{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T02:41:32Z","timestamp":1790217692675,"version":"4.1.0"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T00:00:00Z","timestamp":1655856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Dutch Organization for Scientific Research","award":["016.146.014"],"award-info":[{"award-number":["016.146.014"]}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R01 DK090358"],"award-info":[{"award-number":["R01 DK090358"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,23]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>One of the challenges in living kidney donor screening is to estimate remaining kidney function after donation. Here we developed a new model to predict post-donation measured glomerular filtration rate (mGFR) from pre-donation serum creatinine, age and sex.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>In the prospective development cohort (TransplantLines, n\u00a0=\u00a0511), several prediction models were constructed and tested for accuracy, precision and predictive capacity for short- and long-term post-donation 125I-iothalamate mGFR. The model with optimal performance was further tested in specific high-risk subgroups (pre-donation eGFR\u00a0&amp;lt;90\u00a0mL\/min\/1.73\u00a0m2, a declining 5-year post-donation mGFR slope or age\u00a0&amp;gt;65\u00a0years) and validated in internal (n\u00a0=\u00a0509) and external (Mayo Clinic, n\u00a0=\u00a01087) cohorts.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In the development cohort, pre-donation estimated GFR (eGFR) was 86\u00a0\u00b1\u00a014\u00a0mL\/min\/1.73\u00a0m2 and post-donation mGFR was 64\u00a0\u00b1\u00a011\u00a0mL\/min\/1.73\u00a0m2. Donors with a pre-donation eGFR\u00a0\u226590\u00a0mL\/min\/1.73\u00a0m2 (present in 43%) had a mean post-donation mGFR of 69\u00a0\u00b1\u00a010\u00a0mL\/min\/1.73\u00a0m2 and 5% of these donors reached an mGFR\u00a0&amp;lt;55\u00a0mL\/min\/1.73\u00a0m2. A model using pre-donation serum creatinine, age and sex performed optimally, predicting mGFR with good accuracy (mean bias 2.56\u00a0mL\/min\/1.73\u00a0m2, R2\u00a0=\u00a00.29, root mean square error\u00a0=\u00a011.61) and precision [bias interquartile range (IQR) 14\u00a0mL\/min\/1.73\u00a0m2] in the external validation cohort. This model also performed well in donors with pre-donation eGFR\u00a0&amp;lt;90\u00a0mL\/min\/1.73\u00a0m2 [bias 0.35\u00a0mL\/min\/1.73\u00a0m2 (IQR 10)], in donors with a negative post-donation mGFR slope [bias 4.75\u00a0mL\/min\/1.73\u00a0m2 (IQR 13)] and in donors\u00a0&amp;gt;65\u00a0years of age [bias 0.003\u00a0mL\/min\/1.73\u00a0m2 (IQR 9)].<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>We developed a novel post-donation mGFR prediction model based on pre-donation serum creatinine, age and sex.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ndt\/gfac202","type":"journal-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T15:57:01Z","timestamp":1655913421000},"page":"212-221","source":"Crossref","is-referenced-by-count":11,"title":["Prediction of measured GFR after living kidney donation from pre-donation parameters"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7145-3240","authenticated-orcid":false,"given":"Marco","family":"van Londen","sequence":"first","affiliation":[{"name":"Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen and University of Groningen , Groningen , The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0247-7526","authenticated-orcid":false,"given":"Jessica","family":"van der Weijden","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen and University of Groningen , Groningen , The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert S","family":"Niznik","sequence":"additional","affiliation":[{"name":"Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic , Rochester, MN , 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