Open Journal of Internal Medicine, 2012, 2, 1-6 OJIM
http://dx.doi.org/10.4236/ojim.2012.21001 Published Online March 2012 (http://www.SciRP.org/journal/ojim/)
Transfusion, erythropoiesis-stimulating agent therapy, and
kidney transplant wait time
Robert M. Perkins1,2*, H. Lester Kirchner3, Rajesh Govindasamy2
1Center for Health Research, Geisinger Medical Center, Danville, USA
2Department of Nephrology, Geisinger Medical Center, Danville, USA
3Division of Medicine, Geisinger Medical Center, Danville, USA
Received 29 June 2011; revised 10 October 2011; accepted 21 October 2011
ABSTRACT
Aim : Anemia is highly prevalent among patients wait-
listed for renal transplant, and management with
blood transfusion or erythropoietin stimulating agents
may impact transplant wait time. The purpose of this
study was to examine the impact of blood transfusion
and erythropoiesis stimulating agent therapy on renal
transplant wait time. Methods: We retrospectively
analyzed all adult patients listed for first deceased
donor kidney transplantation at two transplant cen-
ters in Central Pennsylvania between 2004 and 2008.
The exposures of interest were blood transfusion and
erythropoietin stimulating agent therapy. Cox pro-
portional hazards were used to model time to de-
ceased donor kidney transplant. Results: Among 407
patients listed for transplant, 84 received a deceased
donor kidney during a median follow-up of 26.3
months. In an adjusted Cox proportional hazards
model, with erythropoiesis stimulating agent and
transfusion both treated as time-dependent exposures,
UNOS inactive status at listing date (hazard ratio
[HR] 0.81; 95% CI 0.73 - 0.89; P < 0.001) and trans-
fusion during the wait list period (HR 0.27; 95% CI
0.11 - 0.69; P = 0.01) independently predicted longer
transplant wait time. Erythropoiesis stimulating agent
use prior to or after transplant wait listing date did
not independently predict wait time. Conclusion:
Blood transfusion while waitlisted for kidney trans-
plant is strongly associated with prolonged wait time.
Keywords: Anemia; Blood Transfusion; Erythropoietin;
Kidney Disease; Transplantation
1. INTRODUCTION
Renal transplantation is the preferred therapy for patients
with end-stage renal disease (ESRD) because it is asso-
ciated with better long-term survival, [1] improved qual-
ity of life, and lower costs [2]. For potential deceased-
donor renal transplant recipients, time spent on the trans-
plant wait list has increased during the past decade. For
those patients listed in 2009, it is expected that median
wait time will approach 4 years, an increase of nearly
25% compared with wait times for patients listed in 2004
[3]. A longer renal transplant wait time—particularly
when receiving maintenance hemodialysis—is associated
with an increased risk of cardiovascular and other mor-
bidity and shorter graft and patient survival after trans-
plantation [4,5].
Several factors have been consistently associated with
longer renal transplant wait times, including non-white
race, B and O blood types, and immunologic sensitiza-
tion, as reflected by an elevated panel reactive antibody
(PRA) level [6,7]. Along with pregnancy and prior organ
transplant, transfusion of red blood cells can contribute
to the elevation in PRA among renal transplant candi-
dates [8], which suggests that transfusion might be asso-
ciated with longer transplant wait time.
Anemia of chronic kidney disease (CKD) is highly
prevalent among patients listed for renal transplant. The
management of this condition has undergone a major
change in the past 20 years, as erythropoiesis-stimulating
agent (ESA) therapy has largely replaced intermittent
transfusion of packed red blood cells among patients
with ESRD. Although the rates of blood transfusion have
declined substantially in this population [9], it still re-
mains a common intervention in patients not treated with
ESAs [10].
Despite the relatively recent evolution of anemia
management among patients with late-stage CKD, the
impact of packed red blood cell transfusion or ESA ther-
apy on transplant wait time has not been systematically
reported. The goal of this study was to determine that
impact among those listed for first deceased donor renal
transplant.
2. METHODS
*Corresponding author. This was a retrospective cohort study of CKD patients

R. M. Perkins et al. / Open Journal of Internal Medicine 2 (2012) 1-6 2
listed for a deceased donor kidney transplant. The study
was approved by the Geisinger Medical Center Institu-
tional Review Board in December, 2009 (protocol #
20100136). The data source was EpicCare, Geisinger
Medical Center’s electronic health record, which con-
tains detailed demographic, lifestyle (e.g., smoking),
procedural, laboratory, radiographic, vital, and other
clinical data for all patients receiving care at any of more
than 40 outpatient clinics and 3 inpatient facilities, and
has been previously used as a research instrument [11].
All patients 18 years or older with stage 4 or 5 CKD
and listed for renal transplantation at either of Geis-
inger’s 2 transplant centers between July 1, 2004, and
November 30, 2008, were eligible for the study. Patients
with a prior solid-organ transplant were excluded, as
were those who received a living-donor kidney transplant
during the study period. Study outcomes were assessed
through November 30, 2009.
Baseline information was obtained during the 6
months before the United Network for Organ Sharing
(UNOS) listing date (index date) for each patient. Ex-
tracted information included demographic data; detailed
comorbid disease history; clinical encounters; laboratory
test results; prescription and procedure orders. Interna-
tional Classification of Diseases, version 9 (ICD-9)
codes from a minimum of 2 separate outpatient encoun-
ters or listed on the medical problem list were used to
document the presence of any comorbid condition (CHF:
425.4 - 425.9, 428, 428.**; cerebrovascular disease: 438,
438.0, 438.*0, 438.*1, 438.*2, 438.*9, 438.53, 438.6* -
438.9*, 438.6 - 438.9, 437, 437.0, 437.0 - 437.2, 437.8,
437.9, 436, 435, 435.*, 434, 434.*, 434.**, 433, 433.*,
433.**; diabetes: 250, 250.*, 250.**; pulmonary disease:
491, 491.*, 491.**, 492, 492.*, 493, 493.*, 493.**, 494,
494.*, 496; peripheral vascular disease: 440, 440.*,
440.**, 443, 443.8, 443.9, 443.8*, 445.0*, 445.8*, 557,
557.*). Dialysis, transplant, and vital status were ex-
tracted from 6 months prior to index date through the end
of the study period (November 30, 2009). Confirmation
of renal transplantation was made by linking a cohort
member’s medical record number from EpicCare with
Geisinger’s renal transplant service database, and further
verified by identifying CPT codes 50360 and 50365 in
EpicCare.
The Charlson Comorbidity Index (CCI) [12] score was
calculated at baseline for each patient. Baseline labora-
tory and vital information was recorded using the value
closest in time and preceding the transplant listing date.
For the purposes of coding maintenance dialysis and
intravenous (IV) iron status, outpatient CPT codes for
hemodialysis or peritoneal dialysis (90960-63, 90966,
90999, 90945, 90947) and a prescription order for IV
iron at any point during the period 6 months before
transplant listing date through end of the study period
qualified. All laboratory analyses were performed at a
single central laboratory located on the campus of the
Geisinger Medical Center, with the exception of human
leukocyte antigen (HLA) antibody testing, which was
performed at a single external contracting laboratory
using the Luminex®100™ IS Total System analyzer
(Luminex, Inc; Austin, TX, USA), with One Lambda
PRA microcytotoxicity reagent testing (One Lambda, Inc;
Canoga Park, CA, USA). Patients were censored for
death, or at the end of the study period.
The primary exposure of interest was packed red
blood cell transfusion during the period 6 months before
listing date through the end of the study period. For the
purpose of providing a descriptive comparison of the
group transfused vs. those not transfused (as shown in
Table 1), transfusion was treated as a simple binary
stratification term (i.e., if ever transfused during the pe-
riod 6 months prior to listing through the end of the study
period, a subject was grouped in that category). For the
adjusted, time-to-event analysis, however, cumulative
transfusion burden was considered (i.e., each distinct
transfusion event was included in the model in additive
fashion, and only at the time at which it occurred). In this
manner, exposure was defined at the time of transfusion,
rather than assuming a fixed exposure at the start of
transplant wait time. The number of transfused units at
each transfusion occurrence was not considered—each
transfusion event was considered equivalent regardless
of the total number of units transfused. Transfusion was
identified using CPT code 36430 (transfusion of blood or
blood components) during the study period, and was con-
firmed by cross-linking medical record numbers for co-
hort members with Geisinger’s internal blood bank data-
base.
The secondary exposure of interest was any ESA
therapy (prescription order for Epoetin alfa or darbepo-
etin alfa) during the period 6 months before listing date
through the end of the study period. As with transfusion,
ESA therapy was treated as a time-dependent variable in
the Cox proportional hazard model. For ESA, this meant
that a patient with a prescription for ESA was treated as
“exposed” for a period 12 months after that order date,
but unexposed during other periods, if no refill or new
order was placed for the medication. In this manner, gaps
in ESA therapy were accounted for in the Cox model.
The primary study outcome was transplant wait time,
defined as the time in months from index date to date of
deceased donor kidney transplantation.
3. STATISTICAL ANALYSIS
Descriptive statistics, including mean and standard de-
viation (SD) for continuous variables and frequency and
percentage for categorical variables, were presented for

R. M. Perkins et al. / Open Journal of Internal Medicine 2 (2012) 1-6 3
Table 1. Baseline characteristics of patients listed for first deceased donor renal transplant, by blood transfusion status†.
No Transfusion (n = 336) Transfusion (n = 71) P-value
Mean (SD) age at listing date, years 53.3 (13.3) 54.8 (11.4) 0.36
Male sex, n (%) 202 (60.1) 46 (64.8) 0.46
White race, n (%) 307 (91.4) 63 (88.7) 0.48
Mean (SD) BMI, kg/m2 29.0 (6.1) 30.5 (6.6) 0.07
Blood type, n (%) 0.51
A 136 (40.5) 35 (49.3)
AB 16 (4.8) 2 (2.8)
B 38 (11.3) 10 (14.1)
O 144 (42.9) 24 (33.8)
Inactive UNOS status at listing, n (%) 282 (90.1) 64 (92.8) 0.49
Charlson Comorbidity Index score, n (%) 0.04
0 265 (78.9) 46 (64.8)
1 32 (9.5) 11 (15.5)
≥2 39 (11.6) 14 (19.7)
Congestive heart failure, n (%) 10 (3.0) 3 (4.2) 0.71
Cerebrovascular disease, n (%) 7 (2.1) 3 (4.2) 0.39
Diabetes, n (%) 43 (12.8) 16 (22.5) 0.03
Pulmonary disease, n (%) 14 (4.2) 3 (4.2) 0.9
Peripheral vascular disease, n (%) 12 (3.6) 3 (4.2) 0.73
Mean (SD) hemoglobin level, g/dL 12.3 (1.8) 12.1 (1.8) 0.36
Mean (SD) GFR, ml/min/1.73 m2†† 15.5 (5.6) 14.2 (5.2) 0.28
Peak PRA (from listing date to end of follow up)§ 0.77
0% - 9%, n (%) 129 (38.39) 24 (33.80)
10% - 79%, n (%) 36 (10.71) 6 (8.45)
≥80%, n (%) 19 (5.65) 5 (7.04)
Dialysis, n (%) 206 (61.3) 42 (59.2) 0.74
IV iron, n (%) 22 (6.6) 5 (7.0) 0.8
ESA use in prior 6 months, n (%) 80 (23.8) 22 (31.0) 0.21
BMI = body mass index; ESA = erythropoiesis-stimulating agent; GFR = glomerular filtration rate; IV = intravenous; UNOS = United Network for Organ
Sharing. †Transfusion window includes the 6-month period before the transplant listing date through the end of the study period. Lab values and weights were
results closest to listing date. ††Reported for subjects not on dialysis at time of transplant listing. §Data missing for 47.2% of study population.
the study sample stratified by transfusion status. Demo-
graphic and medical characteristics were compared be-
tween the two groups using the 2-sample t- and Pearson
chi-square tests as appropriate. For descriptive purposes,
Kaplan-Meier curves were presented estimating the cu-
mulative incidence of transplant for the two groups.
Cox proportional hazard regression models were used
to estimate the adjusted associations with transplant wait
time. First, univariate regression models were employed.
Variables from the univariate analysis associated with the
outcome at P < 0.10 or that were plausibly associated
with transplant wait time were included in a full multi-
variate Cox regression model to account for possible
confounding effects. In the Cox model, both transfusion
and ESA were treated as two distinct temporal expo-
sures—therapy during the 6-months prior to listing date,

R. M. Perkins et al. / Open Journal of Internal Medicine 2 (2012) 1-6 4
and therapy after the listing date. The results of the mod-
els were expressed as hazard ratios (HRs) with 95% CIs.
A HR greater than one meant the corresponding variable
was associated with higher probability of receiving a
transplant, hence a shorter transplant wait time. SAS®
Version 9.2 (SAS Corporation, Cary, NC, USA) was used
for all analyses.
4. RESULTS
During the study period, 485 patients were listed for first
renal transplantation. Of these, 78 received a living do-
nor kidney and were excluded, leaving 407 patients in
the final study cohort. The median follow up period was
26.3 months, during which 84 patients received a de-
ceased donor renal transplant. 71 patients (17%) received
a total of 162 transfusions during the study period; 7 pa-
tients received a total of 8 transfusions during the 6
month period prior to transplant listing, and 67 patients
received a total of 154 transfusions after the listing date.
Of those transfused after the listing date, 45 (67%) re-
ceived 2 or fewer units, while 22 (33%) received 3 or
more.
At baseline, differences were minimal between those
who received transfusions and those who did not (Table
1). Patients who received a transfusion at any point dur-
ing the study period were more likely to have diabetes
and a greater co-morbid disease burden than patients
who did not. ESA use prior to transplant listing was
similar between those who did and did not receive a
transfusion. There was no statistically significant differ-
ence observed in peak PRA levels during the study pe-
riod between the 2 groups in patients with PRA data
available. Because approximately half of the overall
study population had no recorded PRA level during the
study period, this covariate was not included in adjusted
models.
Among patients who received a transfusion at any
time during the study period, 4 (5.6%) received a de-
ceased donor kidney transplant during the study period,
compared with 80 (23.8%) patients who had no transfu-
sion. Twenty-three (32.4%) deaths occurred while await-
ing renal transplantation in the transfusion group; 69
(20.5%) deaths occurred in the no-transfusion group (P =
0.03).
In the unadjusted analyses examining the association
with time to first renal transplant, higher body mass in-
dex (HR = 0.96; 95% CI, 0.93 - 0.99; P = 0.02), inactive
UNOS status at time of listing (HR = 0.83; 95% CI, 0.75
- 0.91; P < 0.001), and transfusion of packed red blood
cells during the wait list period (HR = 0.18; 95% CI,
0.04 - 0.48; P < 0.001) were significantly associated with
longer transplant wait time. Age, gender, race, weight,
blood type, CCI score, co-morbid conditions, baseline
hemoglobin level, baseline glomerular filtration rate,
baseline dialysis status, prior ESA use, and peak PRA
levels were not statistically significant predictors.
In the multivariate Cox proportional hazard regression
analysis, independent predictors of longer transplant wait
time included baseline inactive UNOS listing status and
transfusion of packed red blood cells after the date of
listing, but not within 6 months prior to the listing date
(Tab le 2). Exposure to ESA prior to the listing date (HR
0.82, 95% CI 0.44 - 1.56, P = 0.55) or during the wait list
period (HR 0.88, 95% CI 0.45 - 1.72, P = 0.7) was not
significantly associated with time to first deceased donor
renal transplant. Co-linearity (between ESA exposure
prior to and after waitlisting, and similarly between
transfusion status prior to and after waitlisting) was as-
sessed. These analyses were nonsignificant. In addition,
as ESA use may be more likely in those requiring a blood
transfusion, the Cox model was repeated after first ex-
cluding anyone who was transfused (n = 71); this did not
substantially alter the association between ESA use and
transplant wait time (data not shown).
Table 2. Multivariate cox proportional hazard analysis on time to first deceased donor renal transplant (N = 407).
Hazard Ratio 95% CI P-value
Inactive UNOS status at listing date 0.81 0.73 - 0.89 <0.001
Blood transfusion (during 6-month period prior to listing date) 0.59 0.14 - 2.46 0.47
Blood transfusion (after listing date), per unit transfused 0.27 0.11 - 0.69 0.01
ESA (during 6-month period prior to listing date) 0.82 0.44 - 1.56 0.55
ESA (after listing date) 0.88 0.45 - 1.72 0.7
Dialysis 0.65 0.39 - 1.09 0.1
IV iron 2.23 0.904 - 5.017 0.08
IV = intravenous; UNOS = United Network for Organ Sharing. Model adjusted for age, race, BMI at listing date, Charlson Comorbidity Index score, and ABO
lood group. ESA status and cumulative transfusion status were time-adjusted as described in the methods. b

R. M. Perkins et al. / Open Journal of Internal Medicine 2 (2012) 1-6 5
5. DISCUSSION
In this health system analysis, blood transfusion after
listing for renal transplant was strongly associated with
prolonged renal transplant wait time; patients who re-
ceived a transfusion after the listing date were more than
70% less likely to receive a deceased donor renal trans-
plant for each unit transfused, compared with those who
were not transfused. There was a trend towards longer
transplant wait time among those transfused during the 6
months prior to wait list date which was not significant,
potentially because of the relatively small number of
transfusions (n = 8) occurring among the cohort during
this time period.
The relationship between blood transfusion and renal
transplant outcomes is complex and controversial. His-
torically, pre-transplant transfusion was thought to confer
a graft-protective effect via induction of immunologic
tolerance [13]. The introduction of improved immuno-
suppressive regimens—specifically calcineurin inhibi-
tor-based regimens—was thought to negate this potential
beneficial effect [14], and the practice has been largely
abandoned. However, studies examining this question
have not addressed the impact of transfusion on trans-
plant wait time, the focus of our study.
While the impact of transfusion on PRA levels has
been studied, [15] and the association between higher
PRA levels and delayed renal transplantation is well-
recognized [6,7], to our knowledge no prior study has
directly assessed the association between transfusion of
packed red blood cells and renal transplant wait time.
Although our study cannot demonstrate causality, the
association between transfusion after listing date and
prolonged wait time remained strong despite the inclu-
sion of other covariates that might be associated with
prolonged transplant wait time.
ESA therapy was not independently associated with
shorter transplant wait time in this analysis. There are
multiple potential explanations for this observation.
ESAs are not immunologically inert; varying effects of
this class of drugs on human T-cell function and cytokine
signaling have been reported [16,17]. However, the net
effect of the cytokine-modulating actions of these drugs
on immune profiles is not clear.
Second, any potential beneficial impact of ESAs on
transplant wait time might be limited to those with higher
peak PRA levels, a subgroup of patients who are under-
represented in our analysis. Several older, small studies
have examined the impact of ESA therapy on PRA levels
among ESRD patients [18-20]. As a whole, these studies
would suggest the impact of ESA therapy on PRA levels
is modest, and perhaps limited to those most highly sen-
sitized [18-20]. The fact that this study cohort included
very few patients with peak PRA levels > 80% might
have limited our ability to demonstrate that impact.
Finally, the effects of confounding by ESA indication
not accounted for in the analysis should be considered.
ESA therapy occurred more frequently among those who
received transfusions than those who did not, though the
difference was not significant. In addition, the effect es-
timates for ESA in the Cox model were qualitatively no
different after excluding those who were transfused from
the analysis.
These findings have potential clinical implications. In
an era of heightened ESA sensitivity due to concerns
about the cardiovascular risks of this medication class, an
increase in transfusions among patients with late stage 4
CKD might be expected. While our findings do not es-
tablish causality between transfusion and longer wait
time, increased reliance upon transfusion as an anemia
management strategy among this population may pro-
long wait time for listed patients. Further, our findings
would not suggest that ESA therapy as a transfusion-
avoidance intervention would shorten time spent on the
wait list. Therefore, as with trends in anemia manage-
ment generally among patients with advanced CKD, tol-
erance of a lower hemoglobin level for the appropriate
waitlisted patient may be the approach with fewest nega-
tive potential consequences. When transfusion is re-
quired, use of leukopore-filtered blood products may
limit the potential immunologic impact.
This study has limitations. Because a subset of the pa-
tient population received maintenance hemodialysis out-
side the Geisinger system, data regarding medication use
and laboratory tests may be incomplete for these patients;
therefore, misclassification bias may have occurred. Al-
though the multivariate model included covariates that
might be associated with transplant waiting time and
transfusion, the potential for residual confounding in this
cohort study persists, particularly for factors related to
unobserved comorbid disease severity. Additional limita-
tions include limited data on PRA levels for study par-
ticipants: PRA results were obtained for all patients who
received blood work, but not all study participants—in
fact nearly half of the population—had these tests per-
formed after the index date, likely because of the rela-
tively large numbers of patients listed as inactive at the
time of transplant listing. Finally, the predominantly
white study population reflects the demographic profile
of central Pennsylvania but is not representative of the
overall US population with advanced CKD, thus limiting
the generalizability of the study findings to more racially
diverse populations.
In conclusion, transfusion of packed red blood cells
after listing for renal transplant strongly correlates with
prolonged transplant wait time among patients listed for
first deceased donor renal transplant.

R. M. Perkins et al. / Open Journal of Internal Medicine 2 (2012) 1-6 6
6. ACKNOWLEDGEMENTS
The investigators thank Amanda Bengier and Ryan Kissinger for assis-
tance with data extraction and programming; Haiyan Sun for assistance
with data analysis; and Zahra Daar, Bradley Moyer, Dikran Toroser,
and Margit Rezabek for editorial assistance. Dr. Perkins has received
research funding and honoraria from American Regent. Dr. Kirchner
has received research funding from American Regent. This project was
funded by Amgen, Inc.
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