The numbers speak for themselves. There are currently 121,678 people waiting for life-saving organ transplants in the US. Of these, 100,791 await kidney transplants. A patient is added to the kidney waitlist every 14 minutes and 13 people die every day waiting for a kidney transplant (1). These numbers and their implications led to the Kidney Week 2017 session, Political Correctness? Public Policy Influences on Transplantation, moderated by Roy D. Bloom, MD, and Michelle A. Josephson, MD.
In the segment Kidney Allocation Changes: Past, Present, and Future, Richard N. Formica, MD, of the Yale School of Medicine, outlined where the Organ Procurement and Transplantation Network (OPTN) Kidney Allocation System (KAS) changes of December 2014 have led. Formica is professor of medicine and surgery and director of transplant medicine at Yale.
He laid out several precepts for consideration:
An organ allocation system without disparities is probably not possible.
Equity may not always be desirable if other goals are adversely affected.
Allocation policy only addresses disparities in allocation for waitlisted patients—it does not address disparities in access to the kidney waitlist itself.
Simply put, getting on the waitlist is an access issue, and receiving a kidney is an allocation issue.
Prior to revisions to the KAS, disparities existed in several areas. Revisions to the KAS were designed to address four of these areas (Table 1).
One of the key revisions now shaping allocation policy is the introduction of longevity matching, which basically pairs those patients with the longest life expectancy with kidneys expected to last the longest. This is the first of four pillars of the current KAS. The remaining three pillars are:
Matching the allocation score to the biological need of the highly sensitized recipient.
Recalculating waiting time to start at the date of dialysis initiation instead of the date of listing.
Improving access for minority candidates by allocating donor organs with blood type A2 to B blood type recipients.
Issues of insurance and geography still persist. Those with the resources can be waitlisted in multiple locations, increasing their chances of moving up the waitlist. Those without those resources may be affected by living in a donor service area with much lower rates of transplantation. Formica pointed out that under section 121.8 of the OPTN Final Rule, organ allocation, “shall not be based on the candidate’s place of residence or place of listing, except to the extent required by paragraphs (a)(1)-(5) of this section.” The presence of geographic disparities seems to be, on its face, in violation of stated policy. The next big challenge according to Formica was access to the transplant list itself.
Transplant regulation: benefits and challenges
Transplant is arguably one of the most regulated parts of medicine, noted Jesse D. Schold, PhD, Director of the Center for Population Health Research at the Cleveland Clinic.
Use of quality oversight report cards yields potential benefits but also potential ill effects. Objectively, Schold said that report cards:
Inform patients and caregivers—healthcare transparency.
Ensure regulatory oversight—quality assurance.
Provide incentives and feedback for quality monitoring—a lot more people paying attention.
Identify best processes.
Invoke competition in quality.
Concerns about using report cards in healthcare include the creation of artificial objectives at the expense of patient care efforts, variability in assessments by statistical methods used, the selected or limited use of information by consumers, the deleterious impact on access to care for vulnerable populations, and the lack of input of patient practices.
The Scientific Registry of Transplant Recipients (SRTR) has the data to evaluate the challenges currently facing patients. In 2017, 35,000 organ transplants were performed in the US (2). To be eligible to participate as a transplant center in the US the center must have its outcomes measured on a semi-annual basis and assessed based on observed and expected survival. These measured outcomes are equated to public funding dollars. Approximately 10% of US transplant programs have lower than expected graft or patient survival in a given year.
CMS flags a transplant program for review based on the assessment of a center’s risk-adjusted expected (E)* and observed (O) events for 1-year patient survival and 1-year graft survival. However, different statistical analysis approaches are used by CMS and UNOS.
Schold pointed out that in 2014, a Bayesian statistical model was incorporated into the SRTR outcomes assessment for transplant centers. Bayesian analysis is a statistical method that answers research questions about unknown parameters of statistical models by using probability statements. Bayesian analysis rests on the assumption that all model parameters are random quantities and thus can incorporate prior knowledge. This assumption is in sharp contrast with the more traditional, also called frequentist, statistical inference where all parameters are considered unknown but fixed quantities. This second approach, frequentist, is used by CMS.
Has kidney transplant regulation gone too far?
With these factors in mind, Schold laid out the four areas that help answer the overall question: Has kidney transplant regulation gone too far?
Too much flagging?
Using data on flagging of facilities and observed and expected outcomes at small and large centers (3), the level of regulation does not appear proportional—the ramifications and layers of regulation exceed the level of existing outliers.
Influence of confounding factors
The adjustment for co-morbid information obtained from Medicare claims would change the qualitative performance of 8–9% of centers. This lack of comorbidity adjustment may disadvantage centers willing to accept higher risk patients (4). Community risk is strongly associated with pre-transplant processes and outcomes—where you live matters significantly (5).
Significant unintended consequences
There are many smaller consequences tied to the new rating system, but the most significant change is the culling of the waitlist by a transplant center following a low performance rating. The removal of people classified as removed for being “too sick” or “other” reasons leaves some concerned that relatively healthy transplant candidates may get caught up in the zeal to avoid any additional low performance scores with overly stringent list culling (6).
Wrong Endpoints
A one-year survival rate may have made sense 25 to 30 years ago, but is that the appropriate timeframe today? In an era of comprehensive care and payment models, perhaps patients would be better served by a more comprehensive quality assessment that also captures pre-transplant and factors impacting access to waitlist.
Patient survival rates should have real life relevancy, which means factoring in dialysis and survival rates for patients who remain on a waitlist when evaluating performance.
References
- 3.↑
Schold JD. Evaluation of flagging criteria of United States kidney transplant center performance: how to best define outliers? Transplantation 2017; 101:1373–1380.
- 4.↑
Weinhandl ED, et al.. Effect of comorbidity adjustment on CMS criteria for kidney transplant center performance. Am J Transplant 2009; 9:506–516.
- 5.↑
Schold JD, et al.. Prominent impact of community risk factors on kidney transplant candidate processes and outcomes. Am J Transplant 2013; 13: 2374–83.
- 6.↑
Schold JD, et al.. Association of candidate removals from the kidney transplant waiting list and center performance oversight Am J Transplant 2016; 16:1276–84.
