{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T14:44:41Z","timestamp":1786373081057,"version":"build-2736575974"},"reference-count":22,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"2","license":[{"start":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T00:00:00Z","timestamp":1759708800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"crossref","award":["R01DK-93770"],"award-info":[{"award-number":["R01DK-93770"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Edward S. Kraus Scholar Award"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["CJASN"],"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>This risk prediction model can enhance post-transplant care by identifying high-risk recipients for personalized interventions.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The predicted risk of 3-year graft failure is dynamically updated with each follow-up eGFR measurement.<\/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>The identification of kidney transplant recipients at high risk of graft failure enables timely interventions to improve outcomes. We developed and validated a risk prediction model that uses changes in eGFR to dynamically predict 3-year graft failure.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The risk prediction model was developed in a prospective multicenter cohort of deceased donor kidney transplant recipients (2010\u20132013) and validated in three independent cohorts (a registry cohort and two electronic medical record cohorts). The two-stage approach first estimated eGFR trends using a linear mixed-effects model and then used these trends in a logistic regression model to predict 3-year graft failure. eGFR was calculated using race-free equation, and graft failure was defined as the return to dialysis or retransplantation, censored for death. The model can be used at any time within the first 3 years after transplant, and the predicted risk is dynamically updated with each additional eGFR measurement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the development cohort (\n                      <jats:italic toggle=\"yes\">N<\/jats:italic>\n                      =1114), 94 (8%) experienced graft failure within 3 years of transplant. The model's predictive accuracy improved over time with the increase in available eGFR measurements. The optimism-corrected area under the ROC curve (AUC) was 0.70 (95% confidence interval, 0.63 to 0.77) at 3 months post-transplant and reached an AUC of 0.90 (95% confidence interval, 0.85 to 0.95) at 30 months post-transplant. Performance was attenuated in the validation cohorts (AUC range 0.60\u20130.64 at 3 months to 0.72\u20130.78 at 30 months), likely due to differences in data collection approaches in ascertaining eGFR.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This risk prediction model has the potential to enhance post-transplant care by identifying high-risk recipients who may benefit from closer monitoring and personalized interventions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Podcast<\/jats:title>\n                    <jats:p>This article contains a podcast at https:\/\/dts.podtrac.com\/redirect.mp3\/www.asn-online.org\/media\/podcast\/CJASN\/2026_02_18_CJASNFeb.21.2.21820.mp3<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2215\/cjn.0000000883","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T22:00:07Z","timestamp":1759788007000},"page":"306-314","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Risk Prediction of Graft Failure after Deceased Donor Kidney Transplant"],"prefix":"10.2215","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2067-0514","authenticated-orcid":false,"given":"Heather","family":"Thiessen Philbrook","sequence":"first","affiliation":[{"name":"Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9306-4705","authenticated-orcid":false,"given":"David G.","family":"Hu","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, 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