Development and validation of a new prediction model for graft function using preoperative marginal factors in living-donor kidney transplantation.

BACKGROUND

Recently, living-donor kidney transplantation from marginal donors has been increasing. However, a simple prediction model for graft function including preoperative marginal factors is limited. Here, we developed and validated a new prediction model for graft function using preoperative marginal factors in living-donor kidney transplantation.

METHODS

We retrospectively investigated 343 patients who underwent living-donor kidney transplantation at Kyushu University Hospital (derivation cohort). Low graft function was defined as an estimated glomerular filtration rate of < 45 mL/min/1.73 m at 1 year. A prediction model was developed using a multivariable logistic regression model, and verified using data from 232 patients who underwent living-donor kidney transplantation at Tokyo Women's Medical University Hospital (validation cohort).

RESULTS

In the derivation cohort, 89 patients (25.9%) had low graft function at 1 year. Donor age, donor-estimated glomerular filtration rate, donor hypertension, and donor/recipient body weight ratio were selected as predictive factors. This model demonstrated modest discrimination (c-statistic = 0.77) and calibration (Hosmer-Lemeshow test, P = 0.83). Furthermore, this model demonstrated good discrimination (c-statistic = 0.76) and calibration (Hosmer-Lemeshow test, P = 0.54) in the validation cohort. Furthermore, donor age, donor-estimated glomerular filtration rate, and donor hypertension were strongly associated with glomerulosclerosis and atherosclerotic vascular changes in the "zero-time" biopsy.

CONCLUSIONS

This model using four pre-operative variables will be a simple, but useful guide to estimate graft function at 1 year after kidney transplantation, especially in marginal donors, in the clinical setting.