{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T01:25:35Z","timestamp":1787189135145,"version":"build-2736575974"},"reference-count":34,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Nephrology"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>Develop and evaluate a chronic kidney disease (CKD) electronic (e\u2010) phenotype to identify and risk\u2010stratify CKD patients at scale using electronic health record (EHR) data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Patient encounter and laboratory data determining kidney function and proteinuria from a four\u2010year period (2021\u20132024) at a large Australian tertiary referral hospital was extracted from the hospital EHR. Following exclusion of kidney transplant recipients and patients on dialysis, eligible patients were classified as having CKD based on ICD\u201010 codes, decreased estimated glomerular filtration rate (eGFR, &lt;\u200960\u2009mL\/min per 1.73\u2009m\n                      <jats:sup>2<\/jats:sup>\n                      ) and\/or albuminuria (urine albumin\u2010to\u2010creatinine ratio [uACR]\u2009\u2265\u20093\u2009mg\/mmol, A2\/A3 equivalent) for a minimum of 3\u2009months. Patients with sufficient laboratory data were also staged according to the Kidney Disease Improving Global Outcomes (KDIGO) A\u2010by\u2010G grid.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      From a total undifferentiated hospital cohort of\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009342\u2009146, the CKD e\u2010phenotype algorithm identified\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200917\u2009908 likely CKD cases (5.2%). Algorithm\u2010detected CKD cases were validated against blinded manual chart review (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009200), revealing sensitivity and specificity for CKD by ICD\u201010 codes (0.85 and 0.83), eGFR (0.62 and 0.98) and proteinuria criteria (0.33 and 0.75). The proportion of patients that were ICD\u201010\u2010coded for CKD increased significantly (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) with increasing G\u2010stage (odds ratio [OR] 3.05) and A\u2010stage (OR 2.31).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Incorporating eGFR and proteinuria data into the e\u2010phenotype algorithm appears to identify novel CKD cases with high specificity, particularly in the earlier stages of CKD progression. However, overall sensitivity is limited when compared to ICD\u201010 CKD codes alone.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/nep.70248","type":"journal-article","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T14:34:18Z","timestamp":1784903658000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of a Chronic Kidney Disease e\u2010Phenotype: Identification and Characterisation by Electronic Health Record Data"],"prefix":"10.1111","volume":"31","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  Parkville Australia"},{"name":"Department of Medicine (RMH) University of Melbourne  Parkville 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