The role of the economic environment in kidney transplant outcomes.

The relationship between global economic indicators and kidney allograft and patient survival is unknown. To investigate possible relationships between the two, we analyzed kidney transplant recipients receiving transplants between January of 1995 and December of 2002 (n = 105,181) in the USA using Cox regression models. We found that: The Dow Jones Industrial Average had a negative association with outcome at one year post-transplant (HR 1.03 and 1.06, p < 0.001 for graft and recipient survival, respectively) but changed to a protective effect in the late period (HR 0.77, p < 0.001, and HR 0.83, p < 0.001 for graft and recipient survival, respectively, five yr after transplantation). Unemployment rate had a protective effect at the time of transplantation (HR 0.97, p < 0.005) and at one year after transplantation (HR 0.95, p < 0.005) but changed to the opposite in the late period at the fifth post-transplant year (HR 1.35, p < 0.001, and HR 1.20, p < 0.001, for graft and recipient survival respectively). The Consumer Price Index measured at different post-transplant time points seems to have had a protective effect on the graft (HR 0.77, p < 0.001 at five yr) and recipient (HR 0.83, p < 0.001 at five yr) survival. Beyond three yr after transplantation, when some of the recipients lose Medicare benefits, economic downturns might have a negative association with the kidney graft and recipient survival.

The impact of employment status on recipient and renal allograft survival.

BACKGROUND: With the improved median survival of kidney transplant recipients, there has been an increased focus on quality of life after transplantation. Employment is a widely recognized component of quality of life. To date, no study has demonstrated a link between post-transplant employment status and recipient and allograft survival after transplant. METHODS: The records from the United States Renal Data System (USRDS) and the United Network for Organ Sharing (UNOS) from January 1, 1995, through December 31, 2002, were examined in this retrospective study. Two outcomes, allograft survival time (time between the transplantation and allograft failure or censor) and recipient survival time (time between the transplantation and recipient death or censor), were analyzed using Cox models adjusted for potential confounding factors. RESULTS: Compared to patients working full time at the time of transplantation, those not working by choice have a greater risk to graft [hazard ratio (HR) 1.27, p < 0.001] but not to recipient survival. A similar trend was observed in patients not working at 12 months post-transplant (HR 1.30, p < 0.001 for graft survival but not for recipient survival). However, at five-yr post-transplant not working by choice was protective to the graft (HR 0.47, p < 0.01) as compared to working full time. Results of the analysis in the patient subgroups based on the comorbidities and the overall health status were similar. CONCLUSION: Employment status at the time of transplantation and in post-transplant period has a strong and independent association with the graft and recipient survival. Full time employment at the time of transplant and at one-yr post-transplant is associated with lower risk for graft failure and recipient mortality. However, working beyond the time covered by Medicare might be associated with potential risk for graft survival.

Predicting kidney transplant survival using tree-based modeling.

Predicting the outcome of kidney transplantation is clinically important and computationally challenging. The goal of this project was to develop the models predicting probability of kidney allograft survival at 1, 3, 5, 7, and 10 years. Kidney transplant data from the United States Renal Data System (January 1, 1990, to December 31, 1999, with the follow-up through December 31, 2000) were used (n = 92,844). Independent variables included recipient demographic and anthropometric data, end-stage renal disease course, comorbidity information, donor data, and transplant procedure variables. Tree-based models predicting the probability of the allograft survival were generated using roughly two-thirds of the data (training set), with the remaining one-third left aside to be used for models validation (testing set). The prediction of the probability of graft survival in the independent testing dataset achieved a good correlation with the observed survival (r = 0.94, r = 0.98, r = 0.99, r = 0.93, and r = 0.98) and relatively high areas under the receiving operator characteristic curve (0.63, 0.64, 0.71, 0.82, and 0.90) for 1-, 3-, 5-, 7-, and 10-year survival prediction, respectively. The models predicting the probability of 1-, 3-, 5-, 7-, and 10-year allograft survival have been validated on the independent dataset and demonstrated performance that may suggest implementation in clinical decision support system.