Jonathan Dash1, Thomas Verissimo2,3, Anna Faivre2,3, Lena Berchtold4, Thierry Berney5, Jérôme Pugin6, Sophie de Seigneux2,3,4, David Legouis2,3,6. 1. Division of Internal Medicine, Department of Medicine, University Hospitals of Geneva, 1205 Geneva, Switzerland. 2. Laboratory of Nephrology, Department of Medicine, University Hospitals of Geneva, 1205 Geneva, Switzerland. 3. Department of Cell Physiology, Faculty of Medicine, University of Geneva, 1205 Geneva, Switzerland. 4. Division of Nephrology, Department of Medicine, University Hospitals of Geneva, 1205 Geneva, Switzerland. 5. Division of Transplantation, Department of Surgery, University Hospitals of Geneva, 1205 Geneva, Switzerland. 6. Division of Intensive Care, Department of Acute Medicine, University Hospital of Geneva, 1205 Geneva, Switzerland.
Abstract
BACKGROUND: Rapid identification of patients at high risk for slow graft function (SGF) is of major importance in the immediate period following renal graft transplantation, both for early therapeutic decisions and long-term prognosis. Due to the high variability of serum creatinine levels after surgery, glomerular filtration rate (GFR) estimation is challenging. In this situation, kinetic estimated GFR (KeGFR) equations are interesting tools but have never been assessed for the identification of SGF patients. METHODS: We conducted a single-center retrospective cohort study, including all consecutive kidney allograft recipients in the University Hospitals of Geneva from 2008 to 2016. GFR was estimated using both CKD-EPI and KeGFR formulae. Their accuracies for SGF prediction were compared. Patients were followed up for one year after transplantation. RESULTS: A total of 326 kidney recipients were analyzed. SGF occurred in 76 (23%) patients. KeGFR estimation stabilized from the day following kidney transplantation, more rapidly than CKD-EPI. Discrimination ability for SGF prediction was better for KeGFR than CKD-EPI (AUC 0.82 and 0.66, p < 0.001, respectively). CONCLUSION: KeGFR computed from the first day after renal transplantation was able to predict SGF with good discrimination, outperforming CKD-EPI estimation. SGF patients had lower renal graft function overall at the one-year follow up.
BACKGROUND: Rapid identification of patients at high risk for slow graft function (SGF) is of major importance in the immediate period following renal graft transplantation, both for early therapeutic decisions and long-term prognosis. Due to the high variability of serum creatinine levels after surgery, glomerular filtration rate (GFR) estimation is challenging. In this situation, kinetic estimated GFR (KeGFR) equations are interesting tools but have never been assessed for the identification of SGF patients. METHODS: We conducted a single-center retrospective cohort study, including all consecutive kidney allograft recipients in the University Hospitals of Geneva from 2008 to 2016. GFR was estimated using both CKD-EPI and KeGFR formulae. Their accuracies for SGF prediction were compared. Patients were followed up for one year after transplantation. RESULTS: A total of 326 kidney recipients were analyzed. SGF occurred in 76 (23%) patients. KeGFR estimation stabilized from the day following kidney transplantation, more rapidly than CKD-EPI. Discrimination ability for SGF prediction was better for KeGFR than CKD-EPI (AUC 0.82 and 0.66, p < 0.001, respectively). CONCLUSION: KeGFR computed from the first day after renal transplantation was able to predict SGF with good discrimination, outperforming CKD-EPI estimation. SGF patients had lower renal graft function overall at the one-year follow up.
Authors: Robert R Redfield; Joseph R Scalea; Tiffany J Zens; Brenda Muth; Dixon B Kaufman; Arjang Djamali; Brad C Astor; Maha Mohamed Journal: Transpl Int Date: 2015-10-26 Impact factor: 3.782
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Authors: Enric Vilar; Mira Varagunam; Muhammad M Yaqoob; Martin Raftery; Raj Thuraisingham Journal: Transplantation Date: 2010-01-15 Impact factor: 4.939
Authors: Lesley A Stevens; Christopher H Schmid; Tom Greene; Liang Li; Gerald J Beck; Marshall M Joffe; Marc Froissart; John W Kusek; Yaping Lucy Zhang; Josef Coresh; Andrew S Levey Journal: Kidney Int Date: 2008-12-31 Impact factor: 10.612
Authors: Thomas Verissimo; Anna Faivre; Sebastian Sgardello; Maarten Naesens; Sophie de Seigneux; Gilles Criton; David Legouis Journal: Metabolites Date: 2022-01-10