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dc.contributor.authorJanowitz, Tobias
dc.contributor.authorWilliams, Edward H.
dc.contributor.authorMarshall, Andrea
dc.contributor.authorAinsworth, Nicola
dc.contributor.authorThomas, Peter B.
dc.contributor.authorSammut, Stephen J.
dc.contributor.authorShepherd, Scott
dc.contributor.authorWhite, Jeff
dc.contributor.authorMark, Patrick B.
dc.contributor.authorLynch, Andy G.
dc.contributor.authorJodrell, Duncan I.
dc.contributor.authorTavaré, Simon
dc.contributor.authorEarl, Helena
dc.date.accessioned2017-08-11T14:30:09Z
dc.date.available2017-08-11T14:30:09Z
dc.date.issued2017-08
dc.identifier.citationJanowitz , T , Williams , E H , Marshall , A , Ainsworth , N , Thomas , P B , Sammut , S J , Shepherd , S , White , J , Mark , P B , Lynch , A G , Jodrell , D I , Tavaré , S & Earl , H 2017 , ' New model for estimating glomerular filtration rate in patients with cancer ' Journal of Clinical Oncology , vol. 35 , no. 24 , pp. 2798-2805 . https://doi.org/10.1200/JCO.2017.72.7578en
dc.identifier.issn0732-183X
dc.identifier.otherPURE: 250731180
dc.identifier.otherPURE UUID: 70feb42e-6160-4f0e-a0d9-4885a9431c35
dc.identifier.otherRIS: urn:33EB7EB9D8B999228B23C9AB29936716
dc.identifier.otherPubMed: 28686534
dc.identifier.otherScopus: 85028510283
dc.identifier.otherORCID: /0000-0002-7876-7338/work/35946866
dc.identifier.urihttp://hdl.handle.net/10023/11432
dc.descriptionT.J. was supported by the Wellcome Trust Translational Medicine and Therapeutics Programme and the University of Cambridge, Department of Oncology (RJAG/076). H.E. was supported by the National Institute of Health Research Cambridge Biomedical Research Centre and the University of Cambridge.en
dc.description.abstractPurpose:  The glomerular filtration rate (GFR) is essential for carboplatin chemotherapy dosing; however, the best method to estimate GFR in patients with cancer is unknown. We identify the most accurate and least biased method. Methods:  We obtained data on age, sex, height, weight, serum creatinine concentrations, and results for GFR from chromium-51 (51Cr) EDTA excretion measurements (51Cr-EDTA GFR) from white patients ≥ 18 years of age with histologically confirmed cancer diagnoses at the Cambridge University Hospital NHS Trust, United Kingdom. We developed a new multivariable linear model for GFR using statistical regression analysis. 51Cr-EDTA GFR was compared with the estimated GFR (eGFR) from seven published models and our new model, using the statistics root-mean-squared-error (RMSE) and median residual and on an internal and external validation data set. We performed a comparison of carboplatin dosing accuracy on the basis of an absolute percentage error > 20%. Results:  Between August 2006 and January 2013, data from 2,471 patients were obtained. The new model improved the eGFR accuracy (RMSE, 15.00 mL/min; 95% CI, 14.12 to 16.00 mL/min) compared with all published models. Body surface area (BSA)?adjusted chronic kidney disease epidemiology (CKD-EPI) was the most accurate published model for eGFR (RMSE, 16.30 mL/min; 95% CI, 15.34 to 17.38 mL/min) for the internal validation set. Importantly, the new model reduced the fraction of patients with a carboplatin dose absolute percentage error > 20% to 14.17% in contrast to 18.62% for the BSA-adjusted CKD-EPI and 25.51% for the Cockcroft-Gault formula. The results were externally validated. Conclusion:   In a large data set from patients with cancer, BSA-adjusted CKD-EPI is the most accurate published model to predict GFR. The new model improves this estimation and may present a new standard of care.
dc.format.extent10
dc.language.isoeng
dc.relation.ispartofJournal of Clinical Oncologyen
dc.rights© 2017 by American Society of Clinical Oncology. Licensed under the Creative Commons Attribution 4.0 License: http://creativecommons.org/licenses/by/4.0/en
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectDASen
dc.subject.lccRC0254en
dc.titleNew model for estimating glomerular filtration rate in patients with canceren
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Medicineen
dc.contributor.institutionUniversity of St Andrews.Statisticsen
dc.contributor.institutionUniversity of St Andrews.Cellular Medicine Divisionen
dc.identifier.doihttps://doi.org/10.1200/JCO.2017.72.7578
dc.description.statusPeer revieweden


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