Faculty Bibliography

IMPORTANCE: Risk of end-stage renal disease (ESRD) in kidney donors has been compared with risk faced by the general population, but the general population represents an unscreened, high-risk comparator. A comparison to similarly screened healthy nondonors would more properly estimate the sequelae of kidney donation. OBJECTIVES: To compare the risk of ESRD in kidney donors with that of a healthy cohort of nondonors who are at equally low risk of renal disease and free of contraindications to live donation and to stratify these comparisons by patient demographics. DESIGN, SETTINGS, AND PARTICIPANTS: A cohort of 96,217 kidney donors in the United States between April 1994 and November 2011 and a cohort of 20,024 participants of the Third National Health and Nutrition Examination Survey (NHANES III) were linked to Centers for Medicare & Medicaid Services data to ascertain development of ESRD, which was defined as the initiation of maintenance dialysis, placement on the waiting list, or receipt of a living or deceased donor kidney transplant, whichever was identified first. Maximum follow-up was 15.0 years; median follow-up was 7.6 years (interquartile range [IQR], 3.9-11.5 years) for kidney donors and 15.0 years (IQR, 13.7-15.0 years) for matched healthy nondonors. MAIN OUTCOMES AND MEASURES: Cumulative incidence and lifetime risk of ESRD. RESULTS: Among live donors, with median follow-up of 7.6 years (maximum, 15.0), ESRD developed in 99 individuals in a mean (SD) of 8.6 (3.6) years after donation. Among matched healthy nondonors, with median follow-up of 15.0 years (maximum, 15.0), ESRD developed in 36 nondonors in 10.7 (3.2) years, drawn from 17 ESRD events in the unmatched healthy nondonor pool of 9364. Estimated risk of ESRD at 15 years after donation was 30.8 per 10,000 (95% CI, 24.3-38.5) in kidney donors and 3.9 per 10,000 (95% CI, 0.8-8.9) in their matched healthy nondonor counterparts (P < .001). This difference was observed in both black and white individuals, with an estimated risk of 74.7 per 10,000 black donors (95% CI, 47.8-105.8) vs 23.9 per 10,000 black nondonors (95% CI, 1.6-62.4; P < .001) and an estimated risk of 22.7 per 10,000 white donors (95% CI, 15.6-30.1) vs 0.0 white nondonors (P < .001). Estimated lifetime risk of ESRD was 90 per 10,000 donors, 326 per 10,000 unscreened nondonors (general population), and 14 per 10,000 healthy nondonors. CONCLUSIONS AND RELEVANCE: Compared with matched healthy nondonors, kidney donors had an increased risk of ESRD over a median of 7.6 years; however, the magnitude of the absolute risk increase was small. These findings may help inform discussions with persons considering live kidney donation.

The recent CMS conditions of participation are based on risk-adjusted models produced by the Scientific Registry for Transplant Recipients (SRTR). The accuracy of these models in identifying poor-performing centers is unknown. In this stochastic simulation study, 1-year mortality outcomes were simulated in virtual transplant centers, and used to flag centers according to the methods used by CMS, evaluating nine overlapping 2.5-year periods of simulated data. In a simulation where all centers had the same underlying risk, 10.2% were falsely flagged at least once during the 4.5 years of simulated evaluations. The probability of false-positive flagging was lowest in low-volume centers (2.5%) and highest in high-volume centers (16.2%). In another simulation where 5% of centers were assigned twofold risk ("poor-performing centers"), only 32% of poor-performing centers were correctly flagged. In a final simulation where each center was assigned a unique mortality risk, 94% of flagged centers had greater-than-median risk, but only 32% of flagged centers were among the 5% with highest risk. Even after disregarding known covariate limitations to the risk adjustment models, statistical noise alone leads to spurious flagging of many adequately-performing transplant centers, yet the methods used by CMS fail to flag most centers with true elevated risk.

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.