Early Effect of the Circular Model of Kidney Allocation in the United States

Abstract

Significance Statement

To reduce geographic disparities in kidney transplantation, the United States implemented a new model of deceased donor kidney allocation in March 2021. The new model’s effect on transplant logistics and kidney utilization is unknown. Using data from the Scientific Registry of Transplant Recipients, this study found an increase in transplants among highly sensitized patients and patients with long dialysis duration. However, cold ischemia time after implementation of the new allocation policy increased significantly, with a suggestion of an increase in kidney discards. Given that the policy was implemented during the coronavirus disease 2019 pandemic, which also affected transplant practices, there is need for continued monitoring for potential unintended consequences of the new policy, along with efforts to mitigate them.

Background

In March 2021, the United States implemented a new kidney allocation system (KAS250) for deceased donor kidney transplantation (DDKT), which eliminated the donation service area-based allocation and replaced it with a system on the basis of distance from donor hospital to transplant center within/outside a radius of 250 nautical miles. The effect of this policy on kidney discards and logistics is unknown.

Methods

We examined discards, donor-recipient characteristics, cold ischemia time (CIT), and delayed graft function (DGF) during the first 9 months of KAS250 compared with a pre-KAS250 cohort from the preceding 2 years. Changes in discards and CIT after the onset of COVID-19 and the implementation of KAS250 were evaluated using an interrupted time-series model. Changes in allocation practices (biopsy, machine perfusion, and virtual cross-match) were also evaluated.

Results

Post-KAS250 saw a two-fold increase in kidneys imported from nonlocal organ procurement organizations (OPO) and a higher proportion of recipients with calculated panel reactive antibody (cPRA) 81%–98% (12% versus 8%; P<0.001) and those with >5 years of pretransplant dialysis (35% versus 33%; P<0.001). CIT increased (mean 2 hours), including among local OPO kidneys. DGF was similar on adjusted analysis. Discards after KAS250 did not immediately change, but we observed a statistically significant increase over time that was independent of donor quality. Machine perfusion use decreased, whereas reliance on virtual cross-match increased, which was associated with shorter CIT.

Conclusions

Early trends after KAS250 show an increase in transplant access to patients with cPRA>80% and those with longer dialysis duration, but this was accompanied by an increase in CIT and a suggestion of worsening kidney discards.

Allocation for deceased donor kidney transplantation (DDKT) was changed to a circular model of distribution in March 2021.^1,2 This change is an effort to eliminate the contribution of arbitrary donation service area (DSA) boundaries to geographic disparities in transplant access, while recognizing the potential for increased travel time and longer cold ischemia time (CIT) in some instances.^1,3 The new allocation model (hereafter referred to as KAS250) prioritizes recipients located within 250 nautical miles (NM) from the donor hospital by providing additional priority (proximity) points. Because all transplant centers within 250 NM of the donor hospital are included in the primary allocation priority in the new KAS250 allocation, the number of centers that “compete” for each deceased donor kidney is substantially higher.⁴ Along with prolonged CIT, a key concern with this increased allocation complexity is the potential for reduced kidney utilization. Given that this change is considered a step toward the eventual goal of continuous distribution,⁵ evaluating its effect on DDKT patterns, especially allocation logistics, is essential. This will identify unintended consequences and measure changes in allocation-related processes such as use of donor kidney biopsy, machine perfusion, and virtual cross-match (VXM), which have an effect on CIT and delayed graft function (DGF).^6–12

Using data from the Scientific Registry of Transplant Recipients (SRTR), we evaluated the early effect of the new KAS250 allocation policy on kidney utilization, CIT, DGF, and changes in allocation-related practices (biopsy, machine perfusion, and VXM use). Because the new allocation policy moves away from DSA-based allocation, we evaluated these changes for kidneys transplanted within and outside of the “traditional” donor organ procurement organization (OPO) boundaries.

Methods

Study Design and Patient Population

This observational study used SRTR data, which include data on all donor, waitlisted candidates, and transplant recipients in the United States, submitted by the members of the Organ Procurement and Transplantation Network (OPTN). The Health Resources and Services Administration, US Department of Health and Human Services, provides oversight to the activities of the OPTN and SRTR contractors. The data reported here have been supplied by the Hennepin Healthcare Research Institute (HHRI) as the contractor for the Scientific Registry of Transplant Recipients (SRTR). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy of or interpretation by the SRTR or the US Government. Data are available quarterly, and this analysis used the March 2022 SRTR files.

To evaluate early changes after KAS250 implementation (March–December 2021), we compared the post-KAS250 cohort to a pre-KAS250 cohort from the preceding 2 years (March 1, 2019 to March 14, 2021; Figure 1). We selected 2 years in order to allow for inclusion of 1 year before the coronavirus disease 2019 (COVID-19) pandemic (March 2019–March 2020) data. Kidneys procured for transplantation during the study period were used to derive the deceased donor cohort and a corresponding recipient cohort, which included pediatric recipients but excluded multiorgan transplants (Figure 1). The post-KAS250 cohort was truncated at December 31, 2022, to ensure reliable data completeness.

Figure 1.

Study outline and cohort selection. Figure shows derivation of the donor and recipient cohorts for both the pre-KAS250 and post-KAS250 periods. Donor cohorts were compared to assess discard rates and changes in donor characteristics. Comparison of recipient cohorts provided changes in characteristics of DDKT recipients, post-transplant outcomes (DGF & CIT), and process-related changes (i.e., machine perfusion and virtual cross-match use). CIT, cold ischemia time; DDKT, deceased donor kidney transplantation; DGF, delayed graft function; KAS250, kidney allocation system 250.

Variables and Outcomes

Study objectives were to evaluate the beneficial effect of the new policy and to examine for unintended consequences stemming from wider sharing of kidneys.

Because kidneys are no longer preferentially offered to transplant centers located within the same donor OPO-based donation service area, we evaluated the change in proportion of transplanted kidneys that were derived locally versus those imported from nonlocal OPOs. For this study, a local kidney implies that the OPO retrieving the kidney and the recipient transplant center were within the same (original) DSA boundaries.

Kidney utilization was evaluated by comparing the proportion of discarded kidneys (kidneys procured for transplantation but not transplanted) in the pre- and post-KAS250 cohorts along with changes, if any, in reasons for discards. Process-related outcomes assessed were changes in CIT and DGF, the latter defined as the need for dialysis within the first week after transplantation. CIT changes were evaluated both as average change in mean CIT and categorically as proportion of kidneys transplanted with CIT>24 or >30 hours. Changes were evaluated for local and nonlocal OPO kidneys.

Given the potential for long CIT and higher allocation complexity, there is a possibility of programs modifying practices surrounding allocation (e.g., machine perfusion, biopsy use, crossmatch practices) to mitigate some of the unintended consequences. To evaluate these changes in practice patterns related to kidney preservation, and donor and recipient assessment at the time of transplantation, we evaluated the proportion of kidneys undergoing machine perfusion and biopsies, and the use of VXM for assessing pretransplant HLA compatibility. VXM was defined as either a transplant with no cell-based (physical) cross-match performed or one where cell-based cross-match was performed retrospectively (i.e., retrospective physical cross-match).

Donor and recipient characteristics were compared between the pre- and post-KAS250 periods, including among subgroup of discarded kidneys and kidneys imported from nonlocal OPOs. The Kidney Donor Profile Index (KDPI) was calculated as per OPTN recommendations.¹³ The 2018 reference population was used to map the Kidney Donor Risk Index (KDRI) to derive the KDPI value. Calculated panel reactive antibody (cPRA; %) was categorized into four categories (≤10%, 11%–80%, 81%–98%, and >98%), given the bimodal distribution and the priority afforded in kidney allocation.

Statistical Analyses

Descriptive analyses with a t test or Wilcoxon rank-sum test for continuous variables (data presented as mean with SD) and chi-squared test for categorical variables were used to evaluate differences between pre- and post-KAS250 cohorts.

For assessing changes in kidney discard and CIT, we used an interrupted time series analysis (ITSA), with the study period divided into 1-month intervals.^14,15 ITSA allows for evaluating the immediate effect (i.e., change in intercept) and the change over time (i.e., slope change) after a policy implementation or a natural experiment. For this study, March 2020 (for COVID-19) and March 2021 (KAS250) were used as the demarcation time points, with the caveat that changes (if any) after March 2021 could be from KAS250 and/or COVID-19 because the COVID-19 pandemic was still ongoing when KAS250 was implemented. Discard proportion and average CIT for each month of the study period was predicted using regression analysis, which adjusted for KDPI (for discards) and KDPI, machine perfusion, and biopsy (for CIT). Autocorrelation was checked using the Cumby–Huizinga general test, which showed no significant correlation of time series data. Final ITSA analyses was completed using the Prais–Winsten generalized least-squares regression assuming a first-order autoregressive process and the use of the Durbin–Watson statistic for testing autocorrelation. ITSA for discards was also repeated for donor subgroups on the basis of KDPI (<20 or >85), donation after circulatory death (DCD; yes or no), and donor serum creatinine (<2 mg/dl or ≥2 mg/dl).

Absolute difference in CIT before and after KAS250 was evaluated using a multivariable linear regression model that adjusted for KDPI, donor kidney biopsy, and machine perfusion. An interaction term for whether the kidney was shared between OPOs was used to evaluate significant changes in CIT for local versus import (nonlocal) kidneys. To account for similarities among kidney pairs from the same donor (mate kidneys), a robust sandwich estimator option was used with the donor as the clustering variable. DGF risk before and after KAS250 was evaluated with a multivariable logistic regression model with adjustment for KDPI, DCD status, CIT, machine perfusion, and recipient factors (age, sex, body mass index [BMI], diabetes status, and pretransplant dialysis duration). DGF risk after KAS250 was also evaluated without adjusting for CIT to avoid masking the effect of CIT changes after KAS250. Missing data for baseline characteristics was ≤0.5%, except for recipient BMI (2%; Supplemental Table 1). A complete case analysis was performed for baseline characteristics and estimation of CIT and DGF risks. For baseline donor and recipient characteristics, we considered P<0.001 statistically significant to limit the type 1 error rate and to highlight clinically meaningful differences. For all other analyses, P<0.05 was considered significant. Analysis was performed with Stata v17 (StataCorp, College Station, TX) with Stata package st0389_7 used for ITSA.

This study was approved by the University of Pittsburgh’s Institutional Review Board (STUDY21070192). The clinical and research activities being reported are consistent with the Principles of the Declaration of Istanbul as outlined in the Declaration of Istanbul on Organ Trafficking and Transplant Tourism.¹⁶

Results

Study Population

Between March 1, 2019, and December 31, 2021, 67,593 deceased donor kidneys were procured for transplantation: 20,976 in the post-KAS250 period (monthly average of 2208 kidneys) and 46,617 in the pre-KAS250 period (monthly average of 1902 kidneys). From this cohort, a post-KAS250 recipient cohort of 12,927 kidney alone recipients was identified, which was compared with a pre-KAS250 recipient cohort of 30,415 DDKT recipients (Figure 1).

Changes in Baseline Donor-Recipient Characteristics and Kidney Utilization

Among all procured kidneys, donor age, KDPI (KDRI), and creatinine were comparable between the two cohorts (Table 1). However, the post-KAS250 cohort had a higher proportion of DCD donors compared with the pre-KAS250 cohort (32% versus 26%; P<0.001). Among procured kidneys, use of machine perfusion was unchanged (43% versus 43%; P=0.63) between the two periods, whereas the frequency of donor kidney biopsy was slightly higher after KAS250 (60% versus 58%; P<0.001).

Table 1.

Characteristics of kidneys retrieved for transplantation (N=67,593)

	Procured Kidneys			Discarded Kidneys
Characteristics	Pre-KAS250	Post-KAS250	P Value	Pre-KAS250	Post-KAS250	P Value
		(n=46,617)		(n=20,976)	(n=9713)		(n=5119)
		Donor age (yr), mean (SD)	41 (16)	42 (16)	<0.001	52 (14)	53 (13)	0.11
Woman donor		17,932 (38)	7800 (37)	0.002	4338 (45)	2177 (43)	0.01
KDPI, mean (SD)	47 (29)	47 (29)	0.03	71 (24)	70 (24)	0.007
KDRI, mean (SD)	1.3 (0.5)	1.3 (0.5)	0.11	1.71 (0.5)	1.68 (0.5)	<0.001
KDPI>85	5504 (12)	2576 (12)	0.08	3457 (36)	1730 (34)	0.03
DCD donor	12,232 (26)	6643 (32)	<0.001	2923 (30)	1936 (38)	<0.001
Donor serum creatinine (mg/dl), mean (SD)	1.5 (1.4)	1.5 (1.4)	0.28	2.1 (1.8)	2.1 (1.8)	0.06
Machine perfusion	20,051 (43)	8983 (43)	0.63	4013 (41)	2552 (50)	<0.001
Donor kidney biopsy	27,140 (58)	12,530 (60)	<0.001	8592 (88)	4511 (88)	0.51
Discard	9713 (21)	5119 (24)	<0.001	—	—
Discard reasona	—	—	—			<0.001
Biopsy findings	—	—	—	2191 (23)	734 (14)
No recipient located	—	—	—	4719 (49)	3165 (62)
Poor organ function	—	—	—	499 (5)	212 (4)
Anatomic abnormalities	—	—	—	382 (4)	149 (3)
Other	—	—	—	1913 (20)	859 (17)

Data shown as n (%) unless otherwise indicated.

^aAmong discarded kidneys.

Discards

The proportion of kidneys discarded increased in the post-KAS250 period (24% versus 21%; P<0.001). This was noted among both kidneys that were biopsied (36% versus 31.7%; P<0.001) and those that were not (7.2% versus 5.7%; P<0.001; P for interaction=0.51). The proportion of discarded kidneys that were biopsied was unchanged before versus after KAS250 (88% versus 88%; P=0.51). DCD kidneys comprised a larger proportion of discarded kidneys in the post-KAS250 period (38% versus 30%; P<0.001; Table 1). Although discards increased among both DCD donors (29% versus 23.9%; P<0.001) and donation after brain death (DBD) donors (22% versus 20%; P<0.001), the KAS250 effect was more among DCD kidneys (P for interaction=0.004). KDPI/KDRI of discarded kidneys were slightly lower in the post-KAS250 period: KDPI, 70 versus 71 (P=0.007); KDRI, 1.68 versus 1.71 (P<0.001). Finally, the post-KAS250 period had fewer discards for “biopsy findings” (14% versus 23%; P<0.001; Table 1).

Figure 2A shows national trends in kidney procurement, transplants, and proportion of discards during the study period. Although the number of kidneys procured increased in the first few months after KAS250, the trend did not persist. After an initial statistically insignificant drop in kidney discards immediately after KAS250 (i.e., intercept of –1.4%; P=0.41), there was a steady increase in discards noted over time, with a statistically significant change in slope after KAS250 implementation (Figure 2B). The increase in discards after KAS250 was 0.64% per month (95% confidence interval [CI], 0.09 to 1.2; P=0.02), resulting in a discard proportion of 28% per month at the end of the study period. There was no statistically significant change in the intercept (–0.65; 95% CI, –4.5 to 3.2; P=0.73) or slope (–0.03% per month; 95% CI, –0.6 to 0.54; P=0.93) of kidney discards after the onset of the COVID-19 pandemic in March 2020.

Figure 2.

Kidney procurement and utilization over the study period. (A) Shown are trends for kidneys procured (numbers/month), transplanted (numbers/month) and discarded (percentage of procured kidneys) over the study period. After an initial drop in percentage of discarded kidneys (orange line) immediately after KAS250, an upward trend in discards was witnessed. (B) Interrupted time series analysis for kidney discards. Kidney discards over the study period are shown, with vertical dashed lines indicating the two demarcation time points corresponding to COVID19 (first line) and KAS250 (second line). Individual data points in the graph represent "predicted" monthly discard percentages derived from a KDPI-adjusted regression model that used individual kidney-level data. There was a statistically significant increase in discard rates beginning in March 2021 (after KAS250) with an average increase in discard rates of 0.6%/month. There was no significant change in discards in the time period between the COVID19 pandemic onset in March 2020 and KAS250 implementation in March 2021. COVID19, coronavirus disease 2019; KAS250, kidney allocation system 250; KDPI, kidney donor profile index.

In subgroup analysis, kidneys with KDPI<20 (Figure 3A), those from DBD donors (Figure 3C), and those with a creatinine ≥2 mg/dl (Figure 3F) also showed a significant increase in discards over time (i.e., slope change) after KAS250. Although an increasing trend in discards was also noted for the KDPI>85 (Figure 3B), DCD (Figure 3D), and creatinine <2 mg/dl (Figure 3E) subgroups, the slope change was not statistically significant. None of the subgroups showed a significant change in discard (either intercept or slope) after the onset of the COVID-19 pandemic in March 2020.

Figure 3.

Interrupted time series analysis for kidney discards among subgroups. Kidney discards over the study period are shown for subgroups, with vertical dashed lines in each subplot indicating the two demarcation time points corresponding to COVID-19 (first line) and KAS250 (second line). A statistically significant increase in discards over time after KAS250 was seen for (A) KDPI<20, (C) DBD donors, and (F) creatinine>2 mg/dl groups. Although an increasing trend in discards was also noted for (B) KDPI>85, (D) DCD, (E) and creatinine<2 mg/dl, the change in slope was not statistically significant compared with the pre-KAS250 period. None of the subgroups showed a statistically significant change in discards between the onset of the COVID-19 pandemic in March 2020 and the implementation of KAS250 in March 2021. Discards were adjusted for kidney donor profile index.

Recipient and Transplant Characteristics

Recipient demographics (age, race, and sex), BMI, preemptive status, and ESKD etiology were similar before and after KAS250 (Table 2). Compared with the pre-KAS250 cohort, the post-KAS250 cohort had a higher proportion of patients with cPRA 81%–98% (12% versus 8%; P<0.001) and those with >5 years of pretransplant dialysis (35% versus 33%, P<0.001; Table 2). Time on the waiting list was unchanged (mean 28 months versus 28 months; P=0.67). Trends in key donor and recipient characteristics over the study period are shown in Figure 4, A–E. The proportion of patients with cPRA 81%–98% bumped immediately after KAS250 and persisted until the end of the study period (Figure 4C), whereas the KAS250 effect on patients with a long dialysis duration appeared to be leveling off (Figure 4E). A greater proportion of kidneys transplanted in the post-KAS250 period were from DCD donors (32% versus 27%; P<0.001), but other donor characteristics (age, creatinine, and KDPI) and frequency of donor kidney biopsies were similar.

Table 2.

Baseline characteristics for transplanted kidneys (N=43,342)

Characteristic		Pre-KAS250	Post-KAS250	P Value
			(n=30,415)		(n=12,927)
Recipient age (yr), mean (SD)	52 (15)	51 (15)	<0.001
Recipient<18 yr	937 (3)	432 (3)	0.16
Woman	12,040 (40)	5117 (40)	1
Recipient race			<0.001
Asian	2249 (7)	1038 (8)
Black	10,105 (33)	4482 (35)
Other (multiracial, Native American, and Pacific Islander)	728 (2)	305 (2)
White	17,333 (57)	7102 (55)
Pretransplant dialysis	26,920 (89)	11,259 (88)	0.2
Recipient BMI (kg/m2), mean (SD)	28 (6)	28 (6)	0.09
Recipient DM	11,172 (37)	4765 (37)	0.8
ESRD cause			0.004
Diabetes mellitus	8945 (30)	3867 (30)
Hypertension	7139 (24)	3119 (24)
PKD	2231 (7)	880 (7)
Glomerular disease	6633 (22)	2650 (21)
Other	5367 (18)	2362 (18)
cPRA (%)			<0.001
0–10	19,710 (65)	7975 (62)
11–80	6115 (20)	2522 (20)
81–98	2334 (8)	1543 (12)
99–100	2256 (7)	887 (7)
Dialysis duration (mo), mean (SD)	48 (40)	49 (41)	0.004
Dialysis >5 yr	10,010 (33)	4518 (35)	<0.001
Time on waitlist (mo), mean (SD)	28 (29)	28 (29)	0.67
Zero HLA ABDR mismatch	1451 (5)	631 (5)	0.62
Nonlocal OPO	8297 (27)	7650 (59)	<0.001
Donor age (yr), mean (SD)	39 (15)	39 (15)	0.06
Woman donor	11,333 (37)	4632 (36)	0.005
KDPI, mean (SD)	42 (26)	42 (26)	0.23
KDRI, mean (SD)	1.2 (0.4)	1.2 (0.4)	0.23
KDPI >85	1782 (6)	744 (6)	0.67
DCD donor	8347 (27)	4127 (32)	<0.001
Donor serum creatinine (mg/dl), mean (SD)	1.3 (1.2)	1.3 (1.3)	0.62
Donor kidney biopsy	16,344 (54)	7017 (54)	0.3
Machine perfusion	14,086 (46)	5646 (44)	<0.001
Virtual cross-match	6973 (23)	3658 (28)	<0.001
Retrospective PXM	4720 (17)	2715 (23)	<0.001

Data shown as n (%) unless otherwise indicated. DM, diabetes mellitus; PXM, physical (cell-based) cross-match.

Figure 4.

Trends for select recipient and donor characteristics over the study period. Shown are (A) changes in average donor KDPI, (B) proportion of DCD kidneys, (C) recipients with cPRA 80%–98%, (D) recipients with cPRA>98%, (E) pretransplant dialysis duration of >5 years, and (F) pretransplant dialysis duration of >10 years. The vertical dashed line in each subplot indicates the demarcation corresponding to the implementation of KAS250 in March 2021. Only the proportion of cRPA 80%–98% saw a noticeable increase in the post-KAS250 period, which persisted at above the pre-KAS250 average at the end of study period. There was no change in the very highly sensitized cPRA>98% group. The initial increase noted among groups with dialysis >5 years did not seem to persist after the first 3 months after KAS250.

As expected, kidneys that were imported from nonlocal OPOs increased significantly—from 27% (8297) in the pre-KAS250 period to 59% (7650) after KAS250 (P<0.001). This was noted across all United Network for Organ Sharing (UNOS) regions, albeit at varying proportions (range 28%–79%; Figure 5A). Imported kidneys in the post-KAS250 period were more likely to be from DCD donors (31% versus 28%; P<0.001), have lower KDPI (39% versus 46%; P<0.001), have a lower proportion with KDPI>85 (5% versus 8%; P<0.001), and were less likely to have undergone biopsies (53% versus 62%; P<0.001; Supplemental Table 2).

Figure 5.

Proportion of transplant kidneys imported from nonlocal OPO and cold ischemia time among the UNOS regions. Nonlocal OPO kidneys (A) and average cold ischemia times (B) increased across all UNOS regions but with some variation in magnitude of change.

Changes in CIT and DGF

CIT

Compared with before KAS250, mean CIT was longer after KAS250 (19.9 versus 17.9 hours; P<0.001; Table 3) and was seen across all UNOS regions (Figure 5B). Although CIT decreased for import (nonlocal OPO) kidneys (21.7 versus 24.4 hours; P<0.001), local kidneys (i.e., kidneys that were placed within the DSA boundaries of the procuring OPO) experienced longer CIT after KAS250 (17.2 versus 15.4 hours; P<0.001)—changes that were apparent immediately post-KAS250 implementation and persisted until the end of study period (Figure 6). Analysis of change in trends using ITSA confirmed the statistically significant increase in CIT of +1.4 hours (95% CI, 0.73 to 2.1; P<0.001) immediately (i.e., intercept) after KAS250 but without any significant trend thereafter (i.e., no difference in slope; –0.02 per month; 95% CI, –0.14 to 0.09; P=0.67; Figure 7). CIT dropped immediately after the onset of COVID-19 (March 2020) but had recovered to prepandemic levels by March 2021 when KAS250 was implemented (Figure 7).

Table 3.

Differences in cold ischemia time before and after KAS250 implementation

Cold Ischemia Time (h)	Pre-KAS250	Post-KAS250	P Value
		Hours, Mean (95% Confidence Interval)		Hours, Mean (95% Confidence Interval)
Overall
Unadjusted	17.9 (17.8 to 18)	19.8 (19.7 to 20)	<0.001
Adjusteda	17.9 (17.8 to 18)	19.9 (19.7 to 20)	<0.001
Local OPO kidneys
Unadjusted	15.6 (15.5 to 15.7)	17.6 (17.4 to 17.8)	<0.001b
Adjusteda	15.4 (15.3 to 15.5)	17.2 (17 to 17.4)	<0.001b
Nonlocal OPO kidneys
Unadjusted	24.1 (23.9 to 24.3)	21.4 (21.2 to 21.6)	<0.001b
Adjusteda	24.4 (24.3 to 24.6)	21.7 (21.6 to 21.9)	<0.001b

^aModel was adjusted for KDPI, biopsy, machine perfusion, and clustering among kidney donors.

^bThe effect of KAS250 on CIT was different for local and nonlocal OPO kidneys (P for interaction<0.001).

Figure 6.

Cold ischemia during the study period. Trends in cold ischemia time (in hours) before and after the implementation of KAS250 are shown with a vertical dashed line indicating the demarcation corresponding to the KAS250 implementation in March 2021. Shown are results for kidneys from (A) local OPO and nonlocal OPO and (B) overall cohorts. CIT increased significantly even among kidneys from local OPOs. CIT for nonlocal OPO kidneys decreased but remained higher than that of local OPO kidneys. The combination of these two changes along with nonlocal kidneys comprising two thirds of transplanted kidneys after KAS250 resulted in overall increase in average CIT for the post-KAS250 cohort.

Figure 7.

Interrupted time series analysis for cold ischemia time. CIT changes over the study period are shown, with vertical dashed lines indicating the two demarcation time points corresponding to COVID-19 (first line) and KAS250 (second line). Individual data points in the graph represent “predicted” monthly CIT averages derived from an adjusted (for KDPI, biopsy, and machine perfusion) regression model that used individual kidney-level data. There was a statistically significant immediate increase (i.e., change in intercept) in CIT after KAS250 (second vertical dashed line) without any subsequent change in trends with time (nonsignificant change in slope). Similarly, there was a statistically significant decrease in CIT immediately after the onset of COVID-19 (first dashed line) but without any subsequent change in trends with time (nonsignificant change in slope).

Kidneys transplanted with >24 hours CIT increased from 20% in the pre-KAS250 period to 25.5% after KAS250 (P<0.001). This increase was noted among most UNOS regions (except regions 3 and 9) and was driven primarily by an increase in local kidneys with >24 hours CIT, with some regions seeing a two- to three-fold increase (Supplemental Figure 1, A–C). A slight increase in kidneys with CIT>30 hours was also observed (9.3% versus 8.2%; P<0.001).

DGF

DGF incidence was higher in the post-KAS250 period (30% versus 28%), with an odds ratio (OR) of 1.09 (95% CI, 1.04 to 1.15; P=0.001) on unadjusted analysis. Among local OPO kidneys, DGF incidence increased in the post-KAS250 period (29% versus 26%; P<0.001), whereas it decreased for nonlocal OPO (import) kidneys (31% versus 34%; P<0.001). On multivariable models adjusting for CIT, DCD, KDPI, machine perfusion, and recipient factors, there was no difference in DGF risk between the pre- and post-KAS250 periods (OR=0.99; 95% CI, 0.93 to 1.04; P=0.6). Results were similar in models that did not adjust for CIT (OR=1.04; 95% CI, 0.99 to 1.1; P=0.1). Kidneys with CIT>24 hours had a higher DGF risk on both univariate and multivariable analyses (OR=1.65; 95% CI, 1.6 to 1.7; P<0.001).

Changes in Machine Perfusion and VXM Patterns among Transplanted Kidneys

The use of machine perfusion increased slightly among both local (53% versus 51%; P=0.002) and nonlocal OPO kidneys (37% versus 34%; P<0.001) in the post-KAS250 period. However, because use was significantly lower among nonlocal OPO kidneys, which constitute two thirds of kidneys transplanted after KAS250, the overall use of machine perfusion was lower after KAS250 implementation (44% versus 46%; P<0.001; Supplemental Figures 2 and 3).

The post-KAS250 period had higher VXM use for assessing pretransplant HLA compatibility (28% versus 24%; P<0.001; Supplemental Figures 2 and 3), including an increase in retrospective physical (cell-based) cross-match use (17% versus 23%; P<0.001). This increase in VXM was noted mostly for local OPO kidneys (28%–22%; P<0.001) rather than for nonlocal OPO (import) kidneys (26% versus 28%; P=0.005; P for interaction <0.001). In analysis adjusted for KDPI, CIT was shorter among transplants performed with VXM compared with a physical cross-match (17.7 versus 18.7 hours; P<0.001). This was similar for local (15.1 versus 16.2 hours) and nonlocal OPO kidneys (22.1 versus 23 hours; P for interaction=0.35).

Discussion

Large variations in the probability of transplantation are noted for patients on the waitlist in the United States that are driven primarily by variations in organ offer acceptance rather than differences in organ availability.¹⁷ KAS250 attempts to reduce geographic inequities that result from the use of arbitrary DSA and OPTN regional boundaries. However, major allocation changes can be associated with unintended consequences, the recognition of which will help identify avenues for further modifications. In this analysis using SRTR data, we present data for early trends for changes to DDKT characteristics, CIT, and kidney utilization after KAS250 implementation.

Simulation modeling by SRTR suggested that the circle of 250 NM would reduce geographic variability by reducing the significant differences in waiting times seen across the OPTN regions.^1,3 The model predicted that transplant rates would increase for patients with long dialysis duration and those with cPRA >80%, which is consistent with our findings that the post-KAS250 period had a higher proportion of recipients with >5 years of dialysis and those with cPRA 81%–98%. However, the increase was more noticeable and persistent for patients with cPRA 81%–98% than for the longer dialysis cohort, who seemed to have experienced a bolus effect that did not persist beyond the first 3–4 months. The 2014 KAS awarded exponentially increasing priority points for cPRA and allowed broader kidney sharing for sensitized patients but limited this wider sharing to candidates with cPRA≥98%.¹⁸ Consequently, cPRA 80%–89% candidates experienced lower transplantation rates after the implementation of the 2014 KAS.^19,20 The new KAS250 thus appears to be improving transplantation access for sensitized patients who did not benefit from the 2014 allocation change. The lack of change for cPRA>98% after KAS250 was expected because this group already had regional and national priority before KAS250.

The increase in proportion of transplanted kidneys imported from nonlocal OPOs to two thirds of transplanted kidneys was expected. The increased “competition” arising from more centers being included in the initial allocation priority may have beneficial and detrimental consequences.⁴ Concern has been expressed that the increased transplant center/OPO interactions may prolong CIT. After the implementation of KAS250, several UNOS regions had 20%–30% of their local OPO kidneys transplanted with CIT>24 hours. Given the increased risk of DGF in this group and that ongoing efforts for accelerated placement of kidneys are focused on lowering the CIT, this change should be of particular concern to the OPTN.⁴ Although the long-term implications of DGF related to prolonged CIT are likely limited, it does decrease the viability of organs that are less than ideal and the willingness of programs to accept these organs.²¹

The increase in CIT even for kidneys transplanted within the original boundaries of the procuring OPO (i.e., local kidneys) points to another inadvertent negative consequence of the new allocation system. This would suggest that the introduction of more centers at the top of the match run is increasing the operational inefficiency in allocation.^22,23 For example, multiple centers with patients at the top of the match run creates the need for donor samples to be sent to more centers when a physical cross-match is desired. Also, local centers are potentially receiving offers late in the allocation process compared with the pre-KAS250 period. As a result, even though local OPO kidneys are traveling a shorter distance, the added steps leading up to the determination of final destination are likely prolonging CIT. Given these issues, programs will have to adapt and modify their practices surrounding organ acceptance, recipient selection, and HLA compatibility assessment.

Consideration should be given for further expanding VXM use for assessing HLA compatibility because this has been associated with shorter CIT.^9,10,12 A higher VXM use was noted in this early post-KAS250 period, particularly among locally placed kidneys. It is possible that programs in the post-KAS250 period are utilizing more VXM for local OPO kidneys due to increased complexity and resulting longer CIT. Modifying HLA testing on waitlisted patients and having a clear protocol may allow for safe expansion and more routine VXM use.^24,25

The overall decrease in machine perfusion is notable, despite its purported benefits for organs with prolonged CIT and DCD kidneys.^6,26 The trend toward lower use was driven by the lower use of machine perfusion among import kidneys compared with local OPO kidneys. Machine perfusion went up only slightly for import kidneys and may be related to logistics and the higher cost associated with transporting kidneys out of the OPO on pump. The increase in machine perfusion for locally placed kidneys was also small, despite the longer CIT and higher DGF risk among this cohort after KAS250 implementation. Given the longer CIT and increased use of DCD kidneys after KAS250, there may be an opportunity to increase machine perfusion use further. This may also help alleviate the worrisome trend of higher kidney discards in the post-KAS250 period. Measures to reduce inappropriate kidney biopsies might also help in reducing CIT, given the strong association of donor biopsy with prolonged CIT and the negative effect of biopsies on utlization.^8,27,28

Finally, despite more transplant centers now being included in the “local” (250 NM) match run, kidney utilization in the early post-KAS250 period appears to be worse, with more than one in four retrieved kidneys being discarded. The reasons for this are unclear. Although it may be related to the new policy, other factors such as the ongoing COVID-19 pandemic, increased scrutiny of OPOs, and change in quality of procured kidneys could be contributing and confounding this observation. The increase in discards after KAS250 was not immediate (i.e., no intercept change) but rather occurred over time, unlike the changes in CIT, which were immediately apparent. The absence of an immediate effect, however, does not entirely rule out KAS250 affecting discards over time. KAS250 does come with increased logistical challenges, but unless programs and/or OPOs are maladapting to the change, the discards should not be worsening over time. It is possible that programs are becoming more selective in their organ acceptance after KAS250 because (1) there has been an increase in transplant access to some categories of patients (long dialysis duration, cPRA>80%), and (2) programs are now dealing with more organ offers from multiple OPOs at the same time—for either the same or different patients on their waitlist. Anecdotal evidence about increased offer volume at larger centers, the need for tighter bypass filter criteria, and increased offer turndown rates are consistent with these findings. The future release of program-specific reports should be evaluated to examine if this is occurring.

Although we adjusted for KDPI in the ITSA analysis, it is still possible that the rise in discards may be related to the quality of organs being procured in ways not fully captured by this adjustment. For example, the number of DCD kidneys retrieved (and utilized) in the post-KAS250 period has increased—with some of this possibly due to increased OPO scrutiny.^29,30 Despite their increased utilization, DCDs did make up a larger proportion of discarded kidneys in the post-KAS250 period. Because OPOs may have changed their procurement practices due to ongoing scrutiny and/or wider sharing of kidneys after KAS250, we looked for a correlation between DCD procurement among OPOs and discard rates, but none was found at the individual OPO level (Supplemental Figure 4, A–C). However, there was a suggestion of relative increase (compared with before KAS250) in discards among OPOs whose proportion of DCD kidneys also increased after KAS250 implementation (Supplemental Figure 4D). It is therefore possible that KAS250 and/or OPO scrutiny may be leading to recovery of more “marginal” kidneys that have a higher discard risk. Concurrent difficulty in placing these organs within the new system may be playing a role as well. There are additional considerations, however. Although the average monthly volumes of procured kidneys was higher in the post-KAS250 period, other than the increase in DCD kidneys, there were no changes in KDPI or proportion of KDPI>85 kidneys. Also, an increase in discards was noted even among KDPI<20 kidneys and DBD donor kidneys, suggesting that donor quality alone does not fully explain the noted increase. The lower proportion of kidneys discarded for “biopsy” reasons perhaps indicates that fewer discards are a result of direct concern about organ quality but instead a function of logistical and operational challenges.

Finally, it is unclear how much of the lowered willingness to accept kidneys for transplantation is a direct result of the ongoing COVID-19 pandemic along with its attendant challenges such as those related to transportation logistics.³¹ Programs and OPOs have had to adapt continuously to the ever-changing nature of a pandemic characterized by the emergence of new variants. Despite these challenges, however, a reduction in transplant activity was noted only in the early phase of the pandemic (March–April 2020). For the time series analysis, we included a year of the COVID-19 pandemic period before KAS250 implementation to derive a “baseline” trend in discards during the pandemic. Although ITSA is a robust quasi-experimental method that can quantify the magnitude and significance of the slope and intercept change, attributing the changes in discard after March 2021 to the policy would require an assumption that COVID-19 changes in transplant processes had stabilized by March 2021. Given the changing nature of the pandemic, this may not be true. Although programs may have adapted to the pandemic, it is possible that the emergence of more infectious variants (Supplemental Figure 5) and the prolonged nature of the pandemic continued to have an effect by affecting the transplant workforce, donor suitability, and “readiness” level of waitlisted candidates. We should note, however, that the Omicron wave was just beginning in December 2021 when we censored the data for our study (Supplemental Figure 5).

Another limitation of our findings is that the data presented here pertain only to the first 9 months after KAS250 implementation, which is a brief period to assess the full implication of a major policy change. Like the “bolus” effect after 2014 KAS, where highly sensitized and long dialysis vintage patients experienced an immediate and significant increase in transplant rates, it is possible that these post-KAS250 trends may also be transient due to the sudden wider geographic sharing and may dissipate as programs and OPOs adjust to the new policy by developing new operational strategies and additional resources. Nevertheless, awareness of early trends does provide an opportunity to evaluate for major deviations from expected outcomes and to confirm if prior modeling predictions were accurate.

In summary, our study provides important insights into how the new deceased donor kidney allocation system in the United States is functioning and draws attention to several consequences of the new system. These changes are of particular importance, given the plans for a continuous distribution-based organ allocation with no “hard” boundaries.⁵ Results show that KAS250 is having some of the intended benefits but with a few unintended consequences. More uniform access to transplantation as evidenced by increased transplantation of those with longer dialysis duration and cPRA 80%–98% is encouraging but was linked with longer CIT—an expected price for reducing geographic disparities. Centers should consider various strategies such as the use of VXM and machine perfusion to counter the prolonged CIT and improve organ utilization. The trend noted in kidney discards needs close monitoring and additional studies to identify contributing factors and to develop improved strategies.

Supplementary Material

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Disclosures

V. Gunabushanam reports that his spouse is employed by Fitch Ratings. S. Hariharan reports being the owner of Transplant Interface, LLC. C. Puttarajappa reports research funding from NIH (NIDDK) and that his spouse is a site Primary Investigator for contracted clinical research with Abeona/Ultragenyx, Denali, Regenxbio, and Shire. S. Mohan reports consultancy for Angion Biomedica, eGenesis, and HSAG; an advisory or leadership role for ASN Quality Committee (member), ETCLC (National Faculty Chair), Kidney International Reports (ISN; deputy editor), SRTR Review Committee (member), and UNOS data advisory committee (vice chair); and research funding from NIH (NIDDK, NIHMD and NIBIB) and the Kidney Transplant Collaborative. All remaining authors have nothing to disclose.

Funding

This work was supported by a career development award through the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health to C. Puttarajappa (K08DK119576).

Author Contributions

V. Gunabushanam, S. Hariharan, W. Hoffman, R. Mehta, S. Mohan, C. Puttarajappa, P. Sood, and A. Tevar reviewed and edited the manuscript; S. Hariharan, S. Mohan, and C. Puttarajappa were responsible for visualization and for the investigation; V. Gunabushanam, W. Hoffman, R. Mehta, S. Mohan, P. Sood, and A. Tevar were responsible for the validation; S. Mohan was responsible for supervision; S. Mohan and C. Puttarajappa were responsible for the conceptualization; S. Mohan and C. Puttarajappa were responsible for the methodology; C. Puttarajappa was responsible for funding acquisition and wrote the original draft of the manuscript; and C. Puttarajappa and X. Zhang were responsible for data curation and formal analysis.

Data Sharing Statement

The data that support the findings of this study are available from the Scientific Registry of Transplant Recipients (SRTR) upon request.

Supplemental Material

This article contains the following supplemental material online at http://links.lww.com/JSN/D599.

Supplemental Table 1. Details on missing data for variables of interest.

Supplemental Table 2. Characteristics of transplanted kidneys imported from nonlocal donor organ procurement organization before and after implementation of KAS250.

Supplemental Figure 1. Proportion of kidney transplants with >24 hours CIT across UNOS regions.

Supplemental Figure 2. Machine perfusion and virtual crossmatch use before and after implementation of KAS250.

Supplemental Figure 3. Machine perfusion and virtual crossmatch use for transplanted local OPO and nonlocal OPO kidneys before and after implementation of KAS250.

Supplemental Figure 4. Relationship between procurement of DCD kidneys and discard among OPOs.

Supplemental Figure 5. Trends in kidney recovery, discards, and transplants (top panel) and COVID-19 cases.