PLOS ONE
RESEARCH ARTICLE
Risk factors for graft loss and death among
kidney transplant recipients: A competing risk
analysis
Jessica Pinto-Ramirez 1ID *, Andrea Garcia-Lopez 2ID , Sergio Salcedo-Herrera1,
Nasly Patino-Jaramillo2, Juan Garcia-Lopez3, Jefferson Barbosa-Salinas3, Sergio Riveros-
Enriquez3, Gilma Hernandez-Herrera4, Fernando Giron-Luque5
1 Department of Transplant Nephrology, Colombiana de Trasplantes, Bogotá, Colombia, 2 Department of
Transplant Research, Colombiana de Trasplantes, Bogotá, Colombia, 3 Departmento of Technology and
a1111111111
Informatics, Colombiana de Trasplantes, Bogotá, Colombia, 4 Postgraduate Program in Epidemiology,
a1111111111 Universidad del Rosario – Universidad CES, Bogotá-Medellı́n, Colombia, 5 Department of Transplant
a1111111111 Surgery, Colombiana de Trasplantes, Bogotá, Colombia
a1111111111
a1111111111 * jpinto@colombianadetrasplantes.com
Abstract
OPEN ACCESS
Introduction
Citation: Pinto-Ramirez J, Garcia-Lopez A,
Salcedo-Herrera S, Patino-Jaramillo N, Garcia- Kidney transplantation is the best therapeutical option for CKD patients. Graft loss risk fac-
Lopez J, Barbosa-Salinas J, et al. (2022) Risk tors are usually estimated with the cox method. Competing risk analysis could be useful to
factors for graft loss and death among kidney
determine the impact of different events affecting graft survival, the occurrence of an out-
transplant recipients: A competing risk analysis.
PLoS ONE 17(7): e0269990. https://doi.org/ come of interest can be precluded by another. We aimed to determine the risk factors for
10.1371/journal.pone.0269990 graft loss in the presence of mortality as a competing event.
Editor: Justyna Gołębiewska, Medical University of
Gdansk, POLAND Methods
Received: August 2, 2021 A retrospective cohort of 1454 kidney transplant recipients who were transplanted between
Accepted: June 1, 2022 July 1, 2008, to May 31, 2019, in Colombiana de Trasplantes, were analyzed to determine
risk factors of graft loss and mortality at 5 years post-transplantation. Kidney and patient sur-
Published: July 14, 2022
vival probabilities were estimated by the competing risk analysis. The Fine and Gray method
Peer Review History: PLOS recognizes the
was used to fit a multivariable model for each outcome. Three variable selection methods
benefits of transparency in the peer review
process; therefore, we enable the publication of were compared, and the bootstrapping technique was used for internal validation as split
all of the content of peer review and author method for resample. The performance of the final model was assessed calculating the pre-
responses alongside final, published articles. The diction error, brier score, c-index and calibration plot.
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0269990
Results
Copyright: © 2022 Pinto-Ramirez et al. This is an
open access article distributed under the terms of Graft loss occurred in 169 patients (11.6%) and death in 137 (9.4%). Cumulative incidence
the Creative Commons Attribution License, which for graft loss and death was 15.8% and 13.8% respectively. In a multivariable analysis, we
permits unrestricted use, distribution, and
found that BKV nephropathy, serum creatinine and increased number of renal biopsies
reproduction in any medium, provided the original
author and source are credited. were significant risk factors for graft loss. On the other hand, recipient age, acute cellular
rejection, CMV disease were risk factors for death, and recipients with living donor had bet-
Data Availability Statement: All relevant data are
available on Mendeley: http://dx.doi.org/10.17632/ ter survival compared to deceased-donor transplant and coronary stent. The c-index were
prrjh2f7xf.1. 0.6 and 0.72 for graft loss and death model respectively.
PLOS ONE | https://doi.org/10.1371/journal.pone.0269990 July 14, 2022 1 / 16
PLOS ONE Risk factors for graft loss and death in kidney transplantation
Funding: The authors received no specific funding Conclusion
for this work.
We developed two prediction models for graft loss and death 5 years post-transplantation
Competing interests: The authors have declared
by a unique transplant program in Colombia. Using a competing risk multivariable analysis,
that no competing interests exist.
we were able to identify 3 significant risk factors for graft loss and 5 significant risk factors for
death. This contributes to have a better understanding of risk factors for graft loss in a Latin-
American population. The predictive performance of the models was mild.
Introduction
Kidney transplantation is the optimal renal replacement therapy for suitable patients with end
stage renal failure [1]. Identifying risk factors for graft failure in kidney transplant recipients is
useful for recognizing those patients at high risk and anticipating potential therapeutic inter-
ventions to improve graft survival [2, 3]. Risk factors in the field of organ transplantation are
typically assessed using time-to-event outcomes, for instance, when recording time-to-death
or time-to-graft loss. Survival analyses are key statistical tools in transplantation research [4].
This analyses are the most used methods to estimate the incidence of an outcome of interest,
often censoring for a competing event [4]. For example, death is competing event to graft loss
because a patient may die before losing the graft, as such no opportunity for graft loss in that
case. Thus, competing events are present when another event precludes the event of interest.
In this condition, the Kaplan-Meier (KM) approach is not suitable because this method
assumes that censored patients are at the same risk as patients who remain in the study. In gen-
eral, this leads to an overestimation of the cumulative incidence of the event of interest [5–7].
To solve this limitation, Kalbfleisch and Prentice introduced the Cumulative Incidence Func-
tion (CIF) [8]. The CIF calculates all events’ probability as the sum of the event of interest’s
probabilities and those of the competing risks. The competing risk analysis (CRA) allows using
a modified chi-squared test to compare CIF curves between groups and the Fine and Gray
model with subdistribution hazards (sdHR) [9, 10]. Thus, patients are followed until observing
the first of multiple event types in the CRA. Adjusting this fact, the inferred incidence of the
event of interest is lower, and the sum of calculated incidences across all event types sums up
to 100% [4, 10].
The CRA may provide further insights into the effect of interventions on the separate end-
points, comparing CIF curves to explore the association between covariates and the absolute
risk. Indeed, CRA may be essential for clinical decision-making and prognostic research ques-
tions [11]. Despite this advantage, competing risk models have not been used frequently by
researchers [2, 10]. In particular, the advantage of using CRA method was highlighted in a
study evaluating race, age, and survival among patients undergoing dialysis, where accounting
for transplant as competing risk brought to light a greater disparity in death on dialysis among
younger black patients (related to disparity in access to transplantation) [12]. Other studies
used the CRA method to evaluate risk of mortality and subsequent graft survival in older recip-
ients after sustaining fracture [13], as well as the risk of graft loss and mortality in older recipi-
ents (age�60 y) receiving older kidneys (age�80 y) versus remaining on dialysis [14]. So far,
there are no Latin-American studies related to competing risks analysis in transplantation. In
this context and given the substantial variability of the identified risk factors for graft loss
across different transplant populations, our transplant program (Colombiana de Trasplantes—
CT) aims to use a well characterized Latin-American cohort of kidney transplant recipients
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PLOS ONE Risk factors for graft loss and death in kidney transplantation
with long term follow-up to determine the risk factors for graft loss in the presence of mortality
as a competing risk event.
Materials and methods
Study design and population
We conducted a retrospective cohort study at Colombiana de Trasplantes. To give a context it
must be said that Colombiana de Trasplantes is a transplant network in Colombia with 4 cen-
ters with around 21% of the annual national kidney transplant activity. We included first time,
kidney transplant recipients aged� 18 years who were transplanted between July 1, 2008, to
May 31, 2019. Patients with primary renal graft thrombosis (arterial or venous) were excluded.
Recipients were followed up to graft failure, death, or end of follow up at 5 years post trans-
plantation, whichever was earliest.
Kidney donors
Informed consent was obtained from organ/tissue deceased donors (DD) by a family interview
where both family and the donation team go through information related to answer inquiries
about encephalic death, emotional support, and the possibility of organ donation. The main
causes of death in our DD were cerebrovascular/stroke, followed by head trauma, anoxia, CNS
tumor, or others. Organ donation and tissue consent form is provided in S1 Table in S1 File.
In the case of the live kidney donors, our transplant team provides kidney donation and
nephrectomy informed consent and the affidavit from the live kidney donors. Overall, less
than 1% of our kidney donors were previously registered as organ donors. According to
Colombian law, the total of donor medical costs is covered including organ donor mainte-
nance and procurement with an average of 5000 USD and without any economic contribution
to the family donor.
Immunosuppression and follow-up protocol
All patients received standard induction therapy with alemtuzumab, basiliximab or antihuman
thymocyte immunoglobulin according to immunologic risk or transplant clinical guidelines.
All patients received a fixed-dose of methylprednisolone perioperatively for 3 days with a tran-
sition to fixed-dose oral prednisone from day 4 to day 7 in the postoperative period. One-week
post transplantation steroids were withdrawn. Chronic immunosuppression consists of Calci-
neurin inhibitors-based therapy and mycophenolate mofetil. Patients are monitored closely in
the first 4 weeks post transplantation, and they return for follow-up monthly thereafter.
The acute rejection was classified under parameters described by Banff (2015) [15]. Biopsy
was performed on those patients with increase of serum creatinine by>20% from baseline.
Our center does not perform biopsies per protocol.
Treatment for acute cellular rejection was started once the histological diagnosis was con-
firmed as follows: Methylprednisolone: 500 mg. IV / day in infusion for three days. Oral pred-
nisolone from the fourth day at a dose of 0.5 mg / Kg / day divided into two doses and for two
weeks. After completing the two weeks, a weekly decrease of 10 mg / day was made until reach-
ing the previous dose that was received before the rejection episode. Serum creatinine was
done 5 days after finishing the boluses. A response to corticosteroid treatment was defined
with a decrease in serum creatinine greater than and equal to 20% of the patient’s baseline
creatinine.
Histocompatibility tests performed in our center correspond to Human Leukocyte Anti-
gens (HLA), Panel Reactive Antibodies (PRA) I and II, flow cytometric crossmatch and anti-
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PLOS ONE Risk factors for graft loss and death in kidney transplantation
HLA antibodies (the latter only in living donors when the crossmatch is positive). HLA match-
ing is when the recipient and the donor shared the same HLA antigens (HLA-A,-B,-DR anti-
gens) [16].
We do not perform routine preimplant biopsies. The decision to take or not the organs
from expanded criteria donors is made by macroscopic evaluation of the graft and, if required,
it is sent for histological evaluation. Indices like KDPI / KDRI are not considered for taking or
allocating organs [17]. Organ allocation is made according to the allocation criteria for kidney
transplantation in Colombia [18].
Outcomes
Our primary outcome of interest was graft loss, not including death with function. Graft loss
was defined by center reported as permanent return to dialysis or retransplantation. Death was
defined as mortality from any cause and was ascertained by review of the Colombiana de Tras-
plantes database which records patient’s death and supplemented with the National register
Master File. Patients were censored at 5 years of follow-up since the last follow-up date if they
were transferred to another transplant center or lost to follow-up. Thus, survival analyses were
performed using a competing risk approach, where graft loss and mortality were treated as
competing events.
Statistical analysis
Descriptive analysis. Descriptive statistics were used to report the population characteris-
tics. Frequencies and percentages were used for categorical variables. The numerical variables
were reported according to its distribution using mean and standard deviation for normally
distributed variables, and median and inter-quartile range (IQR) for non-normally distributed
variables. Multiple imputation was not considered as there were few missing values (5.9% of
the total number of cases), and those values were at random. According to this, we performed
a complete case analysis in the univariable and multivariable models.
Predictors. Prespecified variables based on published literature and those available in our
data, were collected as potential risk factors for graft loss. Data collected included demograph-
ics, medical history and clinic characteristics of kidney transplant recipient and donor. Defini-
tion of predictor measurement is provided in Supplementary material (S1 Table in S1 File).
Incidence estimates. The overall incidence of graft loss and/or death at 5 years post trans-
plantation was calculated by Competing risk analysis method (CRA) using cumulative inci-
dence function (CIF) where mortality was treated as a competing risk with graft loss. Log Rank
test for graft loss and death were compared in the entire population and in specific patient
population including living and deceased donor.
Comparisons between the two groups (graft loss yes/no and death yes/no) were performed
using modified χ2 test. The subdistribution Hazard Ratio (sdHR) also known as Fine and Gray
model was calculated for each independent variable and the two outcomes.
Variable selection and prediction. Variables with p value <0.25 in an univariable analy-
sis and those with clinical importance were selected to perform further analysis. Variable selec-
tion to build the final model for graft loss was performed comparing three methods:
1. Full model: contains all the predictors selected in previous analysis and no variable selec-
tion was done.
2. Backward selection based on the Akaike information criterion (AIC).
3. Backward selection based on the Bayesian Information Criterion (BIC).
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PLOS ONE Risk factors for graft loss and death in kidney transplantation
The model was selected on the model´s better performance.
Bootstrapping technique was used for internal validation as split method for resample a sin-
gle dataset to create many simulated samples. The prediction models were trained on B boot-
strap samples with replacement. The models were assessed in the observations that were not
included in the bootstrap sample. This allowed us to calculate the prediction error, brier score
and c-index. Calibration plot was used to compare the predicted probability with the observed
probability.
The Fine and Gray model directly models the covariate effect on CIF, and it reports the
sdHR. To model the impact of covariates on graft loss, we used the Fine and Gray method [9]
for performing competing risk regression. The association between the primary outcome and
the independent variables were assessed by the sdHR.
The model development and report was based on The Transparent Reporting of a multivar-
iable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) [19]. More details of
modeling process can be found in Supplementary material (Modelling process in S1 File).
Analysis was performed using the Software R version 3.6.3. Library used to perform com-
peting risk analysis was cmprsk [20].
Ethics considerations
This study was approved by the ethics research committee of the institution, acting in concor-
dance with local and national regulations, as well as with the Helsinki declaration. Confidenti-
ality of all patients was secured all the time during the execution of the research. None of the
transplant donors was from a vulnerable population and all donors or next of kin provided
written informed consent that was freely given.
Results
Patient characteristics
A total of 1454 out of 1621 recipients met selection criteria. Exclusions took place in 167 (113
pediatric transplants and 54 kidney transplants with graft thrombosis). In gender distribution
most of patients were male, the overall mean age was 43.58 ± 13 years. Table 1 summarizes the
clinical and demographic characteristics.
Overall cumulative incidence
During the follow-up period graft loss occurred in 169 patients (11.6%) and death occurred in
137 (9.4%). Cumulative incidence for graft loss and death was 15.8% and 13.8% respectively.
Fig 1 displays the combined cumulative incidence for the entire cohort. Significant differences
in estimates of both outcomes were found when analyzing live and deceased transplant sepa-
rately, where deceased transplant (17.1% and 16.3% for death and graft loss respectively) had
greater cumulative incidences (deceased transplant 17.1% and 16.3% for death and graft loss
respectively vs live 5.4% and 15% for death and graft loss respectively). Fig 2 shows the differ-
ence between type of transplant in the cumulative incidence of graft loss and death.
Risk factors for cumulative incidence of graft loss and death with
functioning graft
We fit the Fine and Gray competing risk survival regression model for identifying the potential
determinants of graft loss using covariates with significant association and those with clinical
importance. The covariates that had a significant impact on the graft loss were stroke, cold
ischemia time, qualitative PRA II, BKV nephropathy, number of allograft biopsies, acute
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PLOS ONE Risk factors for graft loss and death in kidney transplantation
Table 1. Clinical and demographic characteristics of kidney transplant recipients.
Variable Total Graft loss P-value Death P-value
No Yes No Yes
(N = 1454) (N = 1285) (N = 169) (N = 1317) (N = 137)
Sex, n (%) 0.495 0.323
Female 586 (40.3) 525 (40.9) 61 (36.1) 539 (40.9) 47 (34.3)
Male 868 (59.7) 760 (59.1) 108 (63.9) 778 (59.1) 90 (65.7)
Age, mean (SD) 43.6 (13.2) 43.6 (13.1) 43.2 (14.3) 0.926 42.8 (13.2) 50.9 (11.3) <0.001
BMI, mean (SD) 23.3 (3.82) 23.3 (3.79) 23.2 (4.04) 0.988 23.2 (3.81) 24.1 (3.78) 0.030
Missing 49 (3.4) 39 (3.0) 10 (5.9) 47 (3.6) 2 (1.5)
Cause of CKD, n (%) 0.946 0.076
Congenital 96 (6.6) 84 (6.5) 12 (7.1) 90 (6.8) 6 (4.4)
Unknown 638 (43.9) 569 (44.3) 69 (40.8) 583 (44.3) 55 (40.1)
Diabetic 200 (13.8) 174 (13.5) 26 (15.4) 167 (12.7) 33 (24.1)
Glomerular 272 (18.7) 241 (18.8) 31 (18.3) 256 (19.4) 16 (11.7)
Hypertensive 163 (11.2) 145 (11.3) 18 (10.7) 144 (10.9) 19 (13.9)
Obstructive 37 (2.5) 34 (2.6) 3 (1.8) 35 (2.7) 2 (1.5)
Other 48 (3.3) 38 (3.0) 10 (5.9) 42 (3.2) 6 (4.4)
RRT, n (%) 0.266 0.213
Hemodialysis 618 (42.5) 531 (41.3) 87 (51.5) 554 (42.1) 64 (46.7)
Peritoneal 447 (30.7) 406 (31.6) 41 (24.3) 415 (31.5) 32 (23.4)
Pre-Dialysis 181 (12.4) 165 (12.8) 16 (9.5) 168 (12.8) 13 (9.5)
Unknown 208 (14.3) 183 (14.2) 25 (14.8) 180 (13.7) 28 (20.4)
Time on dialysis, months (SD) 27.2 (35.4) 26.7 (34.7) 30.8 (40.4) 0.421 26.6 (35.5) 33.5 (34.2) 0.149
Time on waiting list, months (SD) 554 (596) 561 (602) 484 (541) 0.423 547 (594) 615 (623) 0.510
Medical history n (%)
CVD 47 (3.2) 38 (3.0) 9 (5.3) 0.262 41 (3.1) 6 (4.4) 0.728
Stroke 13 (0.9) 8 (0.6) 5 (3.0) 0.010 12 (0.9) 1 (0.7) 0.977
Hypertension 1162 (79.9) 1033 (80.4) 129 (76.3) 0.465 1045 (79.3) 117 (85.4) 0.242
DM 205 (14.1) 179 (13.9) 26 (15.4) 171 (13.0) 34 (24.8) <0.001
Smoking 210 (14.4) 184 (14.3) 26 (15.4) 0.934 186 (14.1) 24 (17.5) 0.561
Type of donor, n (%) 0.176 <0.001
DD 1002 (68.9) 875 (68.1) 127 (75.1) 881 (66.9) 121 (88.3)
LD 452 (31.1) 410 (31.9) 42 (24.9) 436 (33.1) 16 (11.7)
ECD, n (%) 189 (13.0) 158 (12.3) 31 (18.3) 0.157 155 (11.8) 34 (24.8) <0.001
CIT, hours mean (SD) 18.3 (14.4) 17.8 (13.3) 21.3 (20.3) 0.039 18.3 (15.1) 18.3 (7.74) 1
CIT >14 hours, n (%) 675 (46.4) 573 (44.6) 102 (60.4) <0.001 588 (44.6) 87 (63.5) <0.001
Match, n (%) 0.808 0.628
0 117 (8.0) 105 (8.2) 12 (7.1) 110 (8.4) 7 (5.1)
1 202 (13.9) 175 (13.6) 27 (16.0) 183 (13.9) 19 (13.9)
2 298 (20.5) 258 (20.1) 40 (23.7) 267 (20.3) 31 (22.6)
3 498 (34.3) 447 (34.8) 51 (30.2) 457 (34.7) 41 (29.9)
4 220 (15.1) 194 (15.1) 26 (15.4) 189 (14.4) 31 (22.6)
5 69 (4.7) 60 (4.7) 9 (5.3) 62 (4.7) 7 (5.1)
6 40 (2.8) 39 (3.0) 1 (0.6) 39 (3.0) 1 (0.7)
Missing 10 (0.7) 7 (0.5) 3 (1.8) 10 (0.8) 0 (0)
Qualitative PRA I, n (%) 0.321 0.477
Negative 748 (51.4) 663 (51.6) 85 (50.3) 685 (52.0) 63 (46.0)
Positive 78 (5.4) 63 (4.9) 15 (8.9) 73 (5.5) 5 (3.6)
(Continued)
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PLOS ONE Risk factors for graft loss and death in kidney transplantation
Table 1. (Continued)
Variable Total Graft loss P-value Death P-value
No Yes No Yes
(N = 1454) (N = 1285) (N = 169) (N = 1317) (N = 137)
Unknown 628 (43.2) 559 (43.5) 69 (40.8) 559 (42.4) 69 (50.4)
Qualitative PRA II, n (%) <0.001 0.859
Negative 95 (6.5) 69 (5.4) 26 (15.4) 87 (6.6) 8 (5.8)
Positive 736 (50.6) 662 (51.5) 74 (43.8) 672 (51.0) 64 (46.7)
Unknown 623 (42.8) 554 (43.1) 69 (40.8) 558 (42.4) 65 (47.4)
CMV Disease, n (%) 76 (5.2) 62 (4.8) 14 (8.3) 0.165 58 (4.4) 18 (13.1) <0.001
BKV nephropathy, n (%) 36 (2.5) 21 (1.6) 15 (8.9) <0.001 35 (2.7) 1 (0.7) 0.385
Number of renal allograft biopsies, n (%) 1.01 (1.30) 0.874 (1.20) 2.08 (1.54) <0.001 0.995 (1.30) 1.20 (1.36) 0.201
Acute cellular rejection, n (%) 473 (32.5) 368 (28.6) 105 (62.1) <0.001 413 (31.4) 60 (43.8) 0.012
Serum creatinine at 12 months, mean (SD) 1.51 (0.848) 1.42 (0.588) 2.73 (2.10) <0.001 1.48 (0.851) 1.84 (0.748) 0.004
Coronary stent 13 (0.9) 12 (0.9) 1 (0.6) 0.906 9 (0.7) 4 (2.9) 0.030
Number of hospital readmissions, mean (SD) 1.30 (1.81) 1.20 (1.77) 2.07 (1.92) <0.001 1.26 (1.79) 1.70 (2.02) 0.024
SD: standard deviation, BMI: Body Mass Index, RRT: Renal Replacement Therapy, CVD: Cardiovascular Disease, DM: Diabetes Mellitus. DD: Deceased donor, LD: Live
donor, ECD: Expanded criteria donor, CIT: Cold isquemia time, PRA: Panel Reactive Antibody Test, BMI: Body Mass Index, CMV: citomegalovirus, BKV: BK virus.
Negative PRA test is indicative of a lack of anti-HLA antibodies.
https://doi.org/10.1371/journal.pone.0269990.t001
Fig 1. Cumulative incidence of graft loss and death estimated by the method for competing risk.
https://doi.org/10.1371/journal.pone.0269990.g001
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PLOS ONE Risk factors for graft loss and death in kidney transplantation
Fig 2. Log Rank of cumulative incidence of risk of death by type of transplant, and Log Rank of cumulative incidence of risk graft loss by type of
transplant.
https://doi.org/10.1371/journal.pone.0269990.g002
cellular rejection, serum creatinine at 12 months and number of hospital readmissions. The
covariates that had a significant impact on death were recipient age, diabetes mellitus, type of
donor, expanded criteria donor, CMV disease, cold ischemia time, coronary stent, and num-
ber of hospital readmissions.
Table 2 provides the sdHR of risk factors estimated by using the final multivariate Fine and
Gray model. The risk of graft failure was noted to increase in the presence of nephropathy due
BK virus, higher rates of serum creatinine at 12 months post transplantation, and greater num-
ber of kidney biopsies. Significant risk factors associated with cumulative incidence of death
were recipient age, deceased donor, CMV disease, coronary stent, and acute cellular rejection.
Variable selection to build the final model for graft loss was performed comparing three
methods:
4. Full model: contains all the predictors selected in previous analysis and no variable selec-
tion was done. 2. Backward selection based on the AIC. 3. Backward selection based on
the BIC.
The performance and prediction error of the three models were evaluated using Bootstrap
cross-validation, showing similar results for the AIC and BIC models. The C-index for the full
model was 0.57, for the AIC model was 0.6 and, for the BIC model was 0.6.
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PLOS ONE Risk factors for graft loss and death in kidney transplantation
Table 2. Factors associated with graft loss using death as a competing risk in kidney transplant recipients in a final Fine and Gray model.
Characteristic Graft loss outcome p-value Death outcome p-value
sdHR CI 95% sdHR CI 95%
BKV nephropathy 4.43 2.02–9.72 <0.001 - - -
Serum creatinine at 12 months 1.76 1.55–2.00 <0.001 - - -
Number of renal allograft biopsies 1.45 1.28–1.64 <0.001 - - -
Recipient age (years) - - - 1.039 1.02–1.05 <0.001
Living donor (Vs deceased) - - - 0.386 0.21–0.68 <0.001
CMV Disease - - - 2.459 1.46–4.11 <0.001
Coronary stent - - - 3.032 0.99–9.23 0.05
Acute cellular rejection, n (%) - - - 1.336 0.93–1.90 0.11
sdHR: subhazard distribution; CI: Confidence interval
https://doi.org/10.1371/journal.pone.0269990.t002
The same process for variable selection and performance assessment was performed for
death model. Similar results were obtained for the AIC and BIC models. The C-index for the
full model was 0.78, for the AIC model was 0.72 and, for the BIC model was 0.72. Fig 3 pro-
vides the prediction errors and calibration plot of the final Fine and Gray model for graft loss.
Fig 4 provides the prediction errors and calibration plot of the final Fine and Gray model for
death.
Discussion
Kidney transplantation is the best therapy available for most patients with end- stage kidney
disease [21]. We developed two predictive models of risk of graft loss and risk of death in kid-
ney transplant patients. Risk prediction models are useful for identifying kidney recipients at
high risk of graft failure, and optimize clinical care, decision-making and resource allocation;
is a challenging issue in kidney transplantation [2]. Our objective was characterized Latin-
American cohort of kidney transplant recipients with long term follow-up and to predict the
risk factors for graft loss in the presence of mortality as a competing risk event. We were able
to identify 3 significant risk factors for graft loss and 5 significant risk factors for death. This
contributes to have a better understanding of risk factors for graft loss in a Latin-American
population.
Fig 3. Prediction errors and calibration plot of the final Fine and Gray model for graft loss.
https://doi.org/10.1371/journal.pone.0269990.g003
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PLOS ONE Risk factors for graft loss and death in kidney transplantation
Fig 4. Prediction errors and calibration plot of the final Fine and Gray model for death.
https://doi.org/10.1371/journal.pone.0269990.g004
Graft survival is one of the most critical concerns in kidney transplant recipients, and our
ability to accurately monitor the cumulative incidence of graft loss its importance. Risk predic-
tion models are useful for identifying kidney recipients at high risk of graft failure, thus opti-
mizing clinical care. Therefore, using competing risks methods that provide more accurate
estimates, we sought to identify risk factors leading to graft loss, considering death as a com-
peting risk in kidney transplant recipients [4].
Particularly, one study highlights the advantage of using CRA method assessing both the
probabilities of death and graft loss.
Risk factors for graft loss
Late failure of kidney transplants remains an important clinical problem [2, 22]. Renal allograft
loss is multifactorial [23]. In the United States, 5469 kidney transplants developed end-stage
kidney failure in 2008 (data provided by Jon Snyder from USRDS), making kidney transplant
failure the fourth leading cause of end-stage renal disease. The reasons for failure are not well
understood. Some have postulated that late deterioration reflects dysregulated fibrosis, drug
toxicity or progressive “chronic allograft nephropathy” [24–26]. In our study we found that
BKV nephropathy, serum creatinine at 12 months and increased number of renal allograft
biopsies were significant risk factors for graft loss. Sellarés et al., attributed causes of graft fail-
ure in the biopsy-for-cause population to antibody-mediated rejection (ABMR), probable
ABMR or mixed rejection, with nonadherence recorded in nearly half. There was evidence of
ABMR in 18 of 19 nonadherent patients who failed. There were also three groups of nonrejec-
tion causes of failures: glomerulonephritis, BKV nephropathy and failure in the context of an
intercurrent illness. The results emphasize the burden of ABMR and mixed rejection and its
interaction with nonadherence in observed failures, making these key targets for further prog-
ress. The results also illustrate the range of clinical courses leading to failure and the some-
times-complex relationships to the indication biopsy findings [22].
On the other hand, renal allograft biopsy (RAB) is still the best approach to diagnose renal
transplant complications [27]. We found that kidney recipients with more significant require-
ments to perform RAB had greater risk of graft loss. According to our guidelines, biopsy was
performed on those patients with increase of serum creatinine by>20% from baseline, gener-
ally when acute or chronic renal allograft rejection is suspected, antibody-mediated rejection,
polyoma virus nephropathy, glomerular diseases, atrophy u other. Thus, greater number of
RAB may be associated with renal allograft disfunction or detection of other lesions that may
PLOS ONE | https://doi.org/10.1371/journal.pone.0269990 July 14, 2022 10 / 16
PLOS ONE Risk factors for graft loss and death in kidney transplantation
influence graft loss [27–29]. However, as a limitation, we do not have an electronic database of
all the histological findings of dysregulated fibrosis and C4d of the renal biopsies of our
patients. Of those factors related to graft loss, there is a high magnitude of association with
BKV nephropathy [30, 31]. Previous studies have associated the BKV nephropathy with pre-
mature loss of kidney function [32–35], graft loss and alteration of renal histology [21–23, 36].
The reactivation of the virus may occur with the use of immunosuppression. Polyomavirus BK
virus reactivation in kidney transplant recipients can lead to BK polyoma virus-associated
nephropathy (BKPyVAN), which is associated with graft dysfunction in >90% and graft loss
in over 50% of the affected individuals [37].
Our results also showed an association with higher serum creatinine level at 12 months.
This factor has been widely described as predictor of graft loss [3, 38–45].
The identification of risk factors for graft loss in the long term has been provided by several
studies, however, there is substantial variability in data collection, the methods used for model
development and included predictors [2]. Among others, the most described predictors are:
chronic dysfunction [38, 42, 46], episodes of acute rejection [3, 38–41], death with functional
graft [38, 46], glomerulonephritis [38], donor age [47], hypertension [47, 48], diabetes [41, 47],
type of immunosuppression [47], delayed graft function [47], recipient age [3], race [3], albu-
min [3], proteinuria [3, 42, 47], low-density lipoprotein (LDL) cholesterol levels [48] and higher
BMI [49]. However, some of them included in the analysis but that finally were not significant.
Risk factors for death
Identification and quantification of the relevant factors for death can improve patients’ individ-
ual risk assessment and decision-making [50]. In this study we confirm risk factors for death like
recipient age, deceased donor, CMV disease, CMV disease, coronary stent, acute cellular rejec-
tion. Our findings show that older recipients are more likely to die, which is consistent with sev-
eral published studies that report youngest age groups demonstrated a clear trend toward lower
mortality compared with those�60–65 years [50–53]. However, it must be said that long term
patient survival in the elderly has been shown to be significantly better in transplant patients
compared with remaining on the waitlist [54–57]. Similarly to what happens with large series
(Collaborative Transplant Study [58] and UNOS Register [59]), it is observed that living-donor
kidney transplantation provides better outcomes than deceased-donor transplantation. Besides,
it is associated with shorter transplant waiting list period and better early outcome [60]. We have
found that CMV disease represents a risk of death in our population. This is one of the most
important infectious complications in transplant recipient leading to significant morbidity and
mortality [61]. Various direct and indirect detrimental effects occur because of CMV infection
on patients and grafts. Indirect effects may include rejection, immunosuppression resulting in
infections by other microorganisms, graft dysfunction, and poor survival of the kidney graft [62].
Cardiovascular disease (CVD) is frequent after kidney transplantation, is a major cause of
morbidity and of death with functioning graft in recipients [63, 64]. We found as a risk factor
in the model that coronary disease, specifically coronary stent placement, as a risk factor for
death. OPTN/SRTR 2017 Annual Data Report: Kidney, Death with a functioning graft is the
leading cause of graft loss in kidney transplant recipients, and a major cause of death is cardio-
vascular disease, accounting for about one third of known causes [65]. Another of the factors
related in the model with the death of kidney transplant patients that we found was the pres-
ence of Acute Rejection (AR). Clayton et al., proposed that AR and its treatment may directly
or indirectly affect longer-term outcomes for kidney transplant recipients, they found AR was
also associated with death with a functioning graft (HR, 1.22; 95% CI, 1.08 to 1.36), and with
death due to cardiovascular disease (HR, 1.30; 95% CI, 1.11 to 1.53) and concluded AR is
PLOS ONE | https://doi.org/10.1371/journal.pone.0269990 July 14, 2022 11 / 16
PLOS ONE Risk factors for graft loss and death in kidney transplantation
associated with increased risks of longer-term graft failure and death, particularly death from
cardiovascular disease and cancer. The results suggest AR remains an important short-term
outcome to monitor in kidney transplantation [66].
Previous studies have attempted to identify and integrate risk factors for death into predic-
tive models, including the pre-transplant variables gender, race, body mass index (BMI), time
on dialysis, cause of end-stage renal disease, panel reactive antibodies, HLA mismatches,
comorbidities such as diabetes, and heart failure, and donor age. In some models, the post-
transplant factors Delayed graft function (DGF), and graft function were included [50], how-
ever, in our study population these were not significant. We think that in the case of diabetes
the sample size was not sufficient.
Strengths and limitations
Unlike most previous studies, the main strength of this study is that our analysis includes a
competing risk model. Many papers have pointed out the important issue of competing events
in kidney transplantation [2, 4, 5, 7]. This method allowed us to determine graft loss risk fac-
tors differentiating those who increase recipient mortality. We believe that this integral view is
best suited to a rational and patient-centered risk assessment.
Further, our cohort is the largest reporting risk factors for graft loss and death by a unique
transplant program in Colombia and contributes to have a better understanding of Latin-
American population as most of previous studies have been reported by transplant programs
that treat mainly Caucasian patients. Other strengths include consistent data collection with a
high degree of completeness and several variables.
Potential limitations attendant with the nature of data collection. The retrospective nature
of our study prohibited adjusting for unmeasured confounding factors that may explain the
association between independent factors and adverse graft outcomes. Besides, donor age was
no considered in our analysis due to no available information.
On the other hand, variable selection with backward regression is not ideal. A fundamental
problem with stepwise regression is that some real explanatory variables that have causal effects
on the dependent variable may happen to not be statistically significant, while nuisance variables
may be coincidentally significant. As a result, the model may fit the data well in-sample but do
poorly out-of-sample. Unfortunately, penalized methods for Fine-Gray models have some limi-
tations and the output from the crrp () function does not include convenient parameters such as
a p-value. In addition, this package has not been maintained since its first commit in 2015.
We did not perform external validation, and this could be useful to assess the generalizabil-
ity to other similar populations.
Conclusion
In summary, we found that stroke, BKV nephropathy, serum creatinine at 12 months and
increased number of renal allograft biopsies were significant risk factors for graft loss. On the
other hand, recipient age, acute cellular rejection, CMV disease were risk factors for death, and
recipients with living donor had better survival compared to deceased-donor transplant and
coronary stent. This contributes to have a better understanding of Latin-American population.
However, the predictive performance of the models was mild.
Supporting information
S1 File.
(DOCX)
PLOS ONE | https://doi.org/10.1371/journal.pone.0269990 July 14, 2022 12 / 16
PLOS ONE Risk factors for graft loss and death in kidney transplantation
Acknowledgments
We are grateful with Colombiana de Trasplantes to make this study possible.
Author Contributions
Conceptualization: Jessica Pinto-Ramirez, Andrea Garcia-Lopez, Sergio Salcedo-Herrera,
Gilma Hernandez-Herrera, Fernando Giron-Luque.
Data curation: Jessica Pinto-Ramirez, Andrea Garcia-Lopez, Sergio Salcedo-Herrera, Nasly
Patino-Jaramillo, Juan Garcia-Lopez, Jefferson Barbosa-Salinas, Sergio Riveros-Enriquez.
Formal analysis: Jessica Pinto-Ramirez, Andrea Garcia-Lopez, Nasly Patino-Jaramillo, Juan
Garcia-Lopez, Jefferson Barbosa-Salinas, Sergio Riveros-Enriquez, Gilma Hernandez-
Herrera.
Investigation: Andrea Garcia-Lopez, Gilma Hernandez-Herrera, Fernando Giron-Luque.
Methodology: Jessica Pinto-Ramirez, Andrea Garcia-Lopez, Sergio Salcedo-Herrera, Juan
Garcia-Lopez, Jefferson Barbosa-Salinas, Sergio Riveros-Enriquez, Gilma Hernandez-
Herrera.
Project administration: Nasly Patino-Jaramillo, Fernando Giron-Luque.
Supervision: Jessica Pinto-Ramirez, Sergio Salcedo-Herrera, Gilma Hernandez-Herrera, Fer-
nando Giron-Luque.
Validation: Jessica Pinto-Ramirez, Andrea Garcia-Lopez, Gilma Hernandez-Herrera, Fer-
nando Giron-Luque.
Visualization: Jessica Pinto-Ramirez.
Writing – original draft: Jessica Pinto-Ramirez, Andrea Garcia-Lopez, Nasly Patino-Jara-
millo, Juan Garcia-Lopez.
Writing – review & editing: Jessica Pinto-Ramirez, Andrea Garcia-Lopez, Nasly Patino-
Jaramillo.
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