Credit: Original article published here.
Currently in the United States there are more than 500,000 patients with kidney failure on dialysis. The preferred treatment for kidney failure is kidney transplantation. Compared with dialysis, transplantation is associated with longer survival, better quality of life, and fewer hospitalizations. There are national policy directives for dialysis facilities designed to maximize access to transplantation and incentivize waitlisting. However, according to Rachel E. Patzer, PhD, MPH, and colleagues, only ~18.5% of patients with kidney failure are on the transplant waitlist. Further, there is significant variation in access to the waitlist and subsequent transplant.
In December 2014, the United Network for Organ Sharing (UNOS) Organ Procurement and Transplantation Network (OPTN) implemented a policy that has had substantial impact on the way kidneys are allocated. Improvement in transplantation access equity was a major goal of the new kidney allocation system (KAS). The primary determinant of priority for organ allocation is allocation time. The new system changed the start of the allocation time from the date of waitlisting to the date of dialysis initiation (with the exception for pre-emptively waitlisted patients).
Since the implementation of KAS, waitlisting has declined. It is unknown whether the decline was due to a decline in referrals for transplant or changes in practice by transplant centers. Dr. Patzer et al conducted a cohort study designed to examine the impact of the 2014 KAS policy change on referral and evaluation for transplantation in a population of both incident and prevalent patients. Results were reported in the American Journal of Kidney Diseases [2022;80(6):707-717].
The researchers utilized data from a unique dataset encompassing Georgia (GA), North Carolina (NC), and South Carolina (SC). Data were obtained from the US Renal Data System database and then linked with the Early Transplant Access database. All new patients who initiated maintenance dialysis between January 1, 2012, and December 31, 2016, in GA, NC, or SC dialysis facilities were included. Follow-up continued through December 2017.
Exclusion criteria were preemptive transplantation (n=4167), <18 or >80 years of age (n=3553), or preemptively waitlisted (n=1672). The final study cohort included 37,676 patients following exclusion of those referred prior to kidney failure. The primary outcome variable was referral for kidney transplantation from a tristate dialysis facility to a tristate transplant center. Secondary outcomes included start of the transplant evaluation (at any of the nine transplant centers), and overall waitlisting (active, inactive, and missing active listing events) and active waitlisting (defined as UNOS listing status 1) among those who started the transplant evaluation.
The study compared demographic, clinical, and socioeconomic characteristics between incident patients before and after KAS. Compared with pre-KAS patients, post-KAS patients were slightly more likely to be non-Hispanic White, diabetic, informed of kidney transplantation, have a body mass index (BMI) of >35 kg/m2, and have diabetes as the primary cause of kidney failure. However, the differences were minimal between the two groups.
During the study period, 43.4% of patients were referred to transplantation. Of those, 52.4% started the evaluation process and 35.2% were waitlisted. There were minimal differences in sociodemographic and clinical characteristics in the proportions of patients who had each outcome prior to versus following KAS. For example, among referred patients, a slightly higher proportion were >70 years of age in the post-KAS period versus the pre-KAS period (11.46% vs 10.25%), had a BMI of >35 kg/m2 (27.04% vs 25.74%), and had comorbidities (eg, congestive heart failure, 23.00% vs 21.08%). After KAS, waitlisting declined or remained the same in these subgroups.
Pre-KAS, Black patients represented 55.87% of all kidney failure patients (vs 52.2% post-KAS). Compared with White patients, Black patients represented a larger proportion of those referred; differences before and after KAS were minimal (62.44% vs 61.99%, respectively). Among patients following completion of the evaluation process, Black patients represented 56.85% of waitlisted patients before KAS but 62.71% after KAS.
Following implementation of KAS, inactive listings of patients who had started evaluation (and had available active/inactive waitlist status data) declined (38.1% pre-KAS vs 26.4% post-KAS). In adjusted analyses, among incident patients there was a positive association between KAS implementation and referral (adjusted hazard ratio [aHR], 1.16; 95% CI, 1.12-1.20) and evaluation (aHR, 1.16; 95% CI, 1.10-1.21). There was a negative association between KAS implementation and overall waitlisting (aHR, 0.70; 95% CI, 0.65-0.76). Evaluated patients had lower active waitlisting (aHR, 0.81; 95% CI, 0.74-0.90) after versus before KAS.
In the prevalent population, there was an association between KAS implementation and a nonsignificant increase in referral (aHR, 1.18; 95% CI, 0.86-1.61), no impact on evaluation start (aHR, 0.99; 95% CI, 0.75-1.30), higher waitlisting (aHR, 1.74; 95% CI, 1.15-2.63), and higher active waitlisting (aHR, 2.01; 95% CI, 1.16-3.49).
The mean increase in referral after KAS among 790 dialysis facilities was 6.93% (median, 2.12%). A total of 422 facilities (53.4%) increased their referrals after KAS (median increase, 14.16%), and 314 facilities (39.7%) decreased their referrals after KAS (median, –10.10%). Fifty-four facilities (6.8%) did not have a change in referrals. From prior to and following AKS implementation, the median time from referral to waitlisting increased (4.6 months vs 6.0 months, respectively; P<.001). In addition, the proportion of patients who were preemptively waitlisted represented 9.87% of the waitlisted population (21.1% were actively waitlisted) prior to KAS and increased to 18.8% (43.2% actively waitlisted) after KAS (P<.001).
The researchers cited some limitations to the study including the observational design, and the possibility that the results are not generalizable outside the southeast United States where obesity, diabetes, and hypertension are more prevalent and transplant rates are among the lowest in the country. Further, the results do not capture patient referral and evaluation events that occur outside of the tristate area, and the researchers’ estimates may underrepresent referrals and evaluation.
In conclusion, the authors said, “Overall, the effect of KAS implementation differed by transplant step and by incident versus prevalent dialysis patients; and overall declines in waitlisting observed in the post-KAS era are largely due to decreased transplant center waitlisting of referred patients. These findings suggest that the change in KAS policy likely influenced provider behavior both at dialysis facilities (with respect to transplant referrals) as well as transplant programs (eg, evaluations and waitlisting practices). This study offers context for why overall waitlisting rates have declined nationally since the implementation of the 2014 KAS change and underscores the need to collect surveillance data on these important prewaitlisting process measures nationally, particularly as new changes in kidney allocation and new quality measures in dialysis facilities and transplant programs are under development.”
Takeaway Points
- Researchers reported results of a cohort study designed to examine the impact of the 2014 kidney allocation system (KAS) policy change on referral and evaluation for transplantation.
- Among incident dialysis patients, there was an association between KAS and increased referrals and evaluation starts among those referred.
- In the prevalent dialysis cohort, KAS was associated with increases in overall waitlisting and active waitlisting among those evaluated for transplant.
