|Year : 2017 | Volume
| Issue : 2 | Page : 25-31
Demographic and outcome disparities among New York, New Jersey, and Pennsylvania transplant recipients
Kamran Khanmoradi1, Stalin Campos1, Meredith Lynne Scott2, Afshin Parsikia1, Radi Zaki1, Jorge Ortiz2
1 Department of Surgery, Einstein Healthcare Network, Philadelphia, PA, USA
2 Department of Surgery, University of Toledo Medical Center, Toledo, OH, USA
|Date of Submission||04-Jan-2017|
|Date of Acceptance||07-May-2017|
|Date of Web Publication||22-Jun-2017|
Meredith Lynne Scott
Department of Surgery, University of Toledo Medical Center, 3000 Arlington Avenue, Toledo, OH 43614
Source of Support: None, Conflict of Interest: None
Aim: Geographic variations in kidney transplant outcomes are well documented but poorly understood. This study aims to determine outcome variations among patients from three different states. Methods: A total of 917 renal transplant records were analyzed. Demographics and outcomes were retrospectively compared between the three states of New York (NY), New Jersey (NJ), and Pennsylvania (PA) from 2001 to 2012. The variables were compared with Chi-square test or Mann–Whitney test. We used Kaplan–Meier methodology to compare survival. Results: Higher waitlist times between NY and NJ recipients resulted in earlier transplantation when relisted in Philadelphia. PA recipients received a higher proportion of high-risk and hepatitis C virus (HCV) antibody-positive donors and were more likely to be HCV positive. PA recipients recorded the lowest rate of delayed graft function. Overall patient and graft survival rates at 1 and 3 years were not statistically different between the three states. However, by the end of the study, PA recipients exhibited the highest patient mortality and the highest retransplantation rates. Conclusion: One-year and 3-year patient and graft survival rates were not significantly different. This may be indicative of closer follow-up of PA recipients or longer wait times among NY and NJ candidates. Nevertheless, long-term outcomes were significantly worse in PA patients. Poor long-term outcome may be a manifestation of a sicker overall cohort. Medium-term transplantation results of those patients living in the favorable organ procurement organization are not negatively affected.
Keywords: Demographics, renal, transplantation
|How to cite this article:|
Khanmoradi K, Campos S, Scott ML, Parsikia A, Zaki R, Ortiz J. Demographic and outcome disparities among New York, New Jersey, and Pennsylvania transplant recipients. Transl Surg 2017;2:25-31
|How to cite this URL:|
Khanmoradi K, Campos S, Scott ML, Parsikia A, Zaki R, Ortiz J. Demographic and outcome disparities among New York, New Jersey, and Pennsylvania transplant recipients. Transl Surg [serial online] 2017 [cited 2020 Jul 4];2:25-31. Available from: http://www.translsurg.com/text.asp?2017/2/2/25/208869
| Introduction|| |
Geographic differences in transplant outcome have been well documented but not well understood. Our study found that although patients traveling to another organ procurement organization (OPO) are required to travel far distances and have difficulties with follow-up, they still have favorable 1- and 3-year outcomes. Since its inception in 1984, the Organ Procurement and Transplantation Network strived for an equitable distribution of donor's kidneys across the United States. The nation was divided into 12 United Network for Organ Sharing (UNOS) regions and 58 donor service areas (DSAs). The “final rule” defined uniform clinical and referral guidelines and required that kidneys recovered were first offered to waitlisted candidates within a DSA before sharing between the surrounding DSAs and UNOS regions., Despite these efforts, geographic variations in organ allocation and waiting times exist. This coupled with differing clinical and demographic profiles has resulted in variable posttransplant outcomes across the US.,,
Both donor and recipient factors are known to influence survival rates. For instance, a donor over the age of 60 years is considered an expanded criteria donor (ECD). Under the new allocation system implemented in 2014, deceased donors are now stratified by their kidney donor profile index (KDPI). The KDPI factors in donor age, height, weight, ethnicity, presence of hypertension and diabetes, hepatitis C status, serum creatinine level, and donation after circulatory death (DCD) status. Furthermore, KDPI over 85% is considered equivalent to an ECD. A high KDPI, and therefore an ECD, is associated with lower posttransplant survival when compared to standard criteria kidneys. To date, the proportion of transplants from donors over 65 is highest in New York (NY) (13.8%) and Pennsylvania (PA) (12.2%).
The differences in number of patients in need of transplantation and patient demographics have all contributed to the extreme differences in wait time between DSAs. Comparing the 58 DSAs in the country, the median wait time varies from 0.61 to 4.57 years. This disparity has recently grown, with the variation in wait time between DSAs at 3.26 years in 2000 and increasing to 4.72 years in 2009.
Socioeconomic status also has a major role in the kidney transplantation process. Variations in socioeconomic status between recipients from different states are known to influence accessibility to healthcare services., Davis et al. report that DSAs with the longest wait times have patients with lower socioeconomic status. Furthermore, patients with lower socioeconomic status are less likely to multiple lists, are waitlisted late in their disease progression, and have longer wait times for transplantation.
It has been noted that NY (region 2) has a very high staffing rate and a lower proportion of for-profit dialysis units. These two factors are associated with higher standardized transplant ratios (STRs). Another factor associated with improved STR is the number of transplant centers per 10,000 end-stage renal disease patients. The PA (region 4) region exhibits the highest ratio of transplant centers per potential candidates in the country. Therefore, it is not surprising that many patients from the NY and NJ regions seek out transplantation options in PA.
Ongoing efforts to minimize disparities in outcome require a more detailed analysis of these clinical and demographic variables. The goal of our study was to identify disparities in renal transplant outcomes between recipients from NY, NJ, and PA, transplanted in Northern Philadelphia over 11 years.
| Methods|| |
A retrospective chart review of deceased donor transplant recipients (n = 917) at a single tertiary care center from 2001 to 2012 with residential zip codes in NJ (n = 147), NY (n = 214), and PA (n = 556) was conducted. Approval was obtained from the Institutional Review Board. We excluded living donor transplant recipients to achieve a more uniform analysis. All the patients received a standard induction with rabbit antithymocyte globulin and maintenance with a calcineurin inhibitor, antimetabolite, and corticosteroids, which were literature based.,
ECD is defined as any donor over the age of 60 or a donor over 50 years of age plus two of the following: cerebrovascular accident as the cause of death, preexisting hypertension, or terminal serum creatinine >1.5 mg/dL. High-risk donor status is defined by the Centers for Disease Control as donors who engage in behaviors, which present an increased risk of HIV transmission. The outcome was categorized into short-, medium-, and long-term and the three were defined as follows:
- Short-term outcome: Rate of delayed graft function (DGF) and length of stay (LOS)
- Medium-term outcome: One- and 3-year patient survival
- Long-term outcome: The percentage of living, dead, retransplanted, and lost to follow-up cases in each state at the end of the 11-year study period.
Continuous variables such as age, body mass index (BMI), waitlist days, cold ischemia time, and length of hospital stay were expressed in terms of mean and standard deviation. They were compared using the nonparametric Mann–Whitney U-test since all the variables were not normally distributed. Categorical variables including donor type, gender, ethnicity, high-risk status, hepatitis C virus (HCV) serology, panel reactive antibody (PRA) >20% status, DGF, retransplantation, and patient mortality were recorded in terms of percentage of the total number within the group. They were compared with Chi-square or Fisher's exact test. One- and 3-year patient and graft survival rates were computed using Kaplan–Meier (K-M) analysis. Type I error rate was set at 0.05. All statistical analyses were conducted using SAS 9.2 (SAS Institute Inc., Cary, NC, USA). The missing values were excluded from the analysis.
| Results|| |
Recipients from PA were more likely to receive kidneys from HCV antibody-positive donors (P < 0.05). By contrast, donor type, donor age, donor BMI, and donor gender distribution were not significantly different between the three patient categories [Table 1].
Recipients from PA were also most likely to be African-American (AA) (P < 0.05) [Table 2] and with PRA level >20% (although marginally not statistically significant). Mean recipient BMI was highest in NJ recipients and lowest in NY recipients (P = 0.0016). There were more HCV-positive recipients in PA (21.9% for the PA vs. 7.4% for the NY vs. and 11.5% for the NJ) (P < 0.05).
Mean wait time was considerably higher in the NY patient category (1,245 days) compared to NJ (1,070 days) and PA (833 days) (P < 0.05). The longest cold ischemia time in the NJ category trended toward statistical significance (P = 0.06).
Rate of DGF was significantly higher in the NJ group as compared to the NY and PA groups [Figure 1] and [Table 3] (P < 0.05). Mean LOS was significantly different between the three groups (P = 0.008) [Table 2].
Overall patient survival rates at 1- and 3-year were not significantly different between NJ (93.8%, 77.1%), NY (92.3%, 78.2%), and PA (88.6%, 70.2%) recipients (P > 0.05).
Overall, graft survival rates at 1 and 3 years were not significantly different; NJ (77.1%, 66.3%), NY (78.9%, 67%), and PA (71.5%, 62.2%).
Analysis of recipients by donor type revealed no significant difference in 1-year patient survival rates between NJ, NY, and PA for recipients of DCD (88.46%, 80%, and 90.36%), standard (96.05%, 92.73%, and 94.22%), and ECD grafts (100%, 96.55%, and 85.71%).
Similarly, 3-year patient survival rates were not significantly different between NJ, NY, and PA recipients of DCD (81.25%, 78.57%, and 77.19%), standard (86.79%, 77.78%, and 80.18%), and ECD (62.5%, 85.71%, and 67.74%) grafts.
The K-M curves are not shown because they were numerous and none of them demonstrated statistical significance through performing log-rank tests [Table 4].
Long-term outcomes were the worst in the PA group, with highest patient mortality (25%) and highest rate of retransplantation (5.6%). The number of patients who were lost to follow-up was highest in the NY category [Figure 2] (12.6%, P < 0.05).
| Discussion|| |
The DSA in which a recipient lives is a major factor determining posttransplant outcome. Davis et al. found that after adjusting for age, sex, race, PRAs, diagnosis, insurance, and education status, there was a statistically significant difference between recipients in the 58 DSAs. Patients in DSAs with the longest wait times had a significantly higher hazard ratio for graft failure compared to patients in DSAs with shorter wait times.
Numerous studies have linked certain demographic stereotypes to unfavorable posttransplant outcomes.,, However, the influence of these factors is often confounded by issues encountered at different levels of patient care. Patient proximity to the transplant center often determines access to pretransplant evaluation, type of donor organ received, and regularity of postoperative follow-up. In addition, geographic disparities in waiting periods across the US are responsible for variation in the timing and ultimately the outcome following surgery. Our study integrates evidence-based data with our extensive institutional experience to help identify disparities in clinical, demographic, and geographic factors that might ultimately impact survival.
Our analysis spanned 11 years at a high-volume transplant center with deceased donor recipients from three competitive Northeastern states with variable waiting periods. Donor age, type, gender, and BMI were not significantly different. The PA recipients received a higher proportion of HCV-positive donor grafts as compared to the NY or NJ recipients. Due to the immunosuppressed state of the recipient, they may develop an aggressive form of chronic hepatitis C and also have an increased risk of cardiovascular complications, liver disease, and infections, which ultimately leads to higher patient mortality., It is unclear what role direct-acting antiviral agents will play in the long-term outcomes of kidney transplant recipients with HCV.
The PA recipients received their grafts from AA donors at a higher rate (P < 0.05) when compared to the other groups. Kidneys from deceased AA fail more rapidly than kidneys from deceased European-Americans, and they have a hazard ratio of 1.196, which translates to a 20% increased risk of allograft loss.,
Multiple studies show reduced patient and graft survival rates in AAs. Axelrod et al. states that this may be due to the lack of completion of the initial evaluation for transplantation. Patients living far from the transplant center, AA, and patients with low socioeconomic status or a lower education are less likely to initiate their primary transplantation evaluation., Delaying the initial evaluation lengthens the amount of time between the onset of disease and kidney transplantation, potentially worsening transplant outcome. It is unclear how the new rules started in December 2014, which base allograft allocation on initiation date of dialysis rather than the date of listing, will affect these outcomes. We censored our data to eliminate this possible confounding variable.
A higher number of these patients have a lower socioeconomic status, which is associated with an inferior posttransplant survival when compared to more affluent recipients. In addition, AA patients tend to have higher rates of diabetes and hypertension when compared to Whites.
Patients from PA were more likely to have had retransplantation than those from NJ or NY, suggesting a more clinically complex cohort. The NJ recipients had a significantly higher BMI as compared to the NY and PA recipients. Obese transplant recipients have a higher risk of DGF and a slightly increased risk of graft loss. However, none of the recipients in the three states had a mean BMI within the obese range. Hence, the influence of recipient BMI on the outcome was not considered significant.
The longest wait time was noted in NY, followed by NJ and then PA. Longer waitlist periods have been attributed to lower organ procurement rates, higher number of waitlisted patients, and higher transplant center competition. Although longer wait times indicate a higher incidence of dialysis-related complications, the impact of relisting outside the original DSA has to be considered. When NY and NJ recipients were relisted for a transplant in PA, they were eligible for an inter-DSA transfer of waitlist time and therefore received a higher priority than patients in PA.
Recipients in PA demonstrated the least favorable donor and recipient characteristics. However, the short-term outcome revealed that the rates of DGF were considerably lower in PA (41.19%) recipients as compared to NJ (51.7%) and NY (50%) recipients. Higher rates of DGF among AA recipients have been noted and are probably due to their high-risk clinical profile, lower socioeconomic status, nonadherence, and reduced access to care. In addition, PA recipients were more likely to have been retransplanted which has also been associated with higher rates of DGF and graft failure. PA recipients were also more likely to have received grafts from high risk and HCV-positive donors. PA patients' poor prognosticators probably led to their poor long-term survival. We maintain that the close follow-up allowed the patients to achieve acceptable short- and mid-term outcomes. Generally, patients who are of higher socioeconomic status could be found in NJ and NY more than PA. Patients with higher socioeconomic status can travel and can be listed frequently due to their potential access to a secondary insurance.
Our medium-term outcome measures belied this demographic inequality. Overall patient survival rates were significantly different between the three patient categories at 1 and 3 years. In addition, graft survival rates were not significantly different at 1 and 3 years between the groups. These findings may be indicative of closer follow-up care and timely intervention in the PA recipient category due to their proximity to the treatment facility.
On long-term follow-up, patient mortality and rates of retransplantation were significantly higher in the PA group (25%, 5.63%) compared to NY (9.35%, 1.8%) and NJ (17.69%, 3.4%). Recipients of HCV-positive kidneys are at risk for decreased long-term patient and graft survival,, possibly due to HCV-related glomerulonephritis, chronic rejection, and transplant glomerulopathy. However, direct-acting antivirals may lead to improved survival in the future.
In addition, higher rates of nonadherence with immunosuppression due to lower education, income, and Medicare insurance among AA patients have been noted.
These populations are at risk for nonadherence due to the increased prevalence of poor health literacy in these populations. Nonadherence is a major risk factor for graft loss.,,
A significantly higher number of NY patients were lost to follow-up in our study group probably as a result of distance from the center. The distance from Manhattan to our transplant center is 100 miles, and the average transit time by car is 2 h and by public transport is 3 h. The distance from Northern NJ to our transplant center is approximately 55 miles and average transit time is 1 h and 15 min. In a publication on liver transplant accessibility and outcomes, multivariable models showed a strong association between increased distance from transplant center with lower likelihood of being waitlisted, receiving an organ, and long-term survival. As per our analysis, however, greater distance from transplant center did not adversely influence long-term outcome. Some better long-term results in the NJ and NY recipients compared to the PA recipients are likely due to more suitable demographic profiles.
According to the literature, the ratio of Hispanics transplanted was higher than the ratio of Hispanics on dialysis in NY and PA, but NJ-transplanted Hispanics was a much lower percentage than NJ Hispanics on dialysis. This may reflect a lack of education about transfer of time and financial constraints. Surprisingly, the percentage of White patients transplanted was demonstrably lower than the percentage of White patients on dialysis in the three regions. Finally, although the percentage of AA patients transplanted from NY and NJ was similar to dialysis patients in NY and NJ, the percentage of AA patients transplanted was much higher than the percentage of AA patients on dialysis in PA. This may reflect a concerted effort to aggressively transplant the local population.
It is important to note that published percentages of different ethnic groups on dialysis may not appropriately estimate percentages of these ethnic groups on the waitlist for transplantation.
Strengths and limitations
Our study is the first of its kind, which compares important demographic variables between recipients from NJ, NY, and PA transplanted at a single center. Despite high transplant volume, center competition, and long waiting periods in these three states, reports of outcomes in these three state recipients are lacking. We adhered to a consistent immunosuppressive regimen for all recipients transplanted at our center.
Limitations of our study were due to the retrospective nature of the study design. In addition, the study spanned a considerable period, during which there were changes in surgical faculty, technology, and care delivery plans. We did not take into account education status, insurance coverage and income, which are known to influence healthcare accessibility and posttransplant outcome. Although we did establish association, the independent impact of demographic variables on patient and graft survival was not assessed. A total of 54 (5.9%) patients were lost to follow-up and their long-term outcomes were unknown. There is also a possibility that patients in need of retransplantation from other regions did not return to Philadelphia for this intervention.
In December 2014, the transplant community adopted a different allocation system. We did not evaluate the effect of differing KDPI on our patient cohort. At this point, KDPI is just one of many tools utilized to estimate recipient outcomes.
This single-center report should shed significant light on the practice of inter-DSA transfer of waiting time and subsequent transplantation. Whether the allocation revisions will ultimately effect this behavior is not entirely clear.
In conclusion, PA recipients had the least favorable demographic profile with higher percentages of AA, retransplanted, high-risk, and HCV-positive donor recipients. Waitlist times were highest in NY followed by NJ and resulted in these patients receiving an earlier transplantation due to inter-DSA transfer of waiting time. Patient and graft survival at 1 and 3 years was not significantly different between the three groups and is probably indicative of a closer follow-up of PA recipients due to their proximity to the transplant center. Long-term patient mortality and rates of retransplantation were highest in the PA group and are again suggestive of demographic inequalities. At the end of our study, a significantly higher number of patients in the NY group were lost to follow-up due to their distance from our transplant facility. Despite significant distance and potential difficulty in follow-up, patients transferring from one OPO to another may benefit from shortened wait times and experience acceptable 1- and 3-year outcomes.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4]