854) and 0.850 (95% CI 0.781-0.918) in two different external validation sets. The simple model showed an AUROC of 0.795 (95% CI 0.781-0.808) in the internal test set and 0.766 (95% CI 0.744-0.789) and 0.782 (95% CI 0.687-0.877) in the two different external validation sets. This was higher than the value in the well-known scoring system (Mehran criteria, AUROC=0.67) . The seven precatheterization variables selected for the simple model were age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide. The simple model is available at [http://52.78.230.235:8081/](http://52.78.230.235:8081/)[ ](http://52.78.230.235:8081/)Conclusions We developed an AKI prediction machine learning model with reliable performance. This can aid in bedside clinical decision making.  \nKey words: acute kidney injury, contrast-induced nephropathy, percutaneous coronary intervention, machine learning, risk assessment, clinical decision making  \n(Intern Med 63: 773-780, 2024)  \n(DOI: 10.2169/internalmedicine.1459-22)  \n1 Department of Nephrology, Ajou University School of Medicine, Korea, 2 Department of Biomedical Informatics, Ajou University School of Medicine, Korea, 3 Malgundam Internal Medicine Clinic, Korea, 4 Department of Physiology, College of Medicine, Dong-A University, Korea, 5 Department of Cardiology, Cardiovascular Center, Incheon Sejong Hospital, Korea, 6 Department of Critical Care and Emergency Medicine, Incheon Sejong Hospital, Korea, 7 Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Korea, 8 Medical Research Team, Medical AI, Korea and 9 Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Korea  \nReceived: December 16, 2022; Accepted: July 2, 2023; Advance Publication by J-STAGE: August 9, 2023  \nCorrespondence to Dr. Inwhee Park, [inwhee@aumc.ac.kr](inwhee@aumc.ac.kr)  \nIntroduction  \nIodine contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI) in hospitals (1) . Contrast angiography is currently widely applied for diagnostic and therapeutic purposes, and its use is increasing. This has led to an increased risk of iatrogenic kidney dysfunction due to exposure to contrast agents, a condition known as contrast-induced nephropathy (CIN) . In a previous report, CIN accounted for 11% of hospital-acquired kidney insufficiency cases and was the third-most common cau","cbCailHv6LjtomuU","https://ap.wps.com/l/cbCailHv6LjtomuU","pdf",401732,1,"English","en",105,"# Abstract\n# Introduction\n## Background and clinical need\n## Limitations of existing scores and models\n# Methods\n## Study design and datasets\n## Model development and validation\n# Results\n## Predictive performance and selected variables\n## Comparison with Mehran criteria\n# Conclusions","[{\"question\":\"What was the main objective of this study?\",\"answer\":\"To develop and validate machine-learning and clinical scoring models that stratify the risk of contrast-induced nephropathy using pre-catheterization information.\"},{\"question\":\"How was the prediction model developed and tested?\",\"answer\":\"A retrospective dataset of PCI cases was split into development and internal test sets (8:2). A gradient boosting model was trained, then a simpler model using seven variables was derived and validated internally and externally in two other hospitals.\"},{\"question\":\"Which variables were used in the simple pre-catheterization model?\",\"answer\":\"Age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide.\"}]","Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables - Research | PDF",1785808409,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"applicable-machine-learning-model-for-predicting-contrast-induced-nephropathy-based-on-pre-catheterization-variables-research","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/applicable-machine-learning-model-for-predicting-contrast-induced-nephropathy-based-on-pre-catheterization-variables-research/122032/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What was the main objective of this study?","Question",{"text":74,"@type":75},"To develop and validate machine-learning and clinical scoring models that stratify the risk of contrast-induced nephropathy using pre-catheterization information.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How was the prediction model developed and tested?",{"text":79,"@type":75},"A retrospective dataset of PCI cases was split into development and internal test sets (8:2). A gradient boosting model was trained, then a simpler model using seven variables was derived and validated internally and externally in two other hospitals.",{"name":81,"@type":72,"acceptedAnswer":82},"Which variables were used in the simple pre-catheterization model?",{"text":83,"@type":75},"Age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]