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Evaluation of Renal Function in Patients Undergoing Lung Transplantation
C Carillo1, Y Pecoraro1, M Anile1
1Division of Thoracic Surgery, Department of General Surgery and Organ Transplant "PARIDE STEFANINI", Policlinico Umberto I, Sapienza University of Rome, Rome, Italy.
The MDRD and CKD-EPI equations better predict acute kidney injury after lung transplants than the CG equation. All three equations showed similar accuracy for predicting chronic kidney disease in lung recipients.
Area of Science:
- Nephrology
- Transplantation Surgery
- Pulmonology
Background:
- Acute kidney injury and chronic kidney failure are significant complications following lung transplantation.
- Glomerular filtration rate (GFR) is a key marker for assessing kidney function.
- Several equations exist to estimate GFR (eGFR), but their comparative accuracy in lung transplant recipients is unclear.
Purpose of the Study:
- To compare the predictive accuracy of three common eGFR equations (Cockcroft-Gault, MDRD, CKD-EPI) for acute and chronic kidney disease after lung transplantation.
- To identify the most reliable eGFR equation for preoperative risk assessment in lung transplant candidates.
Main Methods:
- Retrospective analysis of 22 lung transplant recipients (2010-2015).
- Preoperative eGFR was calculated using Cockcroft-Gault (CG), Modification of Diet in Renal Disease (MDRD), and CKD-EPI equations.
- eGFR was recalculated at 1, 3, and 6 months post-transplant for 20 patients.
Main Results:
- MDRD and CKD-EPI identified more patients with preoperative reduced eGFR (36.3%) compared to CG (27.2%).
- Five patients (22.7%) developed perioperative acute renal failure requiring dialysis; four had higher preoperative CG eGFR.
- CKD-EPI eGFR values demonstrated greater predictability for acute kidney injury (AKI).
Conclusions:
- Preoperative eGFR assessment using MDRD and CKD-EPI equations shows better correlation with acute renal failure prediction post-lung transplant compared to the CG equation.
- The three eGFR equations demonstrated comparable performance in predicting chronic kidney disease in this cohort.
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