Abstract
Background. Recorded at the time of transplant and reported to the Organ Procurement and Transplantation Network, patient's functional status is measured using the Karnofsky performance score (KPS), ranging 0 to 100. Functional status analysis may provide insights on candidate listing and posttransplant survival outcomes for deceased-donor kidney transplants. Methods. The cohort consisted of adult deceased-donor kidney transplant recipients transplanted beginning January 2007. One-year and 3-year Cox models for posttransplant survival were fitted with current Scientific Registry of Transplant Recipients (SRTR) variables and KPS. Comparative analyses were performed between the SRTR model without KPS and augmented model with it. Using the augmented model, we examined the impact of Kidney Donor Profile Index on posttransplant survivals for 5 different KPS strata: 10 to 30, 40 to 50, 60 to 70, 80 to 90, and 100. Results. Comparative analyses showed that KPS was a statistically significant predictor for posttransplant survival: it improved model calibration, discrimination, and predictive accuracy. From the augmented model, the survival curves illustrated that recipients with KPS 40 to 50 and kidneys with Kidney Donor Profile Index as high as 99 have expected survival probabilities of above 90% in 1 year and above 80% in 3 years. The expected survival probabilities improve as KPS increases. Recipients with KPS 10 to 30 have the worst survival probability, even if they received high-quality kidneys. Conclusions. Insights from the survival analyses recommend possible inclusion of functional status into SRTR's risk-adjusted models. Moreover, they invite further examination of its use to improve current listing and transplantation strategies at transplant centers and potentially reduce deceased-donor kidney discard rate.
Original language | English (US) |
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Pages (from-to) | 1051-1063 |
Number of pages | 13 |
Journal | Transplantation |
Volume | 103 |
Issue number | 5 |
DOIs | |
State | Published - May 1 2019 |
Funding
This research was supported in part through the computational resources and staff contributions provided for the Quest high performance computing facility at Northwestern University, which is jointly supported by the Office of the Provost, Office for Research, and Northwestern University Information Technology. The authors declare no conflicts of interest. This work is funded by National Institutes for Health award 1R21DK108104-01. Correspondence: Sanjay Mehrotra, PhD, Industrial Engineering and Management Sciences, 2145 Sheridan Rd, Tech C246, Evanston, IL 60208. ([email protected]). Supplemental digital content (SDC) is available for this article. Direct URL citations appear in the printed text, and links to the digital files are provided in the HTML text of this article on the journal’s Web site (www.transplantjournal.com).
ASJC Scopus subject areas
- Transplantation