{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T23:27:44Z","timestamp":1789082864729,"version":"build-2803163510"},"reference-count":46,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T00:00:00Z","timestamp":1736121600000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Nephrology"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Chronic kidney disease is characterised by the progressive loss of kidney function. However, predicting who will progress to kidney failure is difficult. Artificial Intelligence, including Machine Learning, shows promise in this area. This narrative review highlights the most common and important variables used in machine learning models to predict progressive chronic kidney disease. Ovid Medline and EMBASE were searched in August 2023 with keywords relating to \u2018chronic kidney disease\u2019, \u2018machine learning\u2019, and \u2018end\u2010stage renal disease\u2019. Studies were assessed against inclusion and exclusion criteria and excluded if variables inputted into machine learning models were not discussed. Data extraction focused on specific variables inputted into the machine learning models. After screening of 595 articles, 16 were included in the review. The most utilised machine learning models were random forest, support vector machines and XGBoost. The most commonly occurring variables were age, gender, measures of renal function, measures of proteinuria, and full blood examination. Only half of all studies included clinical variables in their models. The most important variables overall were measures of renal function, measures of proteinuria, age, full blood examination and serum albumin. Machine learning was consistently superior or non\u2010inferior when compared to the Kidney Failure Risk Equation. This review identified key variables used in machine learning models to predict chronic kidney disease progression to kidney failure. These findings lay the foundations for the development of future machine learning models capable of rivalling the Kidney Failure Risk Equation in the provision of accurate kidney failure prediction.<\/jats:p>","DOI":"10.1111\/nep.14424","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T01:23:08Z","timestamp":1736212988000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Artificial Intelligence to Predict Chronic Kidney Disease Progression to Kidney Failure: A Narrative Review"],"prefix":"10.1111","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6340-1037","authenticated-orcid":false,"given":"Zane A.","family":"Miller","sequence":"first","affiliation":[{"name":"Faculty of Medicine, Dentistry &amp; Health Sciences Melbourne The University of Melbourne  Melbourne Victoria Australia"},{"name":"Epworth HealthCare Medicine  Richmond Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen","family":"Dwyer","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Dentistry &amp; Health Sciences Melbourne The University of Melbourne  Melbourne Victoria Australia"},{"name":"The Royal Melbourne Hospital Nephrology  Parkville Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,1,6]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/sj.ki.5002343"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.kisu.2021.11.003"},{"issue":"10","key":"e_1_2_9_4_1","first-page":"S163","article-title":"All\u2010Cause Costs Increase Exponentially With Increased Chronic Kidney Disease Stage","volume":"23","author":"Golestaneh L.","year":"2017","journal-title":"American Journal of Managed Care"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1071\/AH22291"},{"key":"e_1_2_9_6_1","volume-title":"Chronic Kidney Disease: Australian Facts","author":"Health AIo, Welfare","year":"2023"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.5694\/mja2.51361"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjmed.2011.04.026"},{"key":"e_1_2_9_9_1","volume-title":"Chronic Kidney Disease (CKD) Management in Primary Care","author":"Australia K. 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