{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T02:34:29Z","timestamp":1784946869137,"version":"3.55.0"},"reference-count":43,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T00:00:00Z","timestamp":1757635200000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Nephrology"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Chronic kidney disease (CKD) represents a significant and growing healthcare burden. As CKD is defined and staged using laboratory values, it can be readily identified and characterised via data points derived from the electronic health record (EHR). This narrative literature review describes various strategies that have been employed to develop such a CKD \u2018e\u2010phenotype,\u2019 evaluating accuracy, fidelity, and practicality. Methods discussed include the use of International Classification of Diseases (ICD) codes, estimated glomerular filtration rate (eGFR) and proteinuria criteria, free\u2010text analysis and natural language processing (NLP), and machine learning techniques. Considerable variability in algorithm performance and complexity exists, with the use of eGFR and proteinuria criteria likely constituting the most practical and reliable basis for a CKD e\u2010phenotype. In addition, promising current and future applications of the CKD e\u2010phenotype have been outlined, such as characterising the burden of CKD complications and comorbid disease, and use as a tool to encourage optimisation of CKD management with quality, guideline\u2010directed care. Future directions and challenges may involve integration of risk stratification and clinical decision support systems, alongside applications across public health resourcing and clinical trial recruitment.<\/jats:p>","DOI":"10.1111\/nep.70118","type":"journal-article","created":{"date-parts":[[2025,9,13]],"date-time":"2025-09-13T06:20:16Z","timestamp":1757744416000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Identifying and Characterising a Chronic Kidney Disease Electronic\u2010Phenotype Using Electronic Health Record\u2010Derived Data: A Narrative Review of Strategies and Applications"],"prefix":"10.1111","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1212-0973","authenticated-orcid":false,"given":"Christopher","family":"Sparks","sequence":"first","affiliation":[{"name":"Department of Nephrology The Royal Melbourne Hospital  Melbourne Victoria Australia"},{"name":"Department of Medicine 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