Fractional creatinine clearance of the donated kidney using Cockcroft-Gault formula as a predictor of graft function after living donor transplantation

Prediction of early graft function after living donor kidney transplantation by quantifying the "nephron mass" using CT-volumetric software

Early renal function after living-donor kidney transplantation (LDKT) depends on the "nephron mass" in the renal graft. In this study, as a possible donor-recipient size mismatch parameter that directly reflects the "nephron mass," the cortex to recipient weight ratio (CRWR) was calculated by CT-volumetric software, and its ability to predict early graft function was examined. One hundred patients who underwent LDKT were enrolled. Patients were classified into a developmental cohort (n = 79) and a validation cohort (n = 21). Using the developmental cohort, the correlation coefficients between size mismatch parameters, including CRWR, and the posttransplantation estimated glomerular filtration rate (eGFR) were calculated. Multiple regression analysis was conducted to define a formula to predict eGFR 1-month posttransplantation. Using the validation cohort, the validity of the formula was examined. The correlation coefficient was the highest for CRWR (1-month r = 0.66, p < 0.001). By multiple regression analysis, eGFR at 1-month was predicted using the linear model: 0.23 × donor preoperative eGFR + 17.03 × CRWR + 8.96 × preemptive transplantation + 5.10 (adjusted coefficient of determination = 0.54). In most patients in the validation cohort, the observed eGFR was within a 10 ml/min/1.73 m2 margin of the predicted eGFR. CRWR was the strongest parameter to predict early graft function. Predicting renal function using this formula could be useful in clinical application to select proper donors and to avoid unnecessary postoperative medical interventions.

An Improved Classification of Kidney Function Recovery Using Estimated Glomerular Filtration Rate Slope Post-transplantation

BACKGROUND

The impact of renal function recovery on graft survival was examined using estimated glomerular filtration rate (eGFR) slope after kidney transplantation (GAP classification); this was compared to the conventional classification of immediate graft function (IGF), slow graft function (SGF), and delayed graft function (DGF).

MATERIALS AND METHODS

Overall, 541 cases of cadaveric renal transplants were reviewed from a prospective transplant database. eGFR and its slope were measured using the harmonic mean over the first week post-transplantation. Next, 495 kidney transplant recipients from an independent institution were assessed to determine the prognostic value of graft function based on the eGFR slope.

RESULTS

The main discrimination of eGFR slopes occurred within the first 7 days. Three groups in the GAP classification (Good graft function, Average graft function, Poor graft function) were defined based on eGFR slope tertiles: good graft function (GGF), average graft function (AGF), and poor graft function (PGF) were defined based on the ΔCrCL per day over the first 7 days: <1 mL/min, 1-4 mL/min, and >4 mL/min, respectively. When applied to the validation cohort, the 5-year graft failure was 20% for the PGF group, 4% for the AGF group, and 3% for the GGF group. Multivariable Cox regression analysis demonstrated better prediction of long-term graft function with the new classification (C statistic 0.49 [old)] vs 0.61 [new]).

CONCLUSION

The new GAP criteria were better at predicting long-term graft survival and renal function compared to the conventional classification system, and deserve further consideration in future studies.

Applying genomics to organ transplantation medicine in both discovery and validation of biomarkers

The field of biomarker discovery made a significant leap over the past few decades. As we enter the Era of the Human Genome, thousands of biomarkers can be identified in a relatively high-throughput fashion. While such magnitude and diversity of biomarkers can be seen as a challenge by itself, the field is being moved forward by new advances in bioinformatics and Systems Biology. Because of the life and death nature of end stage organ failure that transplantation treats, the severe donor organ shortage, and the powerful and toxic drug therapies required for the lifetimes of transplant patients, we envision a future for biomarkers as tools to diagnose disease in its early stages, predict prognosis, suggest treatment options and then assist in the implementation of therapies. By harnessing the power of multiple technologies in parallel makes it possible to discover and then validate the next generation of biomarkers for transplantation. We see the road ahead diverge into two paths: one from biomarkers to diagnosis and therapy and the other to a new level of insight into the complex molecular networks that determine when a healthy state becomes diseased and dysfunctional.