{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T07:21:45Z","timestamp":1792048905342,"version":"4.4.0"},"reference-count":24,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"2","funder":[{"DOI":"10.13039\/100001003","name":"Boehringer Ingelheim Pharmaceuticals","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100001003","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["JASN"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>The Klinrisk model predicted risk of 2-year CKD progression with area under the receiver operating characteristic curves of 0.80\u20130.87.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>When compared with standard of care, the Kidney Disease Improving Global Outcomes (KDIGO) heatmap-based staging system, the Klinrisk model achieved superior predictive capacity.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The Klinrisk machine learning model predicted CKD progression in US commercial, Medicare, and Medicaid insured adults.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Early identification of high-risk CKD can facilitate optimal medical management and improve outcomes. We aimed to validate the Klinrisk machine learning model for prediction of CKD progression in large US commercial, Medicare, and Medicaid populations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We developed three cohorts, consisting of insured adults enrolled in (\n                      <jats:italic toggle=\"yes\">1<\/jats:italic>\n                      ) commercial, (\n                      <jats:italic toggle=\"yes\">2<\/jats:italic>\n                      ) Medicare, and (\n                      <jats:italic toggle=\"yes\">3<\/jats:italic>\n                      ) Medicaid plans between January 1, 2007, and December 31, 2020, with \u22651 serum creatinine test, an eGFR between 15 and 180 ml\/min per 1.73 m\n                      <jats:sup>2<\/jats:sup>\n                      , and \u22657 of the 19 other laboratory analytes available. Two primary subcohorts were evaluated within each insurer: (\n                      <jats:italic toggle=\"yes\">1<\/jats:italic>\n                      ) all patients with \u22657 laboratory analytes and (\n                      <jats:italic toggle=\"yes\">2<\/jats:italic>\n                      ) patients in (\n                      <jats:italic toggle=\"yes\">1<\/jats:italic>\n                      ) with available urinalysis results. Disease progression was defined as the composite outcome of a sustained 40% decline in eGFR or kidney failure. Discrimination, accuracy, and calibration were assessed using the area under the receiver operating characteristic curve (AUC), Brier scores, and calibration plots.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the commercial cohort, the Klinrisk model achieved AUCs ranging from 0.83 (95% confidence interval, 0.82 to 0.83) to 0.87 (0.86 to 0.87) and a maximum Brier score of 0.005 (0.0005 to 0.005) at 2 years. In Medicare patients, AUCs ranged from 0.80 (0.79 to 0.80) to 0.81 (0.80 to 0.82), with a maximum Brier score of 0.026 (0.025 to 0.027). In Medicaid patients, we found AUCs ranging from 0.82 (0.82 to 0.82) to 0.84 (0.82 to 0.86) and a maximum Brier score of 0.014 (0.012 to 0.015).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The Klinrisk machine learning model was accurate in predicting CKD progression in 4.8 million US adults across commercial, Medicare, and Medicaid populations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1681\/asn.0000000817","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T16:21:07Z","timestamp":1754497267000},"page":"326-337","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":4,"title":["Validation of the Klinrisk Machine Learning Model for CKD Progression in a Large Representative US Population"],"prefix":"10.1681","volume":"37","author":[{"given":"Navdeep","family":"Tangri","sequence":"first","affiliation":[{"name":"Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada"},{"name":"Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada"},{"name":"Klinrisk Inc., Winnipeg, Manitoba, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas W.","family":"Ferguson","sequence":"additional","affiliation":[{"name":"Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada"},{"name":"Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada"},{"name":"Klinrisk Inc., Winnipeg, Manitoba, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chia-Chen","family":"Teng","sequence":"additional","affiliation":[{"name":"Carelon Research, Wilmington, Delaware"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8316-2325","authenticated-orcid":false,"given":"Ryan J.","family":"Bamforth","sequence":"additional","affiliation":[{"name":"Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada"},{"name":"Klinrisk Inc., Winnipeg, Manitoba, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0288-5538","authenticated-orcid":false,"given":"Joseph L.","family":"Smith","sequence":"additional","affiliation":[{"name":"Carelon Research, Wilmington, Delaware"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Guzman","sequence":"additional","affiliation":[{"name":"Carelon Research, Wilmington, Delaware"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8624-2830","authenticated-orcid":false,"given":"Ashley","family":"Goss","sequence":"additional","affiliation":[{"name":"Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, Connecticut"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2025,8,6]]},"reference":[{"issue":"8","key":"B3-20260203","doi-asserted-by":"crossref","first-page":"1838","DOI":"10.2215\/CJN.00730111","article-title":"Chronic kidney disease awareness among individuals with clinical markers of kidney dysfunction","volume":"6","author":"Tuot","year":"2011","journal-title":"Clin J Am Soc Nephrol."},{"issue":"1","key":"B4-20260203","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.kint.2020.10.012","article-title":"The case for early identification and intervention of chronic kidney disease: conclusions from a Kidney Disease: Improving Global 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