Abstract
Objectives
The disparity between kidney demand and supply necessitates the expansion of the donor pool. This study evaluates the long-term outcomes of single kidney transplantation guided by histological and clinical parameters.
Methods
We retrospectively analyzed 1,024 adult recipients of deceased-donor kidney transplants from January 2011 to December 2020. Graft and patient survival were assessed using Kaplan-Meier analysis, and independent risk factors were identified through Cox regression models. Donor kidney histological specimens were evaluated using the Remuzzi score.
Results
A Remuzzi score of 4 emerged as a critical threshold for safe single kidney transplantation. Recipients were divided into three groups based on Remuzzi score (0–3, 4, and >4). Those with a score of 4 had similar 10-year graft survival to those with score 0–3 (92.0% vs. 92.0%, p = 0.984), whereas grafts with score >4 had poorer outcomes (82.0%, p = 0.033). The 10-year patient survival for recipients with a score of 4 was comparable to those with scores 0–3 (90.0% vs. 94.0%, p = 0.122), while score >4 trended toward worse survival (81.0%, p = 0.067). In subgroup analyses of high Remuzzi scores (>4, n = 105), the Kidney Donor Profile Index (KDPI) and donor terminal creatinine were identified as independent risk factors for graft loss.
Conclusions
Single kidney transplantation is safe for grafts with Remuzzi scores ≤4. Even some kidneys from high-score donors demonstrated favorable graft prognosis when allocated based on KDPI and donor terminal creatinine.
Keywords: Expanded criteria donors/ECD, graft survival, histopathology, remuzzi score, single kidney transplantation
Introduction
Kidney transplantation (KT) is the most effective treatment for end-stage renal disease (ESRD), offering superior survival and quality of life compared to dialysis. However, the growing demand for kidneys far exceeds the available supply. In 2022, the United States recorded 44,187 new candidates on the kidney waiting list, yet only 26,309 transplants were performed [1]. This disparity underscores the urgent need to expand the donor pool by utilizing marginal kidneys, which are often discarded due to perceived inferior quality. Expanded criteria donor (ECD) kidneys represent an important source for alleviating the shortage of donors, although the discard rate of ECD kidneys was significantly higher than that of non-ECD donors [2]. One practical strategy to safely transplant more marginal kidneys is the adoption of pre-implantation histological assessment of chronic kidney injury. Established by Remmuzi and colleagues [3,4], this method scores biopsies (0–12) based on the presences of: glomerular sclerosis; tubular atrophy; interstitial fibrosis; and atherosclerosis, assigning a score of 0–3 for each component. According to the current proposal, kidneys with a summed score of 0–3 are used for single kidney transplants, those with scores of 4–6 are used for dual kidney transplants, and those with a score of > 6 should be discarded. However, this approach has been challenged, when kidneys are scored as ‘untransplantable’ (Remuzzi score > 6), surgeons use these kidneys for dual kidney transplantation which significantly waste the source of kidneys. Some ‘untransplantable’ kidneys may still function well with single kidney transplantation if other clinical parameters compensate for the high Remuzzi score.
Our prior study investigated the predictive value of postreperfusion biopsy on long-term allograft outcome after single-kidney transplantation [5]. However, the study did not clearly establish whether kidneys with Remuzzi scores >3 could still achieve favorable outcomes when used for single transplantation. This uncertainty highlights the need for further research to refine the criteria for utilizing high-score kidneys.
We hypothesize that a combined histopathological and clinical assessment could safely expand the donor pool without compromising graft or patient survival. Depending on our previous findings, we aim to provide more precise guidelines for the allocation of marginal kidneys, particularly those with Remuzzi scores >3.
Materials and methods
Study population
Donor grafts were allocated by the Organ Procurement Organization (OPO) through the China Organ Transplant Response System, an encrypted national electronic network designed to ensure fairness and transparency in the organ allocation process [6]. We have obtained related statements from the OPO and the Organ Transplant Ethics Committee (supporting OPO and documents of institutional review board are available online, as supplementary material). Autonomous consent, free from coercion, was obtained from the donors or their next of kin; and all organs/tissues were not sourced from executed prisoners or prisoners of conscience. Our center was among the first institutions in China to adopt deceased donor kidney transplantation. This research was approved by the ethics committee of the First Affiliated Hospital of Zhejiang University school of medicine (Ref: IIT20241458A). Written informed consent was waived because the retrospective nature of the study. All procedures followed the guidelines of the 2000 Declaration of Helsinki and the 2018 Declaration of Istanbul.
We analyzed data from the OPO and the transplantation follow-up system of the Kidney Disease Center of the First Affiliated Hospital of Zhejiang University of medicine. We identified all deceased donor kidney transplants between January 2011 and December 2020 (n = 1702) and excluded cases without postreperfusion renal allograft biopsies (n = 75). After further exclusions based on predefined criteria, we finally analyzed 1024 transplants (Figure 1).
Figure 1.
Flow diagram of patient enrollment. A total of 1702 kidney transplants were performed from January 2011 to December 2020 in our center. According to the inclusion and exclusion criteria, all enrolled 1024 kidneys from 670 deceased donors were transplanted to ESRD patients with single kidney transplantation.
Histopathological evaluation
Postreperfusion biopsies were performed immediately after vascular anastomosis using a 16-gauge Tru-Cut needle. Tissue samples were fixed in formalin, embedded in paraffin, and stained with hematoxylin and eosin (H&E), periodic acid-Schiff (PAS), Masson’s trichrome, and Jones methenamine silver. According to the Remuzzi scoring system [3,4], a centralized and blinded histopathological evaluation was conducted by experienced renal pathologists at our institution to ensure consistency in assessment. Sample contents of glomerular ≥ 10 and artery ≥ 1 were qualified grafts according to the Banff recommendations [7]. We assessed the degree of glomerular sclerosis (%), tubular atrophy (%), renal interstitial fibrosis (%), renal interstitial nuclear cell infiltration (%), acute tubular injury (%), arteriolar hyalinosis (%), and the number of microthrombus formation in the renal arteries (%).
Clinical variables
Primary endpoints were graft failure and patient death. Secondary endpoints included: delayed graft function (DGF), primary nonfunction (PNF), and estimated glomerular filtration rate (eGFR). DGF was defined as requiring dialysis within the first postoperative week. PNF was defined as a graft that never achieved sufficient function to discontinue dialysis. eGFR (expressed as mL/min/1.73 m2) was calculated using the CKD-EPI formula [8] at 1 year post-transplantation. Immunosuppression was administered according to standard protocols, as previously described [9–11].
Statistical analysis
Statistical analyses were performed using R version 4.4.0 [12]. Continuous variables were expressed as mean ± standard deviation or median with interquartile range (IQR), and comparisons between groups were performed using Student’s t test or Mann-Whitney U test. Categorical variables were expressed as counts and percentages, and differences between groups were analyzed using chi-square tests or Fisher’s exact tests.
Allograft outcomes were assessed using Kaplan-Meier survival curves and the log-rank test. Univariate and multivariate Cox proportional hazards models were used to analyze risk factors for death-censored graft loss. Candidate variables with p < 0.10 in univariate analysis were included in multivariable models. Cutoffs for the continuous variables (Remuzzi score) were selected using receiver operating characteristic (ROC) curve analysis to maximize sensitivity and minimize the false positive rates. The predictive performance of the Remuzzi score and Kidney Donor Profile Index (KDPI) was evaluated using ROC curves, and the area under the curve (AUC) was calculated. A two-tailed p < 0.05 was considered statistically different.
Results
Clinical and biopsy data
A total of 1,024 deceased donor renal grafts were implanted as single kidney transplants at our center according to the inclusion and exclusion criteria (Figure 1). Recipients were followed for at least 4 years; the median follow-up time was 6.7 (IQR 5.1–8.5) years, the maximum follow-up duration was 13.3 years. 71 (6.9%) grafts failed (the causes of graft failure were listed in Supplementary Table S1), 27 (2.6%) cases died with a functional graft and 93 (9.0%) cases were lost to follow-up (the Supplementary Table S2 reported the baseline characteristics). The delayed graft function occurred in 18.5% of cases. Ten-year graft survival was 91% by Kaplan-Meier analysis.
Of these, the mean donor age was 43.2 ± 13.3 years, 80.0% were male, 853 (83.3%) recipients were transplanted with kidneys from DCD donors, 171 (16.7%) from DBD donors and 187 (18.3%) recipients were transplanted with kidneys from donors fulfilling ECD criteria (Table 1) [2]. The terminal creatinine was 1.2 ± 1.0 mg/dL, with 21.9% donors having terminal creatinine > 1.5 mg/dL. Hypertension was present in 18% and diabetes in < 1%. The mean KDPI was 51.2 ± 25.7%. Recipients had a mean age of 44.6 ± 10.1 years, and 60.0% were male. The median Remuzzi score was 2 (IQR 0–3). Detailed baseline characteristics are presented in Table 1, and histopathological data are summarized in Table 2.
Table 1.
Baseline characteristics of donors (n = 670) and recipients (n = 1024).
| Characteristic | Total |
|---|---|
| Donor characteristics (n = 670) | |
| Age (yr) | 43.2 ± 13.3 |
| Male (%) | 819 (80.0) |
| Terminal creatinine (mg/dl) | 1.2 ± 1.0 |
| Terminal creatinine > 1.5 mg/dl (%) | 224 (21.9) |
| BMI (kg/m2) | 23.1 ± 3.2 |
| Hypertension (%) | 184 (18.0) |
| Death from cerebrovascular disease (%) | 344 (33.6) |
| Expanded criteria donor (%) | 187 (18.3) |
| KDRIa | 1.1 ± 0.3 |
| KDPIb (%) | 51.2 ± 25.7 |
| KDPI ≥ 85% (%) | 122 (11.9) |
| Recipient characteristics (n = 1024) | |
| Age at transplant (yr) | 44.6 ± 10.1 |
| Male (%) | 612 (59.8) |
| BMI (kg/m2) | 21.4 ± 3.1 |
| Secondary kidney transplantation (%) | 11 (1.1) |
| Transplant characteristics | |
| HLA mismatches | 2.8 ± 1.4 |
| CIT, h | 7.5 ± 3.7 |
| Median follow-up post-transplant (yr) | 6.7 (5.1, 8.5) |
Note: Continuous variables are shown as mean ± standard deviation; categorical variables are reported as column percentages; median follow up is reported as median (interquartile range); BMI, body mass index; ECD, expanded criteria donor; KDRI, kidney donor risk index; KDPI, kidney donor profile index; CIT, cold ischemia time; IQR, interquartile range; DGF, delayed graft function; ECD, expanded criteria donor; HLA, human leukocyte antigen.
aKDPI calculations were according to the following: https://optn.transplant.hrsa.gov/data/allocation-calculators/kdpi-calculator/.
The KDRI score was calculated on the basis of the following donor parameters: age, height, weight, history of hypertension, history of diabetes, cause of death (cerebral stroke), serum creatinine at donation, HCV serostatus, and donation after circulatory death status.
bthe KDPI is the percentile rank (from the 1st to the 100th) of the KDRI with reference to a given OPTN deceased donor cohort.
Table 2.
Pathologic characteristics of cohorts (n = 1024).
| Histopathological findings | Total |
|---|---|
| Glomeruli number median (IQR) | 15 (12, 20) |
| Glomerulosclerosis % median (IQR) | 0 (0, 9.1) |
| Glomerular sclerosis score median (IQR) | 0 (0,1) |
| Tubular atrophy score median (IQR) | 1 (0,1) |
| Interstitial fibrosis score median (IQR) | 1 (1,1) |
| Vascular score median (IQR) | 0 (0,0) |
| Remuzzi score median (IQR) | 2 (0,3) |
| Acute tubular injury (%) | 263 (25.7) |
| Glomerular thrombi (%) | 55 (5.4) |
| Arteriolar hyalinosis (%) | 255 (24.9) |
Note: Each of the score was determined by Remuzzi scoring criteria.
Changes in each evaluated component of the kidney tissue (vessels, glomeruli, tubules, and connective tissue) received a score ranging from 0 to 3. The sum of these scores was defined as the global kidney score, which ranges from 0 to 12. Glomerulosclerosis, interstitial fibrosis, and tubular atrophy were graded on the basis of percentage involvement (0, absent; 1: <20%; 2: 20%–50%; 3: > 50%). The degree of vascular disease was a composite assessment of arteries and arterioles, focused on blood vessels with the most severe changes. The vascular score was 1 when the vessel-wall thickness was less than the diameter of the lumen; 2 when the vessel-wall thickness was equal or slightly greater to the diameter of the lumen and 3 when the vessel-wall thickness far exceeded the luminal diameter or the lumen was occluded.
Remuzzi score of 4 was sensitive threshold cutoff
In order to assess the relative importance of pathological biopsy and clinical variables, univariate and multivariable Cox analyses were performed, with death-censored graft loss as the independent variable. The analysis showed the Remuzzi score, KDPI and Recipient body mass index (BMI) were independent risk factors for the graft loss (Table 3). These results were similar when adjusting for only KDPI and after adjusting for individual donor, recipient, and transplant characteristics (Supplementary Table S3). Using the model-development population of 1024 transplants, AUC analysis indicated that score of 4 was a sensitive threshold for grouping Remuzzi scores. We therefore divided recipients into three groups according to Remuzzi score: 0–3 (n = 819), 4 (n = 100), and > 4 (n = 105). The relationship between baseline donor chronic kidney injury and graft survival are shown in Figure 2 and Table 4. The incidence of DGF was 15.9%, 28.0%, and 29.5% in the three groups, respectively. The 1-year eGFR was 72.2 ± 23.2 mL/min/1.73 m2, 57.0 ± 21.4 mL/min/1.73 m2, and 52.3 ± 19.7 mL/min/1.73 m2, respectively. Moreover, 10-year cumulative graft survival was significantly higher in the low-score group (92.0%, 92.0% vs 82.0%, p < 0.001). Notably, those grafts with a score of 4 showed similar 10-year graft survival to those with scores 0–3 (92.0% vs. 92.0%, p = 0.984), while scores > 4 were associated with significantly poorer outcomes (82.0%, p = 0.033). 10-year patient survival with a score of 4 was similar to that with scores 0–3 (90.0% vs. 94.0%, p = 0.122), whereas tended to be poorer than score of > 4 (90.0% vs. 81.0%, p = 0.067). These data allowed us to combined Remuzzi score of = 4 and 0–3 group. A direct comparison of kidneys with low Remuzzi score (≤4) versus those with high Remuzzi score (> 4) demonstrated that kidneys with high Remuzzi score were more likely to come from extended criteria donors and had higher KDRI/KDPI scores (P ˂ 0.001; Table 5, Figure S1).
Table 3.
Univariate and multivariable cox analysis for death-censored graft failure (n = 1024).
| Parameter | HR | SD | p-Value | 95% CI |
|---|---|---|---|---|
| Univariate analysis | ||||
| Donor age | 1.022 | 0.009 | 0.016a | (1.004, 1.041) |
| Donor sex (male) | 0.652 | 0.280 | 0.127 | (0.377, 1.129) |
| Donor terminal creatinine | 1.181 | 0.076 | 0.028a | (1.018, 1.371) |
| Donor BMI | 1.011 | 0.038 | 0.773 | (0.938, 1.090) |
| Donor hypertension | 2.314 | 0.258 | 0.001a | (1.395, 3.839) |
| Donor death of CV disease | 2.020 | 0.238 | 0.003a | (1.266, 3.224) |
| Expanded criteria donor | 1.895 | 0.270 | 0.018a | (1.116, 3.218) |
| KDPI | 1.018 | 0.005 | <0.001a | (1.009, 1.028) |
| Recipient age | 0.996 | 0.012 | 0.765 | (0.974, 1.020) |
| Recipient sex (male) | 1.699 | 0.260 | 0.042a | (1.020, 2.829) |
| Recipient BMI | 1.097 | 0.036 | 0.010a | (1.023, 1.177) |
| HLA mismatch | 0.912 | 0.082 | 0.259 | (0.776, 1.071) |
| CIT | 0.999 | 0.001 | 0.265 | (0.998, 1.000) |
| Remuzzi Score | 1.285 | 0.054 | <0.001a | (1.155, 1.429) |
| Acute tubular injury | 0.866 | 0.271 | 0.596 | (0.509, 1.474) |
| Glomerular thrombi | 2.729 | 0.376 | 0.008a | (1.305, 5.707) |
| Arteriolar hyalinosis | 2.290 | 0.245 | <0.001a | (1.418, 3.698) |
| Multivariable analysis | ||||
| Remuzzi Score | 1.228 | 0.067 | 0.002a | (1.077, 1.400) |
| KDPI | 1.011 | 0.006 | 0.049a | (1.001, 1.023) |
| Recipient BMI | 1.129 | 0.037 | 0.001a | (1.050, 1.213) |
Abbreviations: BMI, body mass index; CV, cardiovascular; KDPI, kidney donor profile index; HLA, human leukocyte antigen; CIT, cold ischemia time; HR, hazard ratio; SD, standard deviation; CI, confidence interval.
Remuzzi Score was analyzed as a continuous variable.
ap < 0.05; factors with p < 0.1 was included in the multivariable analysis.
Figure 2.
Relationship among baseline donor chronic kidney injury and graft survival. Kaplan–Meier analysis of kidney transplant outcome according to histopathological assessment of baseline chronic kidney injury based on the Remuzzi classification. survival of kidneys with histological score of 4 was similar to that of kidneys with histological scores of 0–3 (p = 0.339), whereas survival of kidneys scoring > 4 tended to be poorer (p = 0.067).
Table 4.
Outcomes of cohorts (n = 1024).
| Characteristic | Total (n = 1024) | Remuzzi Score |
p c | p d | p e | ||
|---|---|---|---|---|---|---|---|
| ≤3 (n = 819) | =4 (n = 100) | >4 (n = 105) | |||||
| DGF (%) | 189 (18.5) | 130 (15.9) | 28 (28.0) | 31 (29.5) | 0.002 | 0.810 | <0.001 |
| 1-yr graft eGFRa (mL/min/1.73m2) | 68.7 ± 23.8 | 72.2 ± 23.2 | 57.0 ± 21.4 | 52.3 ± 19.7 | <0.001 | 0.101 | <0.001 |
| 5-yr graft survival rate (%) | 94.0 | 95.0 | 92.0 | 82.0 | 0.208 | 0.033 | <0.001 |
| 5-yr patient survival rate (%) | 97.0 | 98.0 | 93.0 | 93.5 | 0.004 | 0.925 | 0.001 |
| 10-yr graft survival rate (%) | 91.0 | 92.0 | 92.0 | 82.0 | 0.984 | 0.033 | <0.001 |
| 10-yr patient survival rate (%) | 93.0 | 94.0 | 90.0 | 81.0 | 0.122 | 0.067 | <0.001 |
Note: Continuous variables are shown as mean ± standard deviation; categorical variables are reported as percentages; DGF, delayed graft function; eGFR, estimated-glomerular filtration rate. Bold values indicate p < 0.05.
aKidneys that failed within the first year following transplantation (n = 21) were assigned an arbitrary eGFR value of 10 mL/min/1.73 m2.
bKidneys that failed within the third year following transplantation (n = 38) were assigned an arbitrary eGFR value of 10 mL/min/1.73 m2.
cp comparing ≤ 3 and = 4 group.
dp comparing = 4 and > 4 group.
eP comparing ≤ 4 and > 4 group.
Table 5.
Baseline characteristics of donors and recipients grouped according to remuzzi score.
| Characteristic | ≤4 | >4 | p-Value |
|---|---|---|---|
| N (%) | 919 (89.7) | 105 (10.3) | – |
| Donor characteristics | |||
| Age (year) | 42.4 ± 13.2 | 50.3 ± 11.3 | <0.001c |
| Male (%) | 738 (80.7) | 81 (87.6) | 0.609 |
| Terminal creatinine (mg/dL) | 1.2 ± 1.0 | 1.8 ± 1.2 | <0.001c |
| BMI (kg/m2) | 23.0 ± 3.1 | 24.1 ± 3.3 | <0.001c |
| Hypertension (%) | 131 (14.3) | 53 (51.5) | <0.001c |
| Death from cerebrovascular disease (%) | 277 (30.1) | 67 (63.8) | <0.001c |
| Expanded criteria donor (%) | 152 (16.5) | 35 (33.3) | <0.001c |
| KDRIa | 1.0 ± 0.3 | 1.3 ± 0.3 | <0.001c |
| KDPIb (%) | 48.8 ± 25.3 | 72.5 ± 18.6 | <0.001c |
| Recipient characteristics | |||
| Age at transplant (year) | 44.4 ± 10.1 | 46.9 ± 9.8 | 0.014c |
| Male (%) | 553 (60.1) | 59 (56.2) | 0.430 |
| BMI (kg/m2) | 21.5 ± 3.1 | 20.6 ± 2.5 | 0.002c |
| Secondary kidney transplantation (%) | 11 (1.2) | 0 (0) | <0.001c |
| Transplant characteristics | |||
| HLA mismatches | 2.8 ± 1.4 | 2.9 ± 1.4 | 0.645 |
| CIT (h) | 7.5 ± 3.7 | 7.4 ± 3.8 | 0.968 |
Note: Continuous variables are shown as mean ± standard deviation; categorical variables are reported as percentages; BMI, body mass index; ECD, expanded criteria donor; KDRI, kidney donor risk index; KDPI, kidney donor profile index; HLA, human leukocyte antigen; CIT, cold ischemia time.
aKDPI was calculated according to the following: https://optn.transplant.hrsa.gov/data/allocation-calculators/kdpi-calculator/.
The KDRI score was calculated on the basis of the following donor parameters: age, height, weight, history of hypertension, history of diabetes, cause of death (cerebral stroke), serum creatinine at donation, HCV serostatus, and donation after circulatory death status.
bthe KDPI is the percentile rank (from the 1st to the 100th) of the KDRI with reference to a given Organ Procurement and Transplantation Network deceased donor cohort.
cp < 0.05.
The diagnostic or discrimination ability of the Remuzzi score and KDPI were assessed using ROC analysis (Figure 3). The AUC values of Remuzzi score, KDPI, and Remuzzi score + KDPI for predicting 1-year graft survival rate were 0.607, 0.728, and 0.735, respectively.
Figure 3.
The ROC curves for each variable predicting survival of graft kidney. The AUC of Remuzzi score, KDPI and Remuzzi score + KDPI for predicting 1-year graft survival rate were 0.607, 0.728, and 0.735, respectively. ROC, receiver operating characteristic; AUC, area under curve; KDPI, kidney donor profile index.
Survival analysis of the high remuzzi score group (>4)
In the high score group (> 4, n = 105), KDPI and donor terminal creatinine were significantly associated with graft loss (Table 6). ROC analysis identified a KDPI cutoff of 93% and a terminal creatinine cutoff of 1.54 mg/dL as predictive thresholds. Grafts with KDPI > 93% had significantly lower survival than those with KDPI ≤ 93% (p = 0.004). Similarly, grafts from donors with terminal creatinine > 1.54 mg/dL had poorer survival than those with creatinine ≤ 1.54 mg/dL (p = 0.038, Figure 4). In the high score subgroup (Supplementary Table S4), we found that donors of Remzzzi score ≥ 7 deceased donor kidneys were similar to those with Remzzzi score 5–6 kidneys regarding age, sex, terminal creatinine, BMI, hypertension, death from cerebrovascular disease, ECD and KDRI/KDPI (all p > 0.05). The incidence of delayed graft function and graft function were also similar between groups (p > 0.05).
Table 6.
Univariate and multivariable analysis for cox death-censored graft failure (Remuzzi score > 4, n = 105).
| Parameter | HR | SD | 95% CI | p-Value |
|---|---|---|---|---|
| Univariate analysis | ||||
| Donor age | 1.021 | 0.022 | (0.978,1.067) | 0.34 |
| Donor sex (male) | 1.759 | 0.757 | (0.399,7.751) | 0.455 |
| Donor terminal creatinine | 1.443 | 0.169 | (1.036,2.009) | 0.030a |
| Donor BMI | 0.982 | 0.078 | (0.843,1.144) | 0.813 |
| Donor hypertension | 1.656 | 0.517 | (0.601,4.560) | 0.329 |
| Donor death of CV disease | 0.574 | 0.486 | (0.221,1.489) | 0.254 |
| Expanded criteria donor | 1.158 | 0.508 | (0.428,3.134) | 0.772 |
| KDPI | 1.039 | 0.018 | (1.004,1.076) | 0.031a |
| Recipient age | 1.028 | 0.027 | (0.975,1.084) | 0.313 |
| Recipient sex (male) | 0.867 | 0.486 | (0.334,2.247) | 0.769 |
| Recipient BMI | 0.948 | 0.098 | (0.782,1.148) | 0.585 |
| HLA mismatch | 0.894 | 0.178 | (0.631,1.266) | 0.529 |
| CIT | 0.998 | 0.001 | (0.996,1.000) | 0.077 |
| Remuzzi Score | 1.209 | 0.194 | (0.826,1.768) | 0.329 |
| Multivariable analysis | ||||
| KDPI | 1.045 | 0.018 | (1.009,1.082) | 0.015a |
| Donor terminal creatinine | 1.571 | 0.189 | (1.084,2.277) | 0.017a |
Abbreviations: BMI, body mass index; CV, cardiovascular; KDRI, kidney donor risk index; HLA, human leukocyte antigen; CIT, cold ischemia time; HR, hazard ratio; SD, standard deviation; CI, confidence interval.
Remuzzi Score was analyzed as a continuous variable.
aIndicates p < 0.05; factors with p < 0.1 was included in the multivariable analysis.
Figure 4.
Kaplan–Meier analysis of kidney transplant outcome in the Remuzzi score> 4 (n = 105). Panel A: survival of grafts with KDPI > 93% was lower than that of kidneys with KDPI of ≤ 93% (p = 0.004). Panel B: survival of grafts with donor terminal creatine > 1.54 mg/dl was lower than that of kidneys with donor terminal creatine of ≤ 1.54 mg/dl (p = 0.038).
Discussion
This study demonstrates that single kidney transplantation is safe for grafts with Remuzzi scores of 4, as these grafts exhibited excellent long-term survival comparable to those with scores of 0–3. Using a large cohort of postreperfusion kidney biopsies, excluding selection bias, sampling techniques (wedge versus core-needle biopsy) and pathologist factors, we found that integrating histopathological assessment with clinical parameters (e.g., KDPI and donor creatinine) can optimize donor kidney allocation and expand the donor pool without compromising outcomes.
If one kidney functions well, dual kidney transplantation is not necessary. A cutoff value of a Remuzzi score of 4 that identifies kidneys to be implanted as dual transplants has been questioned [13,14]. This study demonstrated that single kidney transplantation with Remuzzi scores = 4 was safe. Our study indicated that the single graft survival with Remuzzi score of 4 was similar to that of single graft with Remuzzi score of 0–3 donors. Just as Kosmoliaptsis et al. [14] noted, kidneys with Remuzzi scores of ≤4 can be implanted singly with acceptable outcomes. Fernandez-Lorente et al. [13] also suggested that dual kidney transplantation should be considered for scores of 5 or 6. Using this cutoff value, several dual implants were performed in some centers using kidneys that could perhaps have provided satisfactory function and adequate outcomes if they had been transplanted with single kidney.
Preimplantation histopathology was the most frequently cited justification for kidney discard [15,16]. However, the role for preimplantation histopathology is an area of great debate and the role of preimplantation biopsy in the process of deciding to use kidneys as single or dual kidney transplants versus discard has not been adequately studied. As noted by Mohan et al. [17], histologic findings on postreperfusion biopsy are associated with the graft outcomes. However, some recent studies [18,19] suggested the uselessness of procurement biopsies in predicting post-transplant outcomes when accounting for other donor characteristics. The present study showed that the Remuzzi score indeed helped the surgeons predict graft and patient prognosis after kidney transplantation. Our study highlights the prognostic value of combining the Remuzzi score with KDPI. The recipients’ BMI was independent predictor of graft survival after transplantation except for donor biopsy and KDPI. Lower BMI was associated with a better outcome. This finding indicate that recipients’ BMI is a key determinant of graft outcome, and hence confirms that careful matching of donor and recipient according to BMI is important for kidney grafts allocated according to the results of histologic evaluation. While the Remuzzi score alone showed moderate predictive accuracy (AUC = 0.607), its combination with KDPI significantly improved discrimination (AUC = 0.735). This integrated approach allows clinicians to better assess the functional potential of marginal kidneys, particularly those with high Remuzzi scores (> 4). For example, in the high-score group, grafts from donors with KDPI ≤ 93% and terminal creatinine ≤ 1.54 mg/dL exhibited acceptable survival rates, suggesting that clinical parameters can partially compensate for histopathological damage. The integration of clinical and pathological assessments is not merely an academic exercise but a pragmatic necessity, bridging the gap between organ shortage and equitable access to transplantation.
It was worth noting that 105 kidneys (which could have theoretically been considered for implantation as a dual transplant or discarded) with postreperfusion histological scores of > 4 were implanted as single kidney transplants, 84 kidneys had Remuzzi scores of 5–6 and 21 kidneys had Remuzzi scores ≥ 7, two grafts suffered from primary nonfunction or early failure; 17 (16.2%) suffered renal failure. The 10-year graft survival rate was 82% and the 10-year patient survival rate was 81%. Transplants that have not failed all continued to provide good function (eGFR of 52.3 and 53.4 mL/min/1.73 m2 at first year and third years, respectively). Although histological scores were generally unfavorable in this group, most kidney recipients had acceptable outcomes. Subgroup analysis showed that, when postreperfusion histopathology was severe (Remuzzi score > 4), KDPI and terminal creatinine were independent predictors of graft prognosis. Therefore, we believe that when baseline chronic kidney injury is severe (Remuzzi score >4), organ selection needs to be determined on the basis of clinical variables rather than pathological scores. The decision to accept a kidney for an individual is complex and multifaceted. It is difficult to quantify the interaction of all factors, including donor characteristics, recipient characteristics, and center resources. Our results suggested that a strategy of single kidney transplant with poor microscopic appearances but favorable clinical characteristics are safe sometimes. Gandolfini et al. [20] showed that pretransplant donor biopsy–based allocation of marginal grafts led to a limited discard rate of 15% for kidneys with KDPI 80–90% and 37% for kidneys with KDPI 91–100%. Bae and coworkers [21] found that for kidneys with discordant ECD and KDPI indicators, so called, ‘high risk’ standard criteria donor (SCD) kidneys (with KDPI > 85%), these kidneys were at increased risk of discard in the KDPI era. However, compared to those remaining on dialysis while waiting for low-KDPI kidneys, recipients of these kidneys had a much lower risk of mortality. Given the growing number of patients on the waiting list, broadening our approach to kidney acceptance could have an important impact on the ESRD population awaiting transplant.
This study has several limitations. First, the single-center design may introduce center-specific bias, affecting the generalizability of the results. Second, we acknowledge that the subgroup comparisons (Remuzzi score 5–6 and ≥7) may be underpowered due to limited sample sizes, and the observed statistical significance is borderline. Third, the retrospective design relies on previously recorded data, which may be incomplete or inaccurately documented, thereby compromising the reliability and validity of the study findings. While our rigorous data processing and assessment efforts instill confidence in the quality of the data used, we acknowledge that the predictive performance of our models may still be influenced by the overall quantity and completeness of the available data. Nevertheless, this study still provides important insights into expanding the donor pool for kidney transplantation and lays the foundation for further research. Future studies should focus on incorporating more comprehensive, larger, up-to-date data, including varied data types for improved prediction accuracy.
Conclusions
Donor renal biopsy and KDPI are correlated with the long-term prognosis of the graft. The long-term survival of single kidney grafts from donors with Remuzzi score ≤4 is excellent. Some kidneys from donors with high Remuzzi scores donors can provide excellent renal function as single kidney transplants, providing that they are allocated according to KDPI and donor creatinine before transplantation. This approach may help expand the donor-organ pool for kidney transplantation.
Supplementary Material
Funding Statement
This work was supported by the National Natural Science Foundation of China to J. W. (81970647) and R.W. (82070766). The authors thank the colleagues at the Kidney Disease Center, the First Affiliated Hospital, College of Medicine, Zhejiang University for their support and assistance.
Ethics approval and consent to participate
This study was approved by the appropriate ethics committee (Ref: IIT20241458A). All study activities followed the guidelines of the 2000 Declaration of Helsinki and the 2018 Declaration of Istanbul 2018.
Authors contributions
Meifang Wang: Conceptualization, Data curation, Formal analysis, Writing – original draft; Sulin Luo: Data curation, Formal analysis; Xinyi Gao: Data curation, Formal analysis, Investigation, Resources; Qin Zhou: Data curation, Methodology; Huiping Wang: Data curation; Jianghua Chen: Conceptualization, Project administration, Resources; Rending Wang: Conceptualization, Methodology, Writing – review & editing; Jianyong Wu: Conceptualization, Methodology, Writing – review & editing.
Disclosure statement
No potential conflict of interest was reported by the authors.
Data availability statement
The data that support the findings of this study are available from the corresponding author on reasonable request.
References
- 1.Lentine KL, Smith JM, Lyden GR, et al. OPTN/SRTR 2022 Annual Data Report: kidney. Am J Transplant. 2024;24(2S1):S19–S118. doi: 10.1016/j.ajt.2024.01.012. [DOI] [PubMed] [Google Scholar]
- 2.Port FK, Bragg-Gresham JL, Metzger RA, et al. Donor characteristics associated with reduced graft survival: an approach to expanding the pool of kidney donors. Transplantation. 2002;74(9):1281–1286. doi: 10.1097/00007890-200211150-00014. [DOI] [PubMed] [Google Scholar]
- 3.Remuzzi G, Cravedi P, Perna A, et al. Long-term outcome of renal transplantation from older donors. N Engl J Med. 2006;354(4):343–352. doi: 10.1056/NEJMoa052891. [DOI] [PubMed] [Google Scholar]
- 4.Remuzzi G, Grinyo J, Ruggenenti P, et al. Early experience with dual kidney transplantation in adults using expanded donor criteria. Double Kidney Transplant Group (DKG). J Am Soc Nephrol. 1999;10(12):2591–2598. doi: 10.1681/ASN.V10122591. [DOI] [PubMed] [Google Scholar]
- 5.Wang M, Lv J, Zhao J, et al. Postreperfusion renal allograft biopsy predicts outcome of single-kidney transplantation: a 10-year observational study in China. Kidney Int Rep. 2024;9(1):96–107. doi: 10.1016/j.ekir.2023.10.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Zhao H, Wang H, Pu M, et al. An important measure taken for China Organ Transplant Response System. Hepatobiliary Surg Nutr. 2022;11(4):651–653. doi: 10.21037/hbsn-22-226. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Racusen LC, Solez K, Colvin RB, et al. The Banff 97 working classification of renal allograft pathology. Kidney Int. 1999;55(2):713–723. doi: 10.1046/j.1523-1755.1999.00299.x. [DOI] [PubMed] [Google Scholar]
- 8.Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–612. doi: 10.7326/0003-4819-150-9-200905050-00006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Kidney Disease: improving Global Outcomes Transplant Work G . KDIGO clinical practice guideline for the care of kidney transplant recipients. Am J Transplant. 2009;9(Suppl 3):S1–S155. [DOI] [PubMed] [Google Scholar]
- 10.Rodríguez Faba O, Boissier R, Budde K, et al. European Association of Urology Guidelines on Renal Transplantation: update 2018. Eur Urol Focus. 2018;4(2):208–215. doi: 10.1016/j.euf.2018.07.014. [DOI] [PubMed] [Google Scholar]
- 11.Branch of Organ Transplantation CMA . Branch of Kidney Transplantation, China International Exchange and Promotive Association for Medical and Health Care. Chinese clinical practice guidelines for immunosuppressive therapy in kidney transplant recipients (2023 edition). Chin J Organ Transplant. 2024;45(10):645–663. [Google Scholar]
- 12.R Core Team . The R Project for Statistical Computing. https://www.R-project.org/. Accessed 02/01/2025.
- 13.Fernández-Lorente L, Riera L, Bestard O, et al. Long-term results of biopsy-guided selection and allocation of kidneys from older donors in older recipients. Am J Transplant. 2012;12(10):2781–2788. doi: 10.1111/j.1600-6143.2012.04153.x. [DOI] [PubMed] [Google Scholar]
- 14.Kosmoliaptsis V, Salji M, Bardsley V, et al. Baseline donor chronic renal injury confers the same transplant survival disadvantage for DCD and DBD kidneys. Am J Transplant. 2015;15(3):754–763. doi: 10.1111/ajt.13009. [DOI] [PubMed] [Google Scholar]
- 15.Mohan S, Chiles MC, Patzer RE, et al. Factors leading to the discard of deceased donor kidneys in the United States. Kidney Int. 2018;94(1):187–198. doi: 10.1016/j.kint.2018.02.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Lentine KL, Kasiske B, Axelrod DA.. Procurement biopsies in kidney transplantation: more information may not lead to better decisions. J Am Soc Nephrol. 2021;32(8):1835–1837. doi: 10.1681/ASN.2021030403. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Mohan S, Campenot E, Chiles MC, et al. Association between reperfusion renal allograft biopsy findings and transplant outcomes. J Am Soc Nephrol. 2017;28(10):3109–3117. doi: 10.1681/ASN.2016121330. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Reese PP, Aubert O, Naesens M, et al. Assessment of the utility of kidney histology as a basis for discarding organs in the United States: a comparison of international transplant practices and outcomes. J Am Soc Nephrol. 2021;32(2):397–409. doi: 10.1681/ASN.2020040464. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Husain SA, King KL, Cron DC, et al. Influence of organ quality on the observed association between deceased donor kidney procurement biopsy findings and graft survival. Am J Transplant. 2022;22(12):2842–2854. doi: 10.1111/ajt.17167. [DOI] [PubMed] [Google Scholar]
- 20.Gandolfini I, Buzio C, Zanelli P, et al. The Kidney Donor Profile Index (KDPI) of marginal donors allocated by standardized pretransplant donor biopsy assessment: distribution and association with graft outcomes. Am J Transplant. 2014;14(11):2515–2525. doi: 10.1111/ajt.12928. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Bae S, Massie AB, Luo X, et al. Changes in discard rate after the introduction of the kidney donor profile index (KDPI). Am J Transplant. 2016;16(7):2202–2207. doi: 10.1111/ajt.13769. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the corresponding author on reasonable request.