No abstract

Background Evaluation of candidates for living kidney donation relies on screening for individual risk factors for end-stage renal disease (ESRD). To support an empirical approach to donor selection, we developed a tool that simultaneously incorporates multiple health characteristics to estimate a person’s likely long-term risk of ESRD in the absence of donation. Methods We used meta-analyzed risk associations from 7 general population cohorts, calibrated to US population-level incidence of ESRD and mortality, to project the estimated long-term incidence of ESRD in the absence of donation according to 10 demographic and health characteristics. We then compared 15-year projections to observed risk among recent US living kidney donors (N=52,998). Results There were 4,933,314 participants followed a median of 4 to 16 years. For a 40-year-old person with health characteristics similar to age-matched kidney donors, the 15-year ESRD risk projections in the absence of donation varied by race and sex: 0.24%, 0.15%, 0.06%, and 0.04% in black men, black women, white men, and white women. Risk projections were higher in the presence of lower estimated glomerular filtration rate, higher albuminuria, hypertension, smoking, diabetes, and obesity. In the model-based lifetime projections, ESRD risk was highest at younger age, particularly among African Americans. Risk projections in the absence of donation were 3.5–5.3-fold lower than 15-year observed risk post-donation in US kidney donors. Conclusions We suggest multiple health characteristics be considered together to estimate long-term ESRD risk for living kidney donor candidates.

We have previously described strong associations between frailty, a measure of physiologic reserve initially described and validated in geriatrics, and early hospital readmission as well as delayed graft function. The goal of this study was to estimate its association with postkidney transplantation (post-KT) mortality. Frailty was prospectively measured in 537 KT recipients at the time of transplantation between November 2008 and August 2013. Cox proportional hazards models were adjusted for confounders using a novel approach to substantially improve model efficiency and generalizability in single-center studies. We precisely estimated the confounder coefficients using the large sample size of the Scientific Registry of Transplantation Recipients (n = 37 858) and introduced these into the single-center model, which then estimated the adjusted frailty coefficient. At 5 years, the survivals were 91.5%, 86.0% and 77.5% for nonfrail, intermediately frail and frail KT recipients, respectively. Frailty was independently associated with a 2.17-fold (95% CI: 1.01–4.65, p = 0.047) higher risk of death. In conclusion, regardless of age, frailty is a strong, independent risk factor for post-KT mortality, even after carefully adjusting for many confounders using a novel, efficient statistical approach.

Early hospital readmission (EHR) after kidney transplantation (KT) is associated with increased morbidity and higher costs. Registry-based recipient, transplant, and center-level predictors of EHR are limited, and novel predictors are needed. We hypothesized that frailty, a measure of physiologic reserve initially described and validated in geriatrics and recently associated with early KT outcomes, might serve as a novel, independent predictor of EHR in KT recipients of all ages. We measured frailty in 383 KT recipients at Johns Hopkins Hospital. EHR was ascertained from medical records as ≥1 hospitalization within 30 days of initial post-KT discharge. Frail KT recipients were much more likely to experience EHR (45.8% vs. 28.0%, P=0.005), regardless of age. After adjusting for previously described registry-based risk factors, frailty independently predicted 61% higher risk of EHR (adjusted RR=1.61, 95% CI: 1.18–2.19, P=0.002). In addition, frailty improved EHR risk prediction by improving the area under the receiver operating characteristic curve (P=0.01) as well as the net reclassification index (P=0.04). Identifying frail KT recipients for targeted outpatient monitoring and intervention may reduce EHR rates.