Prevalence and Outcomes of Multiple-Listing for Cadaveric Kidney and Liver Transplantation

Abstract

Transplant candidates are permitted to register on multiple waiting lists. We examined multiple-listing practices and outcomes, using data on 81 481 kidney and 26 260 liver candidates registered between 7/1/95 and 6/30/00. Regression models identified factors associated with multiple-listing and its effect on relative rates of transplantation, waiting list mortality, kidney graft failure, and liver transplant mortality. Overall, 5.8% (kidney) and 3.3% (liver) of candidates multiple-listed. Non-white race, older age, non-private insurance, and lower educational level were associated with significantly lower odds of multiple-listing. While multiple-listed, transplantation rates were significantly higher for nearly all kidney and liver candidate subgroups (relative rate [RR] = = 1.42-2.29 and 1.82-7.41, respectively). Waiting list mortality rates were significantly lower while multiple-listed for 11 kidney subgroups (RR = = 0.22-0.72) but significantly higher for 7 liver subgroups (RR = = 1.44-5.93), suggesting multiple-listing by healthier kidney candidates and sicker liver candidates. Graft failure was 10% less likely among multiple-listed kidney recipients. Multiple-and single-listed liver recipients had similar post-transplant mortality rates. Although specific factors characterize those transplant candidates likely to multiple-list, transplant access is significantly enhanced for almost all multiple-listed kidney and liver candidates.

Survival after liver transplantation in the United Kingdom and Ireland compared with the United States

“…With regards to MELD, we6 and others22 23 have previously shown it to be a poor predictor of post-transplant mortality and, hence, its omission from the multivariable analyses is highly unlikely to have made a significant difference to our results. Similarly, although our analysis did not take account of waiting time for the procedure (which is considerably longer in the US4), it is inconceivable that this has influenced our results since previous studies have clearly demonstrated that longer waiting time does not predict more severe liver disease at transplantation6 or higher mortality either preoperatively or postoperatively 24 – 28…”

The Association of Center Performance Evaluations and Kidney Transplant Volume in the United States

“…The influence of report cards on contracting by insurance companies has been illustrated in a previous study indicating that there is a decline in candidate registrations that were privately insured associated with changes in graft survival rates (26). In fact, there may be evidence that, particularly for younger patients and those with logistic means to travel, patients do select centers that have better reported outcomes (12,27). In addition, results likely reflect the influence of private insurance agencies propensity to contract (or terminate contracts) with centers that meet (or fail to meet) certain performance criteria.…”

Who Is Caring for Kidney Transplant Patients? Variation by Region, Transplant Center, and Patient Characteristics

“…Factors available from the USRDS that were explored for associations with the odds of visits included age, sex, race, Hispanic ethnicity, primary cause of end-stage renal disease (ESRD), OPTN region, donor type, distance between patient home and transplant center, number of transplants performed at the center (categorized as <18, 18–34, 35–61, and >61 transplants/year, a priori), prior dialysis time, body mass index, delayed graft function (need for dialysis within first week), reported congestive heart failure, reported atherosclerotic heart disease (defined as history of ischemic heart disease, myocardial infarction, or cardiac arrest), most recent panel reactive antibody, number of human leukocyte antigen mismatches, initial maintenance immunosuppressive regimen, use of induction antibodies, median zip code income for patient zip codes, number of transplant centers within 200 miles of the transplant center (center density), urban (vs. rural) setting of the transplant center, and number of inpatient days during each time period. We adjusted for OPTN region to account for regional variations in practice patterns [5], as few patients are waitlisted for deceased-donor organs in more than one region [6]. All these variables were included in logistic regression models regardless of final model significance; therefore, statistical model building procedures were not employed.…”