ASN's Mission

ASN leads the fight to prevent, treat, and cure kidney diseases throughout the world by educating health professionals and scientists, advancing research and innovation, communicating new knowledge, and advocating for the highest quality care for patients.

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: TH-PO1124

External Validation of Predictive Score for Post-Transplantation Outcome in US Deceased Kidney Transplant Recipients

Session Information

Category: Transplantation

  • 1902 Transplantation: Clinical

Authors

  • Yazawa, Masahiko, University of Tennessee Health Science Center, Memphis, United States
  • Thomas, Fridtjof, University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Cseprekál, Orsolya, Semmelweis University, Budapest, Hungary
  • Kar, Suryatapa, University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Abdelaal, Ahmad, University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Talwar, Manish, University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Balaraman, Vasanthi, University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Kovesdy, Csaba P., University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Eason, James D., University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Molnar, Miklos Zsolt, University of Tennessee Health Science Center, Memphis, Tennessee, United States

Group or Team Name

  • METEOR (MEthodist Transplant EpidemiOlogy Research) Group
Background

We previously published a prediction model (www.transplantscore.com (TS)) for allograft and patient survival, which consisted of only predictors available at the time of kidney transplant (KT). Here, we aimed to perform external validation to assess the robustness, reliability, and applicability of our model.

Methods

Five hundred eleven patients who underwent first deceased KT in our Institute between 2010 to 2017 were included. We computed the original prediction score for these patients and compared the results with the observed outcome in terms of the score’s calibration (goodness of fit) and discrimination (AUC: Area Under the Curve). We also assessed the predictive performance in terms of re-classification (NRI: Net Reclassification Improvement) when compared with a binary classifier based on the EPTS raw score.

Results

In the entire cohort, the mean age was 51.2±11.8 years old, 83% were African-American, most of the patients were on hemodialysis (81%) before KT and mean time on dialysis was 5.4 years. The TS-predicted mortality probabilities clearly separate patients as demonstrated in the Kaplan-Meier curves using all available follow-up (Figure, panel A). The AUCs based on TS for 1- and 2-year mortality (panel B) were 0.737 and 0.682, respectively. These were higher than those for the classifier based on the EPTS score (AUC of 0.649 and 0.623 for 1 and 2 year mortality, respectively) and the NRI computed to 0.302 and 0.149 for 1 and 2 year classifications in favor of TS. However, the differences in the AUCs were not statistically significant (p = 0.138 and p = 0.149 for 1 and 2 year comparisons). The Hosmer and Lemeshow goodness of fit test of TS indicated some inadequate fitting (p = 0.015 and 0.038, respectively) apparently especially an overestimation for higher-score patients.

Conclusion

TS appears to broadly correctly classify patients with respect to their 1 and 2 year mortality rate.

Figure 1