ASN's Mission

To create a world without kidney diseases, the ASN Alliance for Kidney Health elevates care by educating and informing, driving breakthroughs and innovation, and advocating for policies that create transformative changes in kidney medicine throughout the world.

learn more

Contact ASN

1401 H St, NW, Ste 900, Washington, DC 20005

email@asn-online.org

202-640-4660

The Latest on Twitter

Kidney Week

Abstract: SA-PO801

Risk Prediction for Early Post-Donation Kidney Dysfunction in Live Kidney Donors Using a Common Data Model

Session Information

Category: Transplantation

  • 2002 Transplantation: Clinical

Authors

  • Park, Sehoon, Seoul National University Hospital, Jongno-gu, Seoul, Korea (the Republic of)
  • Kang, Eunjeong, Ewha Womans University Seoul Hospital, Seoul, Korea (the Republic of)
  • Kim, Sejoong, Seoul National University Bundang Hospital, Seongnam, Korea (the Republic of)
  • Joo, Kwon Wook, Seoul National University Hospital, Jongno-gu, Seoul, Korea (the Republic of)
  • Kim, Yon Su, Seoul National University Hospital, Jongno-gu, Seoul, Korea (the Republic of)
  • Lee, Hajeong, Seoul National University Hospital, Jongno-gu, Seoul, Korea (the Republic of)
Background

Determination of the prognosis of living kidney donors is important. Impairment of early post-donation kidney function is associated with a higher risk of long-term kidney failure. Therefore, a prediction model for early post-donation kidney function in living kidney donors is warranted.

Methods

Common Data Model was implemented for inclusion of uniform data from three tertiary hospitals in Korea. The development cohort consisted of living kidney donors at Seoul National University Hospital (N=1074), and the two validation cohorts consisted of 2595 and 189 living kidney donors from other hospitals in Korea. Logistic regression analysis was performed for insufficient kidney function recovery, comprising estimated glomerular filtration rates (eGFRs) <60 and <50 mL/min/1.73 m2 . Variables that were associated with most of these study outcomes were selected for construction of the final prediction model, a multivariable logistic regression model.

Results

Among the study cohorts, 10%-35% of living kidney donors had an eGFR <60 mL/min/1.73 m2 and 3%-9% had an eGFR <50 mL/min/1.73 m2 in the outcome-assessment period. Older age, male sex, higher body mass index, lower baseline eGFR, higher serum uric acid concentration, and the occurrence of postoperative acute kidney injury were included in the final prediction model. The model yielded acceptable discriminative power (AUC≥0.88) in both the development and validation cohorts. The calibration results were also acceptable (Hosmer-Lemeshow test, all P values but one were >0.05. A risk calculator for the four study outcomes was developed and published online (https://snuhnephrology.github.io/).

Conclusion

We constructed an easily accessible and useful risk prediction calculator for early post-donation kidney function.