Association of Acute Rejection and De Novo Renal Cell Carcinoma in Kidney Transplant Patients: An OPTN Data Analysis
by
, , , , , , , and
Molly E. Casey
1
,
Emmalie Yanacek
2,
Hitesh Kaul
1,
John P. Knorr
3,
Kamran Khanmoradi
1,
Afshin Parsikia
1,
Nikolaos Chandolias
1,
Kevin K. Zarrabi
4 and
Radi Zaki
1,* 1
Department of Surgery, Jefferson Einstein Philadelphia Hospital, 5501 Old York Road, Philadelphia, PA 19141, USA
2
Faculty of Osteopathic Medicine, Philadelphia College of Osteopathic, Medicine 4170 City Ave, Philadelphia, PA 19131, USA
3
Department of Pharmacy, Jefferson Einstein Philadelphia Hospital, 5501 Old York Road, Philadelphia, PA 19141, USA
4
Department of Medical Oncology, Thomas Jefferson University Hospital, 111 S 11th Street, Philadelphia, PA 19107, USA
*
Author to whom correspondence should be addressed.
Transplantology 2024, 5(4), 280-287; https://doi.org/10.3390/transplantology5040028
Submission received: 20 June 2024
/
Revised: 6 September 2024
/
Accepted: 22 October 2024
/
Published: 28 November 2024
(This article belongs to the Section Solid Organ Transplantation)
Abstract
:Background: Kidney Transplant Recipients (KTRs) are at risk of renal cell carcinoma (RCC). The risk of RCC in KTRs is approximated to be 5–10 times higher compared with the general population. A relation between kidney rejection and renal malignancy has been described and relates to the effect of immunosuppression at the genomic level. We decided to investigate any suggestive clinical evidence of this in the OPTN database. Methods: KTRs with de novo RCC between July 2004 and June 2022 were identified. Demographics, baseline characteristics, virology, and immunology data were compared between patients with and without RCC. Our follow-up period was four hundred (400) days. A multivariate regression analysis of the data was conducted. Results: In a total of 215,928 kidney transplant recipients, we identified 839 cases of RCC (0.39%). On multivariate analysis, patients who experienced acute rejection both before hospital discharge (OR 1.559; p = 0.037) and during the follow-up period (OR 1.448; p = 0.002) showed a statistically significant increased risk of developing RCC. Conclusions: Our study is an analysis of a large cohort of KTRs diagnosed with RCC. We observed that RCC appeared more frequently in the kidney transplant recipients that were complicated by acute rejection during transplant admission or follow-up period.
1. Introduction
Transplant patients are at an increased risk for malignancies, including de novo renal cell carcinoma (RCC). The incidence of RCC in kidney transplant recipients (KTR) is estimated to be 0.6%, with an associated mortality rate of 13.9% [1]. In a study by Karami et al., KTR patients were at elevated risk (5.7-fold) compared with the general population, with 683 cases of RCC identified within their KTR population. They also found a 13-fold increase in risk for papillary RCC in KTR patients [2]. Znaor et al. studied the international trends of mortality and incidence of RCC. They showed an increasing incidence of RCC worldwide. They also showed that the incidence remains higher in developed countries. Despite a higher incidence in highly developed countries, mortality due to RCC in these countries is decreasing [3]. RCC often presents without symptoms and is discovered when they reach advanced stages [4]. Consistent risk factors for RCC include smoking, obesity, hypertension, and chronic kidney disease (on dialysis) [5]. Solid organ transplantation is an event that can set the stage for transplant-related RCC due to its association with risk factors such as iatrogenic immunosuppression, oncogenic viruses, and donor type [4,6].
The mechanism through which immunosuppression can lead to malignancy is poorly understood. Oncologic viruses are one proposed mechanism, with epidemiology studies showing almost a one-hundred-fold increased risk in those on immunosuppressing medications. However, despite this proposed mechanism, transplant recipients overall are still at a nearly two-fold increased risk of all types of de novo cancers, suggesting other factors are also at play [6].
The biological association between rejection and RCC has been suggested at the cellular level as one such factor [7]. The goal of this study was to test the association of rejection and de novo RCC through analyzing the national OPTN database.
2. Methods
OPTN data were accessed and merged with rejection data in the follow-up, virology information, and malignancy datasets. Acute rejection in transplant admission and follow-up appeared in OPTN data after 30 June 2004. Therefore, the timeframe was set after this date (1 July 2004).
Patients with de novo RCC were selected. Patients with other kinds of cancer, diagnosis of RCC at the time of transplant, and those with a history of cancer in the donor were excluded. The donor type was selected as deceased donors.
Demographics, baseline characteristics, virology, immunology, and donor information (KDPI) were then compared between patients with and without RCC using a univariate analysis. Chi-squared testing and a t-test or non-parametric test were used for the categorical and continuous variables, respectively. Time-to-rejection was considered for slightly over one year (400 days). A total of 35 additional days after the one-year mark was used to ensure likely late reports were still included. Additionally, patients who were returned to dialysis were excluded from focusing on RCC in the native kidneys. This adjustment removed 14,889 patients.
Attempts were also made to analyze the medications that were utilized at the time of induction and to treat acute rejection during the follow-up period. Zenapax was not specifically analyzed while looking at the immunosuppressive medications, as information was not available for the entire timeframe. Since having a functioning graft is associated with rejection and a need for increasing levels of immunosuppression, the duration of the transplant was calculated. It was defined as the time between the transplant and graft failure date.
Then, acute rejection in transplant admission and acute rejection in the follow-up were tested separately in two regression models. This allowed for performing multivariate logistic regression testing. The covariates were the items that we identified as statistically significant in the univariate analysis.
3. Results
After applying all exclusion criteria, 839 RCC vs. 215,089 non-RCC patients remained for analysis. RCC patients were more often male (RCC, 69.4% vs. 60.3%, p < 0.001), African American (44.1% vs. 29.8%, p < 0.001), diagnosed with focal glomerular sclerosis at the time of transplant (7.7% vs. 5.5%, p 0.007), experienced longer times on the waiting list (809 days vs. 561 days, p < 0.001), and had higher serum creatinine before transplant (serum creatinine at transplant > 5; 85.1% vs. 76.4%, p < 0.001). There was also a higher rate of dialysis at the time of transplant in the RCC group (89.4% vs. 84.4%, p < 0.001). Additionally, the frequency of failed allografts was higher in the RCC group (14.3% vs. 7.2%, p < 0.001). The allograft failed in the RCC group later than the non-RCC group, as shown by a longer duration of transplant in the RCC group (median, 3064.5 days vs. 2468 days, p < 0.001). Moreover, there were some slight differences in age and BMI between the two groups, with higher values for the RCC patients. These differences were statistically significant, as shown in Table 1.
Acute rejection between transplant and discharge was statistically significantly higher in the RCC group (2.7% vs. 1.7%, p 0.013). Similarly, we found an elevated rate of acute rejection in the follow-up (9.4% vs. 6.2%, p < 0.001) (Table 2).
The only significant difference in virology was in CMV seropositivity, which was higher in the RCC patients (72.9% vs. 66.7%). We need to mention that the virology information was based on serology and not viral load (Table 3).
Most RCC patients were induced by thymoglobulin (52%), which was slightly less than patients without RCC (55.8%). This difference was significant (p 0.026). Other immunosuppressive medications (in the induction) were statistically non-significant between the two groups. Steroid was the most common medication that was utilized for the treatment of acute rejection in the follow-up for both groups. However, the difference was not significant (45.6% vs. 37.5%, p 0.14). Thymoglobulin was the second most common medication for the treatment of acute rejection in the follow-up, with less frequency in the RCC patients (11.4% vs. 12.1%, p 0.85) (Table 4).
In terms of donor profile, we noticed an elevated KDPI in the RCC group (median 0.39 vs. 0.35, p 0.004). Similarly, the rate of donor KDPI > 0.5 was elevated in the RCC group (37.3% vs. 32.8%, p 0.006), as shown in Table 5.
All the above information was at a univariate level. At the multivariate level, we studied the risk factors that can have a role in developing RCC in the transplant admission and later in the follow-up. The risk factors that we included were derived from demographics, immunology, virology, and donor profiles.
Table 6 indicates that the odds ratio (OR) of acute rejection between transplant and discharge was 1.559, p 0.037 (95% CI 1.028–2.365). The other significant risk factors that were identified with acute rejection between transplant and discharge were male gender (OR 1.489, p < 0.001), focal glomerular sclerosis at transplant (OR 1.332, p 0.029), African American race (OR 1.667, p < 0.001), recipient serum creatinine at transplant > 5 (OR 1.240, p 0.046), seropositivity for the recipient at transplant (OR 1.278, p 0.002), and KDPI > 0.5 (OR 1.188, p 0.042).
Table 7 is for multivariate analysis of risk factors that were identified in developing RCC in the follow-up. In this multivariate analysis, there was a statistically significant increased risk of developing RCC in KTR who experienced acute rejection with an elevated odds ratio of 1.448 (p 0.002, 95% CI 1.147–1.828). The other risk factors that were determined to have a role in developing RCC were male gender (OR 1.485, p < 0.001), diagnosis of focal glomerular sclerosis at transplant (OR 1.320, p 0.034), African American race (OR 1.654, p < 0.001), creatinine at transplant > 5 (OR 1.260, p 0.033), and recipient seropositivity at transplant (OR 1.283, p 0.002).
4. Discussion
Chewcharat et al. conducted a literature review from inception through October 2018. They included a total of 22 studies. In their study, the overall pooled estimated incidence of RCC after kidney transplant was 0.6% [1]. The rate of RCC in our study was 0.4%. Table 1. While our sample size was robust, the decrease in incidence in our sample could, in part, be attributed to excluding living donor transplants, any patient with a history of cancer, and patients requiring resumption of HD.
The literature has shown that 90% of cases of RCC occur in the native kidney [8]. For this reason, our study focused on de novo RCC in the native kidneys in KTRs, and patients requiring resumption of hemodialysis were excluded.
Our multivariate logistic regression testing was used to evaluate the role of acute rejection. It was adjusted for other factors that we found significant in the univariate testing. We noted an association between acute rejection and de novo RCC in the transplant admission (OR 1.559, p 0.037). Similarly, there was an association noted for rejection in follow-up (OR 1.448, p 0.002).
With the advancements in molecular diagnostics and high-throughput genomic and transcriptomic sequencing, we have gained a deeper understanding of the drivers of tumorigenesis for clear cell and non-clear cell RCC tumors. Clear cell RCC represents the most common histology in sporadic non-transplant-related RCC and is driven by genetic or epigenetic inactivation of the von Hippel–Lindau (VHL) tumor suppressor gene and the aberrant signaling within the canonical VHL-HIF-VEGF pathway. However, VHL loss and HIF accumulation are not sufficient to drive RCC development independently. In fact, HIF targets and HIF-responsive genes include tumor-suppressive factors along with oncogenic factors [9]. HIF accumulation indirectly induces expression of the type I IFN pathway, specifically interferon-stimulated gene factor 3 (ISGFR3) [10,11]. The exact mechanism by which IFN genes are influenced by genomic drivers of RCC remains an area of ongoing investigation.
The exact role of immunosuppression in RCC development remains poorly understood. It is hypothesized that immunosuppressants alter the immune milieu within the tumor microenvironment (TME) and disrupt immune surveillance, facilitating immune evasion by transformed cells [6,12]. Interestingly, a decrease in the risk of acute rejection due to more potent immunosuppressive therapy is often accompanied by an increase in the incidence of malignancies among KTRs [6]. Additionally, calcineurin inhibitors, which inhibit T-cell activation, also induce VEGF and TGF-B production, which are integral in the angiogenesis and RCC tumorigenesis. Antimetabolites, which reduce the proliferation of lymphocytes, may also interfere with DNA replication and reduce DNA repair processes that mediate DNA mutations that can result in cancer [13]. In a comprehensive analysis that leveraged TCGA microarray data, a machine learning algorithm identified a 55-gene signature through a differentially expressed gene analysis of KTRs with and without rejection. The KTR rejection-associated genes were primarily enriched in the cellular response to the interferon-mediated signaling pathway [7]. Together, these data are suggestive of type I interferon pathway dysfunction, which is an established driver of sporadic RCC, as a core driver of KTR-associated RCC. The fact that the most common tumor-suppressing genes associated with sporadic RCC all converge on the type I IFN pathway and that acute rejection in KTRs is enriched in interferon signaling is hypothesis-generating and warrants further investigation.
Further investigation is warranted into the spatial immunoprofiling of native kidney RCC in KTRs. Renal allografts and RCC tumors harbor a predominance of M2-polarized macrophages [14]. Macrophages demonstrate phenotypic plasticity and can transition from a proinflammatory to immunosuppressive phenotype, and evidence suggests the tumor-associated macrophage (TAM) immune subset may serve as a link between immune-mediated allogeneic renal transplant rejection and subsequent RCC tumorigenesis [14]. Investigation in the KTR native kidney TAM infiltration will be integral to understanding the macrophage phenotype and contribution to RCC in patients with transplants.
Despite these causative mechanisms, the influence of specific immunosuppressant agents’ effects on the incidence of cancer is not well established [15].
It should be noted that not all immunosuppressants increase the risk of cancer. Conversion of tacrolimus to sirolimus was associated with a lower rate of 24-month de novo malignancies in kidney transplant recipients who did not have a prior history of cancer [16].
The role of immunodeficiency in tumor development may not be limited to immune signaling alteration within the TME but may also have a contributory element from oncogenic viruses associated with the immunodeficient state of the host [17]. RCC observed in KTRs have gene profiles suggestive of maladaptive responses to viruses, impaired humoral immune responses, and deficient leukocyte chemotaxis [7].
In our study, we analyzed virology and immunology data with attention to HCV, EBV, CMV, HBV, and HIV (Table 3).
Patients seropositive for CMV at the time of transplant were shown to be correlated with an increased risk of developing de novo RCC (72.9% vs. 66.7 <0.001).
Our experience indicates that RCC and acute rejection can potentially be related. Our methodology vigorously eliminated other sources of RCC from donors. As such, we performed our best to make sure that all the occurrences of RCC were de novo.
This will need more investigation and support at the cellular level to differentiate various factors that are involved in the development of RCC post-kidney transplant.
We need to acknowledge some limitations. We excluded the patients who were resuming maintenance dialysis. As such, we hope that most RCCs in our data were native kidney RCCs (supported by the literature). However, we were not able to make that distinction more precisely because of limitations in the data. Additionally, we were not able to differentiate between cell-mediated and antibody-mediated types of rejection with accuracy. Again, this was because of the limitation in the data.
Author Contributions
Conceptualization: A.P. and R.Z.; formal analysis: A.P.; writing—original draft preparation: M.E.C., E.Y., J.P.K. and A.P.; writing—review and editing: M.E.C., E.Y., H.K., J.P.K., K.K., A.P., N.C., K.K.Z. and R.Z.; supervision: R.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Ethical review and approval was not required for this study because it does not meet definition of human research at 45 CFR 46.102(e)(1).
Informed Consent Statement
Informed consent is not applicable. Subjects cannot be identified. It does not meet definition of human research.
Data Availability Statement
The data were obtained from the SRTR (UNOS) and are available upon direct request from them.
Conflicts of Interest
The authors declare no conflicts of interest.
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Table 1.
Recipient Demographics.
| RCC (No) | RCC (Yes) | p-Value | |
|---|---|---|---|
| N = 215,089 | N = 839 | ||
| Recipient age, yr. | 50.8 (14.9) | 53.8 (11.0) | <0.001 |
| Recipient age ≥ 65 yrs. | 40,356 (18.8%) | 139 (16.6%) | 0.10 |
| Recipient, male | 129,780 (60.3%) | 582 (69.4%) | <0.001 |
| Diagnosis at transplant, focal glomerular sclerosis | 11,533 (5.5%) | 64 (7.7%) | 0.007 |
| Kidney days on waiting list, median (IQR)—day | 561 (155–1222) | 809 (315–1431) | <0.001 |
| Recipient diabetes at registration | 80,040 (38.6%) | 218 (27.2%) | <0.001 |
| Recipient, black | 64,122 (29.8%) | 370 (44.1%) | <0.001 |
| Calculated recipient BMI | 27.2 (23.5–31.3) | 28.5 (24.5–32.3) | <0.001 |
| Recipient BMI > 35 | 22,635 (10.5%) | 109 (13.0%) | 0.021 |
| Serum creatinine at time of transplant, mg/dL | 7.43 (5.2–10) | 8.44 (6.2–11.2) | <0.001 |
| Recipient creatinine at transplant > 5, mg/dL | 161,451 (76.4%) | 702 (85.1%) | <0.001 |
| Cold ischemic time (hours) | 16.23 (11–22.13) | 16.57 (11.1–22) | 0.61 |
| Cold ischemic time > 18 h | 84,373 (41.5%) | 344 (42.3%) | 0.63 |
| Dialysis at transplant | 179,430 (84.4%) | 746 (89.4%) | <0.001 |
| Failed allograft | 15,451 (7.2%) | 120 (14.3%) | <0.001 |
| Duration of transplant, median (IQR), day * | 2468 (1684–3377) | 3064.5 (2125.5–3659) | <0.001 |
* Duration of transplant: time between transplant and graft failure.
Table 2.
Recipient Immunology.
| RCC (No) | RCC (Yes) | p-Value | |
|---|---|---|---|
| N = 215,089 | N = 839 | ||
| HLA mismatch level | 4 (3–5) | 5 (3–5) | 0.44 |
| HLA mismatch > 4 | 102,195 (47.8%) | 420 (50.2%) | 0.16 |
| Panel reactive antibody (PRA) > 20% | 63,768 (29.9%) | 232 (27.8%) | 0.19 |
| Acute rejection between transplant and discharge | 3436 (1.7%) | 23 (2.7%) | 0.013 |
| Acute rejection in follow-up | 13,266 (6.2%) | 79 (9.4%) | <0.001 |
| Time to rejection (day), median (IQR) | 193 (179–294) | 191 (178–241) | 0.72 |
Table 3.
Recipient Virology AT Transplant.
| RCC (No) | RCC (Yes) | p-Value | |
|---|---|---|---|
| N = 215,089 | N = 839 | ||
| Recipient HCV seropositive at transplant | 11,657 (5.7%) | 52 (6.5%) | 0.34 |
| Recipient EBV seropositive at transplant | 167,247 (89.1%) | 609 (88.1%) | 0.41 |
| Recipient CMV seropositive at transplant | 138,880 (66.7%) | 593 (72.9%) | <0.001 |
| Recipient HIV seropositive at transplant | 2050 (1.0%) | 8 (1.0%) | 0.99 |
| Recipient HBV surface Ag positive at transplant | 3841 (1.9%) | 21 (2.6%) | 0.12 |
Table 4.
Immunosuppressive Medications.
| RCC (No) | RCC (Yes) | p-Value | |
|---|---|---|---|
| N = 215,089 | N = 839 | ||
| Induction Agents | |||
| Thymoglobulin | 119,979 (55.8%) | 436 (52.0%) | 0.026 |
| Campath | 25,529 (11.9%) | 101 (12.0%) | 0.88 |
| Simulect | 38,909 (18.1%) | 154 (18.4%) | 0.84 |
| Steroid | 143,332 (66.6%) | 577 (68.8%) | 0.19 |
| ATGAM | 2478 (1.2%) | 8 (1.0%) | 0.59 |
| OK3 | 263 (0.1%) | 2 (0.2%) | 0.34 |
| No induction | 22,311 (10.4%) | 78 (9.3%) | 0.31 |
| Anti-Rejection Agents | |||
| N = 13,266 | N = 79 | ||
| Thymoglobulin | 1603 (12.1%) | 9 (11.4%) | 0.85 |
| Steroid | 4979 (37.5%) | 36 (45.6%) | 0.14 |
| ATGAM | 91 (0.7%) | 0 (0.0%) | 0.46 |
| OK3 | 103 (0.8%) | 0 (0.0%) | 0.43 |
| Simulect | 32 (0.2%) | 0 (0.0%) | 0.66 |
| Campath | 46 (0.3%) | 0 (0.0%) | 0.60 |
| Rituxan | 491 (3.7%) | 2 (2.5%) | 0.58 |
Table 5.
Donor Information.
| RCC (No) | RCC (Yes) | p-Value | |
|---|---|---|---|
| N = 215,089 | N = 839 | ||
| Donor age (yr.) | 36.3 (15.7) | 38.1 (15.8) | <0.001 |
| Donor, male | 135,290 (62.9%) | 485 (57.8%) | 0.002 |
| Donor, black | 29,790 (13.9%) | 103 (12.3%) | 0.19 |
| Donor BMI > 35 | 25,931 (12.1%) | 121 (14.4%) | 0.037 |
| Calculated Donor BMI, median (IQR) | 26.2 (22.7–30.7) | 26.8 (23.1–31.4) | 0.023 |
| ECD donor | 26,229 (12.2%) | 120 (14.3%) | 0.063 |
| Donor terminal creatinine | 0.9 (0.7–1.3) | 0.95 (0.7–1.3) | 0.31 |
| Donor terminal creatinine > 1.0 | 88,303 (41.2%) | 357 (42.6%) | 0.42 |
| Donor diabetes | 13,219 (6.2%) | 53 (6.4%) | 0.84 |
| KDPI | 0.35 (0.16–0.59) | 0.39 (0.18–0.61) | 0.004 |
| Donor KDPI > 0.5 | 68,615 (32.8%) | 311 (37.3%) | 0.006 |
| Donor HCV Antibody+ | 10,609 (4.9%) | 37 (4.4%) | 0.48 |
| Donor HCV NAT+ | 5357 (4.8%) | 11 (5.6%) | 0.63 |
| Donor HBV CORE+ | 7924 (3.7%) | 32 (3.8%) | 0.85 |
Table 6.
Multivariate Logistic Regression—Role of Acute Rejection Between Transplant and Discharge.
| RCC | Odds Ratio | SE | p-Value | 95% CI Lower | 95% CI Upper |
|---|---|---|---|---|---|
| Acute rejection between transplant and discharge | 1.559 | 0.331 | 0.037 | 1.028 | 2.365 |
| Recipient age ≥ 65 Yrs. | 0.848 | 0.082 | 0.087 | 0.701 | 1.024 |
| Recipient: male | 1.489 | 0.114 | <0.001 | 1.283 | 1.729 |
| Diagnosis: focal glomerular sclerosis at transplant | 1.332 | 0.175 | 0.029 | 1.030 | 1.723 |
| Recipient: African American | 1.667 | 0.120 | <0.001 | 1.447 | 1.920 |
| Recipient BMI > 35 | 1.204 | 0.125 | 0.074 | 0.982 | 1.475 |
| Recipient creatinine at transplant > 5 | 1.240 | 0.133 | 0.046 | 1.004 | 1.531 |
| Dialysis at the time of transplant | 1.036 | 0.126 | 0.773 | 0.816 | 1.315 |
| Recipient CMV positive at transplant | 1.278 | 0.103 | 0.002 | 1.092 | 1.495 |
| HBV surface antigen at transplant-positive | 1.317 | 0.293 | 0.215 | 0.852 | 2.036 |
| HLA mismatch > 4 | 1.010 | 0.071 | 0.887 | 0.880 | 1.159 |
| KDPI > 0.5 | 1.188 | 0.101 | 0.042 | 1.006 | 1.403 |
| ECD donor | 1.057 | 0.124 | 0.633 | 0.841 | 1.330 |
Table 7.
Multivariate Logistic Regression—Role of Acute Rejection in Follow-up (400 days).
| RCC | Odds Ratio | SE | p-Value | 95% CI Lower | 95% CI Upper |
|---|---|---|---|---|---|
| Acute rejection in follow-up | 1.448 | 0.172 | 0.002 | 1.147 | 1.828 |
| Recipient age ≥ 65 Yrs. | 0.853 | 0.082 | 0.100 | 0.706 | 1.031 |
| Recipient: male | 1.485 | 0.113 | <0.001 | 1.279 | 1.724 |
| Diagnosis: focal glomerular sclerosis at transplant | 1.320 | 0.173 | 0.034 | 1.021 | 1.707 |
| Recipient: African American | 1.654 | 0.120 | <0.001 | 1.435 | 1.905 |
| Recipient BMI > 35 | 1.193 | 0.124 | 0.089 | 0.974 | 1.462 |
| Recipient creatinine at transplant > 5 | 1.260 | 0.137 | 0.033 | 1.019 | 1.558 |
| Dialysis at the time of transplant | 1.067 | 0.131 | 0.599 | 0.838 | 1.358 |
| Recipient CMV positive at transplant | 1.283 | 0.103 | 0.002 | 1.096 | 1.501 |
| HBV surface antigen at transplant-positive | 1.336 | 0.297 | 0.192 | 0.865 | 2.065 |
| HLA mismatch > 4 | 1.008 | 0.071 | 0.914 | 0.878 | 1.156 |
| KDPI > 0.5 | 1.171 | 0.099 | 0.063 | 0.992 | 1.383 |
| ECD donor | 1.068 | 0.125 | 0.574 | 0.849 | 1.344 |
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Casey, M.E.; Yanacek, E.; Kaul, H.; Knorr, J.P.; Khanmoradi, K.; Parsikia, A.; Chandolias, N.; Zarrabi, K.K.; Zaki, R. Association of Acute Rejection and De Novo Renal Cell Carcinoma in Kidney Transplant Patients: An OPTN Data Analysis. Transplantology 2024, 5, 280-287. https://doi.org/10.3390/transplantology5040028
AMA Style
Casey ME, Yanacek E, Kaul H, Knorr JP, Khanmoradi K, Parsikia A, Chandolias N, Zarrabi KK, Zaki R. Association of Acute Rejection and De Novo Renal Cell Carcinoma in Kidney Transplant Patients: An OPTN Data Analysis. Transplantology. 2024; 5(4):280-287. https://doi.org/10.3390/transplantology5040028
Chicago/Turabian StyleCasey, Molly E., Emmalie Yanacek, Hitesh Kaul, John P. Knorr, Kamran Khanmoradi, Afshin Parsikia, Nikolaos Chandolias, Kevin K. Zarrabi, and Radi Zaki. 2024. "Association of Acute Rejection and De Novo Renal Cell Carcinoma in Kidney Transplant Patients: An OPTN Data Analysis" Transplantology 5, no. 4: 280-287. https://doi.org/10.3390/transplantology5040028
APA StyleCasey, M. E., Yanacek, E., Kaul, H., Knorr, J. P., Khanmoradi, K., Parsikia, A., Chandolias, N., Zarrabi, K. K., & Zaki, R. (2024). Association of Acute Rejection and De Novo Renal Cell Carcinoma in Kidney Transplant Patients: An OPTN Data Analysis. Transplantology, 5(4), 280-287. https://doi.org/10.3390/transplantology5040028
