[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125953-en":3,"doc-seo-125953-105":31,"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125953,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine learning for prediction of chronic kidney disease progression - Validation of the Klinrisk model","Aim: validate the Klinrisk machine learning model to predict chronic kidney disease (CKD) progression in type 2 diabetes patients using pooled CANVAS/CREDENCE trial data. Materials and Methods: conduct external validation with CKD progression defined as ≥40% decline in eGFR or kidney failure; evaluate performance up to 3 years using AUC, Brier scores, and calibration plots, and compare with standard-of-care risk assessment via KDIGO eGFR and albumin-to-creatinine categories. Results: strong discrimination (AUC 0.81 at 1 year; 0.88 at 3 years) and improved performance versus KDIGO at all intervals. Conclusions: routine laboratory data enable accurate CKD progression prediction; model integration supports risk-based care in electronic records.","University of Groningen  \nMachine learning for prediction of chronic kidney disease progression  \nTangri, Navdeep; Ferguson, Thomas W. ; Bamforth, Ryan J. ; Leon, Silvia J. ; Arnott, Clare; Mahaffey, Kenneth W. ; Kotwal, Sradha; Heerspink, Hiddo J. L. ; Perkovic, Vlado; Fletcher, Robert A.  \nPublished in:  \nDiabetes, Obesity and Metabolism  \nDOI:  \n10.1111/dom.15678  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2024  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nTangri, N. , Ferguson, T. W. , Bamforth, R. J. , Leon, S. J. , Arnott, C. , Mahaffey, K. W. , Kotwal, S. , Heerspink, H. J. L. , Perkovic, V. , Fletcher, R. A. , & Neuen, B. L. (2024) . Machine learning for prediction of chronic kidney disease progression: Validation of the Klinrisk model in the CANVAS Program and CREDENCE  \ntrial. Diabetes, Obesity and Metabolism, 26(8), 3371-3380 . [https://doi.org/10.1111/dom.15678](https://doi.org/10.1111/dom.15678)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \nTake-down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 29-12-2025  \nReceived: 5 April 2024 Revised: 26 April 2024 Accepted: 4 May 2024  \nDOI: 10.1111/dom.15678  \nO R IG INA L ARTI CLE  \nMachine learning for prediction of chronic kidney disease progression: Validation of the Klinrisk model in the CANVAS Program and CREDENCE trial  \nNavdeep Tangri MD 1,2 | Thomas W. Ferguson MSc 1,2 | Ryan J. Bamforth MSc 1  | Silvia J. Leon MSc 1 | Clare Arnott MD 3,4  | Kenneth W. Mahaffey MD 5 | Sradha Kotwal MD 3,6 | Hiddo J. L. Heerspink MD 7  | Vlado Perkovic MD 3 | Robert A. Fletcher MSc 3 | Brendon L. Neuen MD 3,8   \n1Chronic Disease Innovation Centre, Seven Oaks General Hospital, Winnipeg, Canada 2Department of Medicine, University of Manitoba, Winnipeg, Canada  \n3The George Institute for Global Health, University of New South Wales, Sydney, Australia  \n4Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia 5Department of Medicine, Stanford University, Stanford, California, USA  \n6Department of Nephrology, Prince of Wales Hospital, Sydney, Australia  \n7Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, The Netherlands 8Department of Renal Medicine, Royal North Shore Hospital, Sydney, Australia  \nCorrespondence  \nNavdeep Tangri, MD, Chronic Disease Innovation Centre, Seven Oaks General Hospital, 2LB19-2300 McPhillips Street, Winnipeg, Manitoba R2V 3M3, Canada. Email: [ntangri@sogh.mb.ca](ntangri@sogh.mb.ca)  \nAbstract  \nAim: To validate the Klinrisk machine learning model for prediction of chronic kidney disease (CKD) progression i","cbCaisgOTa7IjijV","https://ap.wps.com/l/cbCaisgOTa7IjijV","pdf",1484276,4,1,11,"English","en",105,"# Abstract\n## Aim\n## Materials and Methods\n## Results\n## Conclusions\n# Introduction\n## CKD progression and SGLT2 inhibitors\n# Original Article","[{\"question\":\"What is the purpose of validating the Klinrisk model?\",\"answer\":\"To validate the Klinrisk machine learning model for predicting chronic kidney disease progression in patients with type 2 diabetes using pooled CANVAS/CREDENCE trial data.\"},{\"question\":\"How was CKD progression defined and how was model performance assessed?\",\"answer\":\"CKD progression was defined as a ≥40% decline in eGFR or kidney failure. Performance was assessed up to 3 years using AUC, Brier scores, and calibration plots, and compared with standard-of-care using KDIGO categories.\"},{\"question\":\"What were the key performance results for the Klinrisk model?\",\"answer\":\"The model achieved an AUC of 0.81 at 1 year and 0.88 at 3 years, with low Brier scores and improved performance versus the KDIGO heatmap at every interval.\"}]","Machine learning for prediction of chronic kidney disease progression - Validation of the Klinrisk model | PDF",1785902205,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-for-prediction-of-chronic-kidney-disease-progression-validation-of-the-klinrisk-model","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/machine-learning-for-prediction-of-chronic-kidney-disease-progression-validation-of-the-klinrisk-model/125953/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the purpose of validating the Klinrisk model?","Question",{"text":76,"@type":77},"To validate the Klinrisk machine learning model for predicting chronic kidney disease progression in patients with type 2 diabetes using pooled CANVAS/CREDENCE trial data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was CKD progression defined and how was model performance assessed?",{"text":81,"@type":77},"CKD progression was defined as a ≥40% decline in eGFR or kidney failure. Performance was assessed up to 3 years using AUC, Brier scores, and calibration plots, and compared with standard-of-care using KDIGO categories.",{"name":83,"@type":74,"acceptedAnswer":84},"What were the key performance results for the Klinrisk model?",{"text":85,"@type":77},"The model achieved an AUC of 0.81 at 1 year and 0.88 at 3 years, with low Brier scores and improved performance versus the KDIGO heatmap at every interval.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]