{"doi":"10.1093/ndt/gfac202","title":"Prediction of measured GFR after living kidney donation from pre-donation parameters","abstract":"BACKGROUND: 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. METHODS: In the prospective development cohort (TransplantLines, n = 511), 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 <90 mL/min/1.73 m2, a declining 5-year post-donation mGFR slope or age >65 years) and validated in internal (n = 509) and external (Mayo Clinic, n = 1087) cohorts. RESULTS: In the development cohort, pre-donation estimated GFR (eGFR) was 86 ± 14 mL/min/1.73 m2 and post-donation mGFR was 64 ± 11 mL/min/1.73 m2. Donors with a pre-donation eGFR ≥90 mL/min/1.73 m2 (present in 43%) had a mean post-donation mGFR of 69 ± 10 mL/min/1.73 m2 and 5% of these donors reached an mGFR <55 mL/min/1.73 m2. A model using pre-donation serum creatinine, age and sex performed optimally, predicting mGFR with good accuracy (mean bias 2.56 mL/min/1.73 m2, R2 = 0.29, root mean square error = 11.61) and precision [bias interquartile range (IQR) 14 mL/min/1.73 m2] in the external validation cohort. This model also performed well in donors with pre-donation eGFR <90 mL/min/1.73 m2 [bias 0.35 mL/min/1.73 m2 (IQR 10)], in donors with a negative post-donation mGFR slope [bias 4.75 mL/min/1.73 m2 (IQR 13)] and in donors >65 years of age [bias 0.003 mL/min/1.73 m2 (IQR 9)]. CONCLUSIONS: We developed a novel post-donation mGFR prediction model based on pre-donation serum creatinine, age and sex.","journal":"Nephrology Dialysis Transplantation","year":2022,"id":274103,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":10,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.945,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":942137,"name":"Jessica van der Weijden","orcid":"0000-0003-0247-7526","position":1,"is_corresponding":false},{"id":942138,"name":"Robert S. Niznik","orcid":"0000-0001-5691-1709","position":2,"is_corresponding":false},{"id":495411,"name":"Aidan F. Mullan","orcid":"0000-0001-8076-6935","position":3,"is_corresponding":false},{"id":21853,"name":"Stephan J. L. Bakker","orcid":"0000-0003-3356-6791","position":4,"is_corresponding":false},{"id":247121,"name":"Stefan P. Berger","orcid":"0000-0003-2228-4676","position":5,"is_corresponding":false},{"id":21814,"name":"Ilja M. Nolte","orcid":"0000-0001-5047-4077","position":6,"is_corresponding":false},{"id":614252,"name":"Jan‐Stephan Sanders","orcid":"0000-0002-0904-3969","position":7,"is_corresponding":false},{"id":303933,"name":"Gerjan Navis","orcid":"0000-0002-0616-0166","position":8,"is_corresponding":false},{"id":339336,"name":"Andrew D. Rule","orcid":"0000-0003-0338-7784","position":9,"is_corresponding":false},{"id":727249,"name":"Martin H. de Borst","orcid":"0000-0002-4127-8733","position":10,"is_corresponding":false},{"id":942136,"name":"Marco van Londen","orcid":"0000-0001-7145-3240","position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T00:28:07.776043Z","pmid":"35731584","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}