Finally, we found a statistically significant higher Δ eGFR after donation for genetically related donors (LRDs), as shown in Figure 2B. The concern that biologically-related living donors, especially first-degree relatives, may face increased risk of adverse renal outcomes after living kidney donation has been a topic of discussion in the transplant community for a long time [28,29]. Increased risks of renal failure in close biological relatives of ESRD patients have been observed in population-based and case-control studies among non-donors, regardless of whether recipient ESRD has a known hereditary cause [30,31]. In a study of Caucasian donors in Norway, the nine donors who developed ESRD were all biologically related to their recipients, and renal failure appeared mainly due to immunological diseases [21]. We believe that, although there is still controversy in the literature around whether LRDs are at increased risk compared to Living Unrelated Donors (LURDs) [32,33], the difference in mean eGFR after donation demonstrated in our study generates the responsibility for the transplant team to inform potential donors genetically related to the recipient about the potential increased risk of ESRD. A possible explanation for this finding, along with the latency of some genetic renal disorders running in the family, could be the presence of emotional factors [34] that push towards the decision to donate in a biologically related donor more than in an unrelated donor. It is also possible that the background risk of ESRD is dependent on several conditions, including family habits or life in a particular socio-economic area—and together, all of these may affect synergistically the eGFR trajectory more significantly than in unrelated donors.

The retrospective nature of this study has limited our analysis to those patients for whom we had complete data during the follow up period. The difficulty for a lifelong follow up in living kidney donors is possibly related to the fact that they are healthy individuals, therefore reluctant to come for tests after the first year post-donation. Another possible explanation could be the fact that as the years go by, donors move around the country and there is no continuity in their follow up.

#### **5. Conclusions**

The present study demonstrated that the higher decline rate in eGFR post-living kidney donation was at 6 months, with female sex, non-Caucasian ethnicity, and age lower than 60 years being independently associated with higher recovery in kidney function.  BMI did not relate to kidney function pre or post living kidney donation.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2077-0383/8/6/883/s1, Table S1: Mean eGFR during follow up and percentual difference between mean eGFR during follow up and eGFR at donation (Δ eGFR).

**Author Contributions:** Conceptualisation, M.I.B. and V.P.s; Data curation, S.C. and I.S.; Formal analysis, M.I.B.; Investigation, M.I.B., S.C., F.J.M.F.D., and V.P.; Methodology, M.I.B., F.J.M.F.D., and V.P.; Project administration, M.I.B., S.C., I.S., and V.P.; Writing – original draft, M.I.B., F.J.M.F.D., and V.P.

**Conflicts of Interest:** The authors declare no conflict of interest.

**Meeting Presentation:** This work has been presented at the Association of Surgeons of Great Britain and Ireland meeting held in Telford, UK on the 7th–9th May 2019.

#### **Abbreviations**

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

#### *Article*

### **Outcomes of Kidney Transplant Patients with Atypical Hemolytic Uremic Syndrome Treated with Eculizumab: A Systematic Review and Meta-Analysis**

**Maria L. Gonzalez Suarez 1,\*, Charat Thongprayoon 2, Michael A. Mao 3, Napat Leeaphorn 4, Tarun Bathini 5 and Wisit Cheungpasitporn 1**

Received: 19 May 2019; Accepted: 26 June 2019; Published: 27 June 2019

**Abstract:** Background: Kidney transplantation in patients with atypical hemolytic uremic syndrome (aHUS) is frequently complicated by recurrence, resulting in thrombotic microangiopathy in the renal allograft and graft loss. We aimed to assess the use of eculizumab in the prevention and treatment of aHUS recurrence after kidney transplantation. Methods: Databases (MEDLINE, EMBASE and Cochrane Database) were searched through February 2019. Studies that reported outcomes of adult kidney transplant recipients with aHUS treated with eculizumab were included. Estimated incidence rates from the individual studies were extracted and combined using random-effects, generic inverse variance method of DerSimonian and Laird. Protocol for this systematic review has been registered with PROSPERO (International Prospective Register of Systematic Reviews; no. CRD42018089438). Results: Eighteen studies (13 cohort studies and five case series) consisting of 380 adult kidney transplant patients with aHUS who received eculizumab for prevention and treatment of post-transplant aHUS recurrence were included in the analysis. Among patients who received prophylactic eculizumab, the pooled estimated incidence rates of recurrent thrombotic microangiopathy (TMA) after transplantation and allograft loss due to TMA were 6.3% (95%CI: 2.8–13.4%, *I* 2 = 0%) and 5.5% (95%CI: 2.9–10.0%, *I* 2 = 0%), respectively. Among those who received eculizumab for treatment of post-transplant aHUS recurrence, the pooled estimated rates of allograft loss due to TMA was 22.5% (95%CI: 13.6–34.8%, *I* 2 = 6%). When the meta-analysis was restricted to only cohort studies with data on genetic mutations associated with aHUS, the pooled estimated incidence of allograft loss due to TMA was 22.6% (95%CI: 13.2–36.0%, *I* 2 = 10%). We found no significant publication bias assessed by the funnel plots and Egger's regression asymmetry test (*p* > 0.05 for all analyses). Conclusions: This study summarizes the outcomes observed with use of eculizumab for prevention and treatment of aHUS recurrence in kidney transplantation. Our results suggest a possible role for anti-C5 antibody therapy in the prevention and management of recurrent aHUS.

**Keywords:** atypical hemolytic uremic syndrome; eculizumab; kidney transplantation; renal transplantation; meta-analysis

#### **1. Introduction**

Atypical hemolytic uremic syndrome (aHUS) is a microvascular occlusive disorder characterized by hemolytic anemia, thrombocytopenia and acute kidney injury that is not associated with Shiga

toxin-producing Escherichia coli (STEC) or ADAMTS13 deficiency. Instead, it is typically associated with dysregulation of the alternative complement pathway [1,2]. Thrombotic microangiopathy (TMA) is the pathological lesion seen with aHUS, which represents a response to endothelial injury [3]. About 10% of hemolytic uremic syndrome (HUS) pediatric cases and the majority of cases in adults are due to atypical HUS [4].

Kidney transplantation in patients with aHUS has been linked to poor outcomes due to high recurrence rate and graft failure. Approximately 50% of patients with aHUS develop end stage renal disease (ESRD) with a high risk of recurrence after kidney transplantation [5]. Roughly 60–70% of aHUS patients have mutations in factors of the complement system or antibodies directed against complement factor H (*CFH*) [6]. In some cases, aHUS recurrence is noted early after transplantation, while other cases may be at lower risk of recurrence [7]. The risk of aHUS recurrence after transplant is higher in patients with gene mutations that encode circulating complement factors (*C3, CFH,* complement factor *I* (*CFI*)) when compared to patients with gene mutations that encode solid phase proteins such as CD46 [6,8,9]. Patients with no prior history of aHUS may also present with de novo aHUS after kidney transplantation.

Therapies described for management of aHUS in the post-transplant period include the use of plasma exchange (PLEX), rituximab (used for anti-Factor *H* autoantibodies; helps to maintain low levels of antibodies, preventing recurrence of aHUS after transplant) [10], simultaneous liver–kidney transplant for *CFH* mutations, and use of eculizumab (a humanized monoclonal antibody directed against complement protein C5 and thus inhibits terminal complement activation) [11–13]. Prior to the use of eculizumab, patients with gene mutations *CFH*, *CFH*-*CFHR1*/*3*, *CFI*, *C3*, and *CFB* had a 50% risk of progression to ESRD or death at onset of recurrent aHUS during the first year, and this risk increased to 75% after 3–5 years [14].

The KDIGO workgroup recommends the prophylactic use of eculizumab in kidney transplant patients at high risk of recurrence based on their genetic mutations [14]. Whether there is an advantage of preemptive use of eculizumab in all patients with a known pretransplant history of aHUS is currently unclear. In addition, eculizumab use is associated with an increased risk of infection due to terminal complement blockade such as meningococcal infections [15,16]. In this study, we aimed to assess the use of eculizumab in the prevention and treatment of aHUS recurrence after kidney transplantation.

#### **2. Methods**

#### *2.1. Search Strategy and Literature Review*

The protocol for the systematic review has been registered in PROSPERO (registration number: CRD42018089438; http://www.crd.york.ac.uk/PROSPERO). A systematic literature review of EMBASE (1988 to February 2019), MEDLINE (1946 to February 2019), and the Cochrane Database of Systematic Reviews (CDSR) (database inception to February 2019) was performed to assess the use of eculizumab in the prevention and treatment of aHUS recurrence after kidney transplantation. The systematic literature search was undertaken independently by two investigators (M.G.S. and C.T.) using a search approach that incorporated the terms of "kidney" OR "renal" AND "transplant" OR "transplantation" AND "eculizumab". The search strategy is provided in online Supplementary Data 1. No language restriction was applied. A manual literature search for conceivably pertinent studies using references of the included articles was also performed. This study was conducted by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement [17].

#### *2.2. Selection Criteria*

Eligible studies must be (1) clinical trials or observational studies (cohort, case-series, or cross-sectional studies) that reported use of eculizumab in the prevention and treatment of aHUS recurrence after kidney transplantation; (2) adult (age ≥ 18 years old) kidney transplant recipients; and (3) they must provide the data on outcomes of interest including rates of aHUS recurrence and allograft loss among patients who received prophylactic eculizumab and rates of allograft loss among patients who received eculizumab for treatment of post-transplant aHUS recurrence. The eculizumab treatment group included post-transplant patients with de novo or recurrent aHUS. We excluded case reports and studies with single cases treated with eculizumab. Retrieved studies were independently reviewed for eligibility by the two authors (M.L.G.S. and C.T.). Discrepancies were discussed and resolved by a third author (W.C.) and common consensus. Inclusion was not confined by the size of study. Newcastle-Ottawa quality assessment scale [18] was used to appraise the quality of observational studies and the Cochrane risk-of-bias tool correspondingly for clinical trials [19]. Detailed evaluation of each study is presented in online Supplementary Tables S1 and S2.

#### *2.3. Data Abstraction*

A structured information collecting form was used to obtain the following information from each study including title, name of the first author, publication year, country where the study was conducted, demographic data of kidney transplant patients, history of previous kidney transplantation, type of donor, genetic mutations associated with aHUS, eculizumab regimen, use of PLEX, and outcomes following kidney transplantation (rates of aHUS recurrence and allograft loss among patients who received prophylactic eculizumab and rates of allograft loss among patients who received eculizumab treatment for post-transplant aHUS recurrence).

#### *2.4. Statistical Analysis*

Analyses were conducted utilizing the Comprehensive Meta-Analysis 3.3 software (version 3; Biostat Inc., Englewood, NJ, USA). We estimated the pooled incidence rate of recurrent TMA, all-cause allograft loss, and allograft loss due to TMA by the generic inverse variance approach of DerSimonian and Laird, which designated the weight of each study based on its variance [20]. Due to the possibility of between-study variance, we used a random-effects model rather than a fixed-effect model. Sensitivity analysis was performed with restriction to only cohort studies with data on genetic mutations associated with aHUS. We used Cochran's Q test and *I* 2 statistic to assess the between-study heterogeneity. A value of *I* 2 of 0% to 25% represents insignificant heterogeneity, 26% to 50% low heterogeneity, 51% to 75% moderate heterogeneity and 76–100% high heterogeneity [21]. The presence of publication bias was assessed by both subjective inspections of funnel plot and the Egger test [22]. The raw data for this systematic review is publicly available through the Open Science Framework (URL: osf.io/2pz4k).

#### **3. Results**

A total of 1096 potentially eligible articles were identified using our search strategy. After the exclusion of 888 articles based on title and abstract for clearly not fulfilling inclusion criteria on the basis of type of article, study design, or population or outcome of interest, and exclusion of 172 articles due to being duplicates; 36 full-length articles were reviewed. Fifteen of these articles were subsequently excluded from full-length review because they were also duplicates [23–28], did not fulfill inclusion criteria [29–33], did not report the outcomes of interest, or data was unable to be abstracted [34–37]. An additional three articles were excluded because they were case reports or single cases treated with eculizumab [8,38,39]. Ultimately 18 studies (13 cohort studies and five case series) [5,9,15,40–54] consisting of 380 patients with a history of aHUS who received eculizumab for prevention and treatment of post-transplant recurrent aHUS were included into the final analysis. The literature review and selection process are shown in Figure 1. The characteristics of the included studies are presented in Tables 1 and 2.

donor; LR: Living related donor; AMR: Antibody mediated rejection; CMR: Chronic antibody mediated rejection; PLEX: Plasma exchange; TMA: Thrombotic

microangiopathy.

#### *J. Clin. Med.* **2019** , *8*, 919

Ecu: Eculizumab cont.: Continued; D: Day; DD: Deceased donor; LD: Living donor; LU: Living unrelated donor; AMR: Antibody mediated rejection; ACR: Acute cellular rejection.

**Figure 1.** Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow diagram.

The baseline characteristics of kidney transplant patients and data on genetic mutations associated with aHUS are summarized in Table 3. There were 192 patients in the treatment group and 188 patients in the prophylaxis group, with a median age of 38 and 32.3 years, respectively. Females were predominant in both groups (59.4% vs. 84.4%, respectively). The most commonly reported genetic mutation in this our study population was CFH, followed by CFI and C3 in both groups.

**Table 3.** Baseline characteristics of kidney transplant patients included in the meta-analysis \*.

\* Percentages are calculated from total available data. *n* = number.

A history of aHUS prior to kidney transplantation was known in all patients included in this systematic review, except for de novo cases. The triggers for post-transplant aHUS were not reported in the majority of patients. The most commonly reported potential triggers were: Viral infections, urosepsis, *C. di*ffi*cile* infection, tacrolimus use, and everolimus use. De novo aHUS was reported in 56 patients [15,42,47,49]. Tacrolimus was identified as a possible trigger in one of the de novo cases in which aHUS presented 16 years after transplant without any genetic mutations identified [49], while it was suspected in four other cases that had antibody-mediated rejection and their immunosuppression was switched from tacrolimus to sirolimus [42,49]. Urosepsis was reported as a trigger in one de novo case [47].

#### *3.1. Use of Eculizumab in the Prevention of aHUS Recurrence after Kidney Transplantation*

Data on the initiation of prophylactic eculizumab therapy are summarized in Table 3. Overall, among patients who received prophylactic eculizumab, the pooled estimated rates of aHUS recurrence and allograft loss due to TMA were 6.3% (95%CI: 2.8–13.4%, *I* 2 = 0%, Figure 2A) and 5.5% (95%CI: 2.9–10.0%, *I* 2 = 0%, Figure 2B), respectively.

**Figure 2.** Incidence of (**A**) aHUS recurrence (recurrent TMA) and (**B**) allograft loss due to TMA after kidney transplantation with prophylactic eculizumab.

Sensitivity analysis excluding studies with potentially duplicated patients was performed, and showed the pooled estimated rates of aHUS recurrence and allograft loss due to TMA of 5.5% (95%CI: 1.9–14.6%, *I* 2 = 0%, Figure S1) and 5.3% (95%CI: 2.4–11.3%, *I* 2 = 0%, Figure S2), respectively. When analysis was limited only to studies with a mean follow-up time >12 month (mean follow up time range 21 to 35 months), we found pooled estimated rates of aHUS recurrence and allograft loss due to TMA of 4.6% (95%CI: 1.5–13.3%, *I* 2 = 0%) and 4.8% (95%CI: 2.1–10.7%, *I* 2 = 0%), respectively.

#### *3.2. Use of Therapeutic Eculizumab for aHUS Recurrence after Kidney Transplantation*

Data on the initiation of eculizumab treatment for post-transplant aHUS recurrence are summarized in Table 3. Among those who received eculizumab for treatment of post-transplant aHUS recurrence, the pooled estimated rates of allograft loss due to all causes was 24.4% (95%CI: 15.9–35.6%, *I* 2 = 23%, Figure 3A). The pooled estimated rates of allograft loss due to TMA was 22.5% (95%CI: 13.6–34.8%, *I* 2 = 6%, Figure 3B). Meta-regression analysis demonstrated no significant correlations between concomitant use of PLEX and rates of allograft loss due to all causes or due to TMA (*p* = 0.61 and 0.18, respectively).

**Figure 3.** Incidence of (**A**) allograft loss due to all causes and (**B**) allograft loss due to TMA after kidney transplantation with therapeutic eculizumab.

Sensitivity analysis excluding studies that potentially included duplicate patients was performed, and showed the pooled estimated rates of allograft loss due to all causes was 26.7% (95%CI: 13.0–46.9%, *I* 2 = 33%, Figure S3) and the pooled estimated rates of allograft loss due to TMA was 20.0% (95%CI: 9.1–38.4%, *I* 2 = 29%, Figure S4), respectively. Sensitivity analysis restricted only to cohort studies with data on genetic mutations associated with aHUS was performed. The pooled estimated incidence of allograft loss due to TMA in cohort studies with data on genetic mutations was 22.6% (95%CI: 13.2–36.0%, *I* 2 = 10% Figure 4).

**Figure 4.** Forrest plot evaluating for the incidence of allograft loss due to TMA in cohort studies with data on genetic mutations.

Subgroup analysis stratified by mean follow-up time was performed. We found the pooled estimated rates of allograft loss due to all causes was 25.5% (95%CI: 14.8–40.3%, *I* 2 = 34%) at a mean follow-up time of 12 to 24 months and 26.0% (95%CI: 3.9–75.1%, *I* 2 = 13%) at a mean follow-up time of 60 to 72 months. The pooled estimated rates of allograft loss due to TMA were 22.6% (95%CI: 12.1–38.1%, *I* 2 = 8%) at a mean follow-up time of 12 to 24 months and 26.0% (95%CI: 3.9–75.1%, *I* 2 = 13%) at a mean follow-up time of 60 to 72 months.

#### *3.3. Evaluation for Publication Bias*

We found no significant publication bias as assessed by the funnel plots (Figure 5) and Egger's regression asymmetry test for the rates of aHUS recurrence and allograft loss due to TMA in patients treated with prophylactic eculizumab therapy (*p* = 0.48 and 0.28, respectively), nor for rates of allograft loss due to all causes and allograft loss due to TMA in patients treated with therapeutic eculizumab for aHUS recurrence after kidney transplantation (*p* = 0.78 and 0.20, respectively).

**Figure 5.** Funnel plots evaluating for publication bias for (**A**) the incidence of aHUS recurrence after kidney transplantation among patients who received prophylactic eculizumab; (**B**) the incidence of allograft loss due to TMA after kidney transplantation among patients who received prophylactic eculizumab; (**C**) the incidence of allograft loss due to all causes after kidney transplantation among patients with therapeutic eculizumab for aHUS recurrence; (**D**) the incidence of allograft loss due to TMA after kidney transplantation among patients with therapeutic eculizumab for aHUS recurrence.

#### **4. Discussion**

Atypical HUS recurrence after transplant is diagnosed with the presence of laboratory abnormalities such as renal failure, microangiopathic hemolytic anemia, thrombocytopenia and microvascular occlusion [1,48]. In many cases, aHUS recurrence is confirmed with a kidney biopsy–showing the usual findings of glomerular intracapillary thrombosis, congestion, endothelial swelling, and thickening of the capillary wall [4].

Our systematic review showed that mutations to genes encoding *CFH*, followed by mutations in *CFI* genes were the most commonly reported in patients with aHUS recurrence after transplant. This observation, however, is limited to the studies included in our analysis, which only included kidney transplant patients treated with eculizumab and did not include patients treated with alternative therapies. In our systematic review, mutations in *CFH* and *CFH*/*CFHR1* were more commonly associated with graft loss in patients who had received eculizumab therapy. The current literature describes the presence of complement factor genetic mutations in approximately 30% of patients who present with de novo HUS after kidney transplantation [6]. However, no mutations were identified in four patients who presented with de novo aHUS and had graft loss [47,49].

We found that in most pretransplant patients with a history of aHUS who had received eculizumab prophylaxis, this was started on the day of kidney transplant surgery (data abstracted from Mallet et al. [47] and Manani et al. [49] were not utilized for this subgroup analysis as only one case in each publication was prophylactically managed with eculizumab). Less than 6% of the patients who received eculizumab prophylaxis presented with aHUS recurrence, and recurrence was more likely to occur when eculizumab was discontinued. Yelken et al. reported their clinical experience in a retrospective cohort of seven patients with a prior history of aHUS who had received eculizumab prophylaxis and underwent kidney transplantation. None of them had presented with aHUS recurrence or graft loss during the follow-up period of 28 months [52]. More recently, a global registry for aHUS enrolled 1549 patients over a 5-year period. One hundred and eighty-eight patients underwent kidney transplantation. From those, 88 patients received eculizumab before or during transplant surgery. This group of patients presented significantly better graft function when compared to those patients who received eculizumab with aHUS recurrence or de novo aHUS in the post-transplant period [15].

The time for recurrence of aHUS after transplant in the eculizumab therapeutic group varied, with recurrence seen as early as 3 days and up to 6 years (median of 2 months). Our study shows that allograft outcomes of patients treated after early recurrence (3 days to 3 months post-transplant) [25,28,42,44,49] when compared to those patients treated for late recurrence (29–96 months post-transplant) [15,47,50] were similar. The effect of timing for therapeutic eculizumab is also unclear. Some studies have concluded that early therapy with eculizumab for aHUS recurrence has failed to show better allograft survival [9,47,48]. Future prospective randomized control trials would help elucidate whether early initiation of therapy has a beneficial effect for allograft survival.

In many cases, management for aHUS recurrence in kidney transplantation includes the use of PLEX. Previously, Le Quintrec et al. had reported a trend towards decreased aHUS recurrence in patients treated preemptively with PLEX [44]. Zuber et al.'s retrospective cohort had subsequently compared PLEX versus eculizumab in both therapeutic and prophylactic modalities. They found that eculizumab was superior when compared to PLEX in preventing and treating aHUS recurrence after kidney transplantation [51]. Other studies have shown no difference in graft survival independently of whether patients received PLEX or not [44,51]. More recently, Favi et al. reported a case-control study where patients with and without PLEX were compared with eculizumab prophylaxis alone. This is the only study that we encountered where a comparison group was used. Patients who did not receive eculizumab (whether they received prophylaxis with PLEX or not) were more likely to have higher graft loss recurrence and allograft rejection when compared to patients who had received eculizumab [54]. Our systematic review showed that 36.2% of individuals required eculizumab after PLEX therapy, while 23.1% received concomitant PLEX in the prophylaxis group. There is no data available regarding the use of PLEX in the rest of the cases. Those patients who received PLEX plus eculizumab had no significant difference in allograft survival and aHUS recurrence in comparison to those without PLEX, (*p* = 0.65). However, it remains difficult to conclude about the effects of concomitant PLEX and eculizumab use in aHUS post-transplant recurrence and associated graft loss due to the small number of patients analyzed, and these patients of interest may have had more severe

risk markers that prompted dual therapy. PLEX is still considered an option in the management for aHUS recurrence [15].

Duration of eculizumab therapy has been controversial and so far is based on expert opinion, as there is no strong evidence to support lifelong therapy. We found that most patients who were prophylactically treated had continued eculizumab up to the time of their respective studies' publication. There is a report of one patient in whom no complement mutation was identified that stopped prophylactic eculizumab after 28.7 months. No aHUS recurrence was identified after 9 months of follow up [46]. The median duration of therapy was 18.9 months in the therapy patient group and 21 months in the prophylaxis patient group. While aHUS allograft recurrence after eculizumab cessation was reported in 5.2% of patients, it is difficult to conclude if other recurrent aHUS cases were missed due to the length of follow up after eculizumab therapy discontinuation.

Acute allograft rejection rates were similar in treatment and prophylactic groups. Despite receiving eculizumab treatment for aHUS post-transplant recurrence, graft loss was seen in 20.3% of the patients compared to 7.9% of patients who received eculizumab prophylaxis. While graft loss in the treatment group was likely associated with recurrence of aHUS, in the prophylaxis group, two cases of kidney allograft loss were associated with the presence of intestinal hemorrhage (one case immediately after transplant [45], the other case presented four months after transplant [47]). Whether there was a possible association of acute arterial allograft thrombosis with aHUS remains unclear.

We acknowledge the limitations inherent in this study. Firstly, this systematic review included only observational studies (cohorts and case series). Consequently, the majority of available studies also lacked a comparison (control) group. Only one of the included studies had a comparison group who did not receive eculizumab [54]. Recently, Duineveld C. et al. described 17 patients with aHUS who underwent living donor kidney transplantation without prophylactic eculizumab with median follow-up of 25 months after kidney transplantation [8]. Their institution's protocol emphasized lower target tacrolimus level and blood pressure control among these patients with aHUS. They found only one patient developed aHUS recurrence at 68 days after kidney transplantation, and was treated successfully with eculizumab. The investigators suggested that living donor kidney transplantation in aHUS without prophylactic eculizumab treatment appears feasible [8]. Secondly, inconsistencies in the reporting of certain variables such as type of donor, history of previous transplantation, genetic mutations, and immunosuppression regimen can make it difficult to draw firm associations between eculizumab prophylaxis or treatment on renal graft outcomes. Caution should be exercised in interpreting these data. Lastly, this study analysis was conducted on a highly selected study population. Only studies in which eculizumab was used were analyzed, and individuals who received an alternate treatment course or did not receive eculizumab were excluded from our analysis. Therefore, the frequency of mutations might not be representative. Although funnel plots and Egger's test of event rates demonstrated no statistical significance, a low risk of publication bias cannot be implied given the lack of comparison groups. Thus, future studies to assess the responsiveness to eculizumab based on certain aHUS genetic mutations (low risk vs. moderate risk vs. high risk of recurrence) [6] are needed.

In summary, our study describes the outcomes observed with use of eculizumab for prevention and treatment of aHUS recurrence in kidney transplantation. Our results suggest a possible advantageous role for anti-C5 antibody therapy in the prevention and management of recurrent aHUS. Future prospective studies and clinical trials are needed to evaluate for the efficacy of eculizumab, timing of initiation, and duration of prophylaxis and treatment therapy according to genetic mutations.

**Supplementary Materials:** The supplementary materials are available online at http://www.mdpi.com/2077-0383/ 8/7/919/s1.

**Author Contributions:** Conceptualization, M.L.G.S., C.T. and W.C.; Data curation, M.L.G.S., C.T. and W.C.; Formal analysis, W.C.; Investigation, M.L.G.S. and C.T.; Methodology, M.L.G.S. and W.C.; Project administration, N.L. and T.B.; Resources, M.A.M., N.L. and T.B.; Software, T.B. and W.C.; Supervision, M.A.M., N.L. and W.C.; Validation, M.L.G.S., C.T. and W.C.; Visualization, M.A.M. and W.C.; Writing, original draft, M.L.G.S.; Writing, review & editing, M.L.G.S., C.T., M.A.M. and W.C.

**Acknowledgments:** All authors had access to the data and played essential roles in writing of the manuscript. **Conflicts of Interest:** The authors deny any conflicts of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Review*

### **Timing of Ureteric Stent Removal and Occurrence of Urological Complications after Kidney Transplantation: A Systematic Review and Meta-Analysis**

### **Isis J. Visser 1, Jasper P. T. van der Staaij 1, Anand Muthusamy 1,2, Michelle Willicombe 1, Je**ff**rey A. Lafranca 1 and Frank J. M. F. Dor 1,2,\***

Received: 23 April 2019; Accepted: 13 May 2019; Published: 16 May 2019

**Abstract:** Implanting a ureteric stent during ureteroneocystostomy reduces the risk of leakage and ureteral stenosis after kidney transplantation (KTx), but it may also predispose to urinary tract infections (UTIs). The aim of this study is to determine the optimal timing for ureteric stent removal after KTx. Searches were performed in EMBASE, MEDLINE Ovid, Cochrane CENTRAL, Web of Science, and Google Scholar (until November 2017). For this systematic review, all aspects of the Cochrane Handbook for Interventional Systematic Reviews were followed and it was written based on the PRISMA-statement. Articles discussing JJ-stents (double-J stents) and their time of removal in relation to outcomes, UTIs, urinary leakage, ureteral stenosis or reintervention were included. One-thousand-and-forty-three articles were identified, of which fourteen articles (three randomised controlled trials, nine retrospective cohort studies, and two prospective cohort studies) were included (describing in total *n* = 3612 patients). Meta-analysis using random effect models showed a significant reduction of UTIs when stents were removed earlier than three weeks (OR 0.49, CI 95%, 0.33 to 0.75, *p* = 0.0009). Regarding incidence of urinary leakage, there was no significant difference between early (<3 weeks) and late stent removal (>3 weeks) (OR 0.60, CI 95%, 0.29 to 1.23, *p* = 0.16). Based on our results, earlier stent removal (<3 weeks) was associated with a decreased incidence of UTIs and did not show a higher incidence of urinary leakage compared to later removal (>3 weeks). We recommend that the routine removal of ureteric stents implanted during KTx should be performed around three weeks post-operatively.

**Keywords:** kidney transplantation; urological complications; ureteric stent; urinary tract infection; timing of removal

#### **1. Introduction**

Kidney transplantation (KTx) is considered the best option for end-stage renal disease (ESRD) management [1,2]. Kidney transplantation increases the life expectancy and quality of life of ESRD patients significantly compared to dialysis [3]. However, KTx is not without peri-operative complication risks; urinary tract infections (UTIs), urinary leakage, and ureteral stenosis are the most frequently seen urological complications. These complications are likely to compromise graft functions [4–7]. In order to minimize leakage and stenosis, in general, a stent is inserted in the ureter during implantation. Ureteric stents decrease the risk of these urological complications by five to ten-fold. [6,8,9] Most

centres use a variation of a JJ-stent (double-J stent), with the typical pigtail end preventing stent migration by positioning one end in the pyelum. The other end remains in the bladder after the ureteroneocystostomy (UNC) is created. The stent can easily be removed by flexible cystoscopy later. Various randomised controlled trials demonstrate that JJ-stenting reduces urinary leakage and ureteral stenosis [5,10–12]. Meta-analyses by Wilson and Mangus [6,9] also confirmed these results. Although the use of a JJ-stent reduces the risk of urinary leakage and ureteral stenosis, it may also predispose to UTIs [12–16]. In the existing literature, there is no consensus about the preferred time of stent removal. The European Association of Urology states in its renal transplantation guideline that stent retention longer than 30 days is associated with an increased risk (6% versus 40%) of UTIs [12,17]. Therefore, the guideline advises stents to be removed earlier than six weeks post-transplant (which is protocol in most transplant centres) rather than later. Therefore, in the last two years, studies have started to investigate different timings of stent removal within one-month post-transplant [8,18–23]. Recently, Thompson et al. [24] published a review regarding this topic; however, the authors included fewer studies and did provide a robust conclusion about timing of stent removal as their focus was more on the difference between per-urethral and bladder indwelling stents.

The aim of this systematic review is to give a comprehensive overview of currently available literature and to investigate if meta-analysis can elucidate whether a more definite timing for stent removal can be determined.

#### **2. Methods**

All aspects of the Cochrane Handbook for Interventional Systematic Reviews were followed, and the study was written according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [25,26]. Details of the protocol for this systematic review were registered on PROSPERO (ID: CRD42018079867) and can be accessed online [27].

#### *2.1. Literature Search Strategy*

A literature search for all articles regarding JJ-stenting after KTx was performed in EMBASE, MEDLINE Ovid, CENTRAL (the Cochrane Library 2017, issue 11), Web of Science, and Google Scholar. The search was performed for articles published up to November 2017 relevant to outcomes of ureteric stent placement and timings of removal. The search strings for each respective database are attached in Appendix A. Reference lists of the identified relevant articles were manually scrutinized to ensure that no articles were missed.

#### *2.2. Literature Screening*

Study selection was performed independently by two authors (J.P.T.v.d.S. and I.J.V.). Study inclusion was carried out in two phases: after an initial title and abstract selection, full articles of the abstracts regarded as potentially eligible were retrieved and underwent complete review and assessment until a final inclusion was made. When a discrepancy in inclusion between the two authors occurred, articles were discussed with two senior authors (J.A.L., F.J.M.F.D.) in order to reach a consensus.

#### *2.3. Data Extraction*

Studies were assessed for timing of stent removal and the incidence of UTIs, urinary leakage, ureteral stenosis, and reintervention. Other parameters that were assessed were donor type, mean recipient age, the type of stent, technique of UNC, technique of stent removal, immunosuppressive therapy and antibiotic prophylaxis regimen. When the type of stent was not specified in a particular article, we reached out to the authors. If the authors did not respond, we noted the stent-type as "unspecified" but did not mark it as an exclusion criterium.

Studies were only included if they indicated time of stent removal and at least one of the following outcome parameters: UTI, urinary leakage, and/or ureteral stenosis. If a study stated an outcome as "major urological complication" (MUC) and we were not able to define whether this included stenosis or leakage, we analysed this outcome parameter separately as MUC.

To define UTI, we used the Guideline for Urological Infections of The European Association of Urology [28]. It states that a positive urine culture with a bacterial colony count of more than 105 colony-forming units per mL urine is defined as a UTI. However, if patients had lower counts of colony-forming units per mL urine but were reported to have symptoms of a UTI, we chose not to exclude them. When authors of the articles did not define a UTI, we assumed that the official definition was used.

We did not find an official guideline defining urinary leakage and ureteral stenosis. However, Dominguez et al. [10] stated the following definitions for urinary leakage and ureteral stenosis: leakage is defined as drainage or accumulation of perirenal fluid with characteristics of urine and ureteral stenosis is defined as impairment of adequate kidney drainage demonstrated at ultrasound (US) or intravenous pyelogram. When authors of the articles did not define urinary leakage and/or ureteral stenosis, we assumed the abovementioned definitions.

#### *2.4. Critical Appraisal*

The level of evidence of each selected paper was established using the GRADE tool [29]. The GRADE approach defines the quality of a body of evidence by consideration of within study risk of bias (methodological quality), directness of evidence, heterogeneity, precision of effect estimates, and risk of publication bias.

#### *2.5. Statistical Analysis*

Articles were assessed as to whether they were suitable for quantitative analysis. If articles compared two or more groups with different timings of stent removal and if those groups could be divided in an early and late timing of stent removal with a cut off at three weeks, they were included for meta-analysis.

Review Manager Software (RevMan 5.3; The Nordic Cochrane Centre, Copenhagen, Denmark) was used for meta-analysis [30]. Each study was weighted by sample size. Heterogeneity of time of stent removal effects between studies was tested using the Q (heterogeneity χ2) and the *I* 2 statistics. A random effects model was used for calculating the summary estimates (odds ratio (OR)) and 95% confidence intervals (CIs) to account for possible heterogeneity. Overall, the effects were determined using a Z-test. In addition, sensitivity analyses were performed to examine whether removing a particular study would significantly change the results and were presented in funnel plots.

#### **3. Results**

Of the 1043 articles identified from the initial literature search, fourteen articles were within the scope of this systematic review; three randomised controlled trials (RCTs) [20,21,31], nine retrospective cohort studies [8,13,19,22,23,32–35] and two prospective studies [36,37]. A total of 3216 patients were included, of which 2406 patients (74.8%) underwent living donor KTx (two studies did not record if they used living or deceased donors [34,35]). Table 1 presents an overview of the included studies. In Figure 1, the PRISMA flowchart diagram for systematic reviews is presented. The quality assessment of the included studies is depicted in a Summary of Findings table in Figure 2.

We could not specify the type of stent in five studies. The authors of these five papers were contacted, but unfortunately, we received no response [19,34–37]. All studies reported the age of the recipients, except for the preliminary results in the three included abstracts [34–36]. Only four studies reported that they both included adults and children [19,21,23,37]. All articles described the incidence of UTIs, and nine articles also reported urinary leakage and/or ureteral stenosis [19–21,31–33,35–37]. Table 2 presents the incidence of the different outcome parameters for each study. Regarding the used UNC technique, seven out of the fourteen articles described the use of the Lich–Gregoir technique [13,20–23,31,32]. Seven articles did not specify which surgical technique was used [8,19,33–37]. Regarding stent removal,

ten articles used a cystoscopy for removal [8,20–23,32–36]. In four studies, the stent was removed along with the urinary catheter seven days after transplantation [21,34–36]. Two studies removed the stent on the seventh day posttransplant by pulling on strings that were placed around the stent at the time of transplantation [8,20]. Four studies did not describe the technique of stent removal [13,31,37,38]. Ten studies reported the use of antibiotic prophylaxis to protect against UTI after KTx, of which seven studies also reported the duration of antibiotic prophylaxis regimen [13,20,21,23,31–33]. Six studies specified the use of co-trimoxazole as prophylaxis [13,21–23,31,32]. Four studies did not record the use of prophylactic antibiotics at all [8,36–38]. Seven studies reported which immunosuppressive regimen KTx recipients received [8,20,22,23,31–33]. An overview of all the characteristics of the included studies can be found in Table A1 in Appendix A. Four studies could not be included in the meta-analysis because the timing of stent removal was much earlier (e.g., earlier and later than four days) or much later (e.g., earlier and later than seven weeks) than the cut-off value of three weeks [18,39–41].

**Table 1.** Overview of the included studies.

RCT: Randomised controlled trial. (1) Abstract of the 17th Congress of the European Society for Organ Transplantation; (2) Abstract of the the World Transplant Congress 2014; and (3) Abstract of the 16th Congress of the European Society for Organ Transplantation.

**Figure 1.** Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart.

**Figure 2.** Quality assessment of the included studies.

**Table 2.** Overview of the measured outcome parameters.

\* Defined as urinary tract infection (UTI).

#### *3.1. Urinary Tract Infection*

Fourteen studies described the incidence of UTIs with different timings of stent removal and were included for meta-analysis (a total of 3216 patients). There was a significant difference between the groups in the risk of developing UTIs, favouring early stent removal (OR 0.49, CI 95%, 0.33 to 0.75 *p* = 0.0009) (Figure 3). Sensitivity analysis showed no change of significance. (Figure A1, Appendix A).

**Figure 3.** Forest plot of urinary tract infection for early (<3 weeks) versus late (>3 weeks) stent removal.

#### *3.2. Urinary Leakage*

Eight studies described the incidence of urinary leakage: three RCTs [20,21,31], one prospective study [37] and four retrospective studies [32,33,35,38]. One of these studies described zero events of urinary leakage; therefore, seven studies remained formeta-analysis, with a total of 1505 patients [21,31–33,35,37,38]. After pooling the data, there was no significant difference between groups in the risk of developing urinary leakage (OR 0.60, CI 95%, 0.29 to 1.23, *p* = 0.16) (Figure 4). Sensitivity analysis showed no change in significance (Figure A2, Appendix A).

**Figure 4.** Forest plot urinary leakage for early (<3 weeks) versus late (>3 weeks) stent removal.

#### *3.3. Ureteral Stenosis*

Five studies described the incidence of ureteral stenosis [20,21,32,33,36]. Three out of these seven studies reported zero incidents of ureteral stenosis in both groups [20,32,33]. Patel et al. [21] described one case of ureteral stenosis in both the early and late group of stent removal (1.2% and 0.8%, respectively). Gunawansa et al. [36] reported two cases of ureteral stenosis in the late stent removal group (1.1%). No meta-analysis was performed given the low incidence of ureteral stenosis.

Dadkhah et al. [37] and Asgari et al. [19] recorded the incidence of hydronephrosis; however, they did not describe the cause of the hydronephrosis. Dadkhah et al. [37] reported eleven cases in the early stent removal group (3.4%) versus three (2.8%) in the late group of stent removal (*p* = 0.122). Asgari et al. [19] reported, respectively, seven (11.5%) and four (13.3%) cases in the early and late group of stent removal (*p* = 0.71).

Some studies only reported MUC without defining whether this was urinary leakage or ureteral stenosis [13,23,34]. We decided to perform an additional meta-analysis of MUC. We included data from those studies and combined ureteral stenosis and urinary leakage in a single MUC category. After pooling the data, there was no significant difference between groups in the risk of developing major urological complications (OR 1.01, CI 95%, 0.45 to 2.27, *p* = 0.98) (Figure A3, Appendix A). However, we think that ureteral stenosis and urinary leakage are fundamentally different because these complications have a different pathophysiology, so we should be careful with interpretation of these combined outcome parameters.

#### *3.4. Reintervention*

Yuksel et al. [23] described the incidence of surgical reintervention because of urological complications after renal transplantation at four different timings of stent removal. There was a clear difference between early (less than three weeks) and late (more than three weeks) stent removal (6.3% versus 1.3%). Patel et al. [21] reported three cases (3.7%) of major urological complications that required surgical revision in the early (five days) versus one case (0.8%) in the late (28 days) stent removal group. Indu et al. [31] reported one case (2.0%) of urinary leakage that required percutaneous nephrostomy in the early stent removal group. Huang et al. [33] reported two cases (1.1%) of urinary leakage that required surgical revision in both the early and late stent removal groups.

Verma et al. [32] reported zero surgical reintervention after major urological complications in both early and late stent removal group (two and four weeks, respectively).

Soldano et al. [35] and Liu et al. [20] investigated surgical reimplantation of the JJ-stent; Soldano et al. [35] reported one case (2.1%) of surgical reimplantation of the stent in the late stent removal group (six weeks), whereas Liu et al. did not report any reimplantation in both the early and late stent removal groups (one and four weeks, respectively).

#### **4. Discussion**

There is good evidence that stenting the UNC at the time of KTx is beneficial to reduce major urological complications. Intuitively, transplant professionals feel that ureteric stents should not be in situ for too long to reduce the incidence of infectious complications. However, the optimal timing of stent removal remains unclear. Previous studies show a wide range in the timing of stent removal after KTx (five days until 60 days) [42–45]. It is already known that the incidence of UTIs is higher when a stent is removed later than five weeks (24.6% to 44%) [13,21,22,34,35]. A UTI after KTx is associated with graft loss, higher morbidity rates, increased risk of rejection and increased hospitalisation rates [6,7,46–48]. For this reason, studies have been performed to investigate whether earlier stent removal (e.g., around three weeks) would reduce the incidence of UTIs [8,13,19,20,22,23,31,33,36,37]. We decided to perform a meta-analysis to further investigate if we could define a more optimal timing of stent removal.

Based on the results, we demonstrated that earlier (<three weeks) stent removal shows a significantly lower incidence of UTIs compared to later removal (>three weeks). Furthermore, earlier stent removal does not appear to lead to a higher incidence of urine leakage. Regarding ureteral stenosis and reintervention, no hard statements can be made, since we were not able to meta-analyse the results. However, overall, incidence or ureteral stenosis and reintervention is clearly very low in kidney transplant recipients (~1 and 3%).

#### *4.1. Di*ffi*culty in Anastomosis*

We realize that characteristics of the study population of both donors and recipients varies and that these are factors that can influence the difficulty of the ureteral anastomosis, and therefore, the outcome after KTx. Unfortunately, only a few studies describe the donor and recipient characteristics in detail; type of donor (living/deceased), type of deceased donor (donor after brainstem/circulatory death), pre-emptive status of the recipients and duration of dialysis are often not given.

Almost every study recorded whether they included a living and/or deceased donor. The majority of the included studies only involved living donor KTx [19,20,22,23,31,32,36,37]; Huang et al. [33] only included deceased donor kidneys. Three studies included both deceased and living donors [8,13,21]. Two studies did not record whether they included living and/or deceased donors [34,35].

Furthermore, Patel et al. [21] reported that of the deceased donors, the majority was a DBD—in the early stent group 81.2% and in the late stent group 64.4%. They recorded that in both the early and late stent removal group, 72% of the transplant patients were dialysis dependant before KTx. In the study by Huang et al. [33], all patients were receiving dialysis before KTx. In both groups, around 95% of the transplant patients underwent haemodialysis and 5% peritoneal dialysis. In the early and late stent removal groups, patients were respectively 25.7 and 24.8 months on dialysis prior to transplantation. Coskun et al. [13] only reported that duration of dialysis prior to transplantation varied between 1 and 168 months.

#### *4.2. Urinary Tract Infection*

The incidence of UTIs varied widely between included studies: 0 to 53% [8,13,19–23,31–37]. Most transplant centres used a similar triple-regimen immunosuppressive therapy, consisting of calcineurin inhibitors (tacrolimus or cyclosporine), mycophenolate mofetil and corticosteroids. For all details of the immunosuppressive therapy see Table A1 in appendix A. We noticed that some studies did not describe the immunosuppressive drugs nor the prophylactic antibiotics used. We assumed that in these cases, a calcineurin inhibitor-based triple-immunosuppressive regimen was used, and antibiotic prophylaxis was prescribed. Yuksel et al. [23] attribute the low incidence of UTIs to their strict regime of antibiotic prophylaxis. In addition, Sarier et al. [22] and Wilson et al. [6] stress the importance of prophylactic co-trimoxazole to protect against UTIs after transplantation. Furthermore, they state that previous in vivo and in vitro studies have demonstrated that the antibiotic types fluoroquinolones, aminoglycosides and

beta-lactam antibiotics may be effective in prevention of the biofilm mechanism—a major problem in bacterial stent colonization [22].

Coskun et al. [13] state that UTIs should rather be treated with earlier stent removal opposed to the prescription of antibiotics. We agree with this statement and would advise transplant centres to remove the (potential) source of UTIs earlier rather than later as a best possible way to prevent UTIs.

Some studies investigated very early stent removal, around one week post-transplantation, and it showed promising results, specifically (and maybe only) regarding the incidence of UTIs [20,21,23,31,34,36].

Dadkhah et al. [37] showed a remarkably high incidence of UTIs in the early stent removal group (ten days), which was two times higher than for late stent removal (30 days). Surprisingly, they concluded that removal of the ureteral stent shortly after KTx has a statistically negligible impact on the rate of UTIs. We decided to include their controversial incidence of UTIs in our meta-analysis. However, one should keep in mind that this study had some paucity of data granularity, as the technique of UNC, length of follow up, immunosuppressive regimen, the use of antibiotic prophylaxis and technique of stent removal were not described.

#### *4.3. Ureteral Leakage and Ureteral Stenosis*

Included studies with wide varying timings of stent removal show that ureteral leakage and stenosis are complications with low incidences (0–3%). Three studies described a remarkably high incidence of urinary leakage [32,35,38]. Asgari et al. [38] reported 6.6% urinary leakage in the early stent removal group (ten days) and 13.3% in the late stent removal group (30 days). Soldano et al. [35] reported 6.3% urinary leakage in the late stent removal group (at six weeks). Verma et al. [35] reported 5.8% and 10% urinary leakage in the early (two weeks) and late (four weeks) stent removal groups. Because these remarkably high incidences were derived from (pilot) studies with small patient populations (each group containing respectively around 50 patients), we have to interpret these data very carefully.

Overall, the data implies that both leakage and stenosis can be successfully prevented with insertion of a stent but the duration of the stent being in situ does not have a great influence on the incidence of these urological complications [5,6,10–12]. Huang et al. [33] support this by concluding that stent removal at three weeks is as effective in preventing urological complications as removal at six weeks (with similar prophylactic antibiotics and immunosuppressive therapies). Furthermore, Patel et al. [21] demonstrate that very early stent removal (less than five days) results in a lower incidence of leakage and stenosis than in the un-stented population. Therefore, even when the stent is inserted for a brief period, it already shows benefit in preventing leakage and stenosis. The first two weeks after KTx are believed to have the highest incidence of urinary leakage and ureteral stenosis [8,13,23,32]. Yuksel et al. [23] conclude that stent removal earlier than fourteen days shows a significant increase of recurrent surgical UNC intervention. In addition, Coskun et al. [13] conclude that stent removal at two weeks results in acceptable mucosal healing of the anastomosis to prevent urological complications. In order to keep the incidence of urinary leakage and ureteral stenosis as low as possible, we recommend that stents should not be removed earlier than two weeks.

#### *4.4. Additional Advantages*

In addition to a lower incidence of UTIs, early stent removal has other advantages. Instead of a cystoscopy, stents can be removed less invasively together with the removal of the urinary catheter if tied to it at the time of transplantation. This procedure is considered far more comfortable for the patient [8,20]. Additionally, early stent removal provides the opportunity to remove the stent during the same admission, which leads to a reduction in costs and fewer forgotten stents [5,21,32,33].

#### *4.5. Limitations*

A meta-analysis can only be as good as the quality of the included studies. Unfortunately, most of the included studies are retrospective cohort studies. Only three RCTs were included [20,21,31]. We mention this limitation to alert the reader to carefully interpret the data. In the forest plot analysing urinary tract infections for early (<3 weeks) versus late (>3 weeks) stent removal (Figure 3), one can appreciate the relatively high heterogeneity (*I* 2 = 61%). The cause of this heterogeneity lies in the differences in study design; for example, not defining which type of stent has been used, technique of stent removal, technique of ureteroneocystostomy, use of prophylactic antibiotics or choice of immunosuppressive therapy. The latter two aspects are particularly important factors influencing UTI rates. Furthermore, urinary leakage and ureteral stenosis were not always defined, or no uniform definition was adhered to. This leads to assumptions, which may cause inadequate comparison. Studies often do not report at what time during follow-up, especially before or after stent removal, specific complications occurred.

Recently, Thompson et al. [24] also investigated the benefits of early stent removal. They concluded that early stent removal reduces the incidence of UTIs, while it was uncertain if there is a higher risk of MUC. Furthermore, the authors used incidence of MUC as a primary outcome and UTIs as a secondary outcome. In our study, UTIs and MUC were chosen as, respectively, primary and secondary outcome, because our main focus was to investigate whether early stent removal reduced the disadvantages of stenting (incidence of UTIs), without compromising the beneficial effects (preventing MUC).

Thompson et al. [24] classified early and late stent removal in a different manner; although they mention that early stent removal was defined as stent removal below fifteen days, they did not particularly use this definition in their analyses. The authors copied the "early" and "late" groups from included studies. As a consequence, there is no common cut-off value analysed in their study and meta-analysis.

Furthermore, the focus of their analysis was more on the specific type of stents used (bladder indwelling stent and per-urethral stents). In our opinion, we should first focus on the relation between duration of stenting and the incidence of UTIs rather than the influence of the type of stent.

#### **5. Conclusions**

The results of this systematic review clearly point favourably towards an earlier stent removal around three weeks opposed to the six weeks that is currently used in most transplant centres. Earlier stent removal (at or below three weeks) results in fewer UTIs without negatively affecting the anastomosis between ureter and bladder. We recommend at this stage that ureteric stents should not be removed earlier than two weeks. We would recommend initiating an RCT, randomising between very early stent removal at one week and stent removal at three weeks. Another option would be a three-armed RCT, adding an additional group of stent removal at six weeks.

**Author Contributions:** The study was conceptualized by F.J.M.F.D. and J.A.L., methodology was developed by I.J.V., J.P.T.v.d.S., J.A.L. and F.J.M.F.D. Formal analyses were done by I.J.V., J.P.T.v.d.S. and J.A.L. The original draft was written by I.J.V., J.P.T.v.d.S and J.A.L. The MS was further reviewed and edited by I.J.V., J.P.T.v.d.S., A.M., M.W., J.A.L. and F.J.M.F.D. The project was supervised by J.A.L. and F.J.M.F.D.

**Acknowledgments:** We thank Wichor Bramer for his expert assistance with the systematic literature search.

**Conflicts of Interest:** The authors declare no conflict of interest.

*J. Clin. Med.* **2019**, *8*, 689

#### **Appendix A**

**Figure A1.** Funnel plot of urinary tract infection for early (<3 weeks) versus late (>3 weeks) stent removal.

**Figure A2.** Funnel plot of urinary leakage for early (<3 weeks) versus late (>3 weeks) stent removal.

**Figure A3.** Forest plot of major urological complications for early (<3 weeks) versus late (>3 weeks) stent removal.

**A1.** Overview of the study characteristics.

**Table**

Tac =

(2) including the non-stented population (*n* = 99). \* Recorded that they both included children and adults; ˆ Induction therapy; " Specific type of prophylactic antibiotics.

Tacrolimus, Cyc =

cyclosporine,

 Pred =

antithymocyte

 globulin, Bas =

Basiliximab, Dac =

Daclizumab, Aza =

Azathioprine.

 (1) 20–30, 30–40, 40–50, 50–60 days,

prednisone, ATG =

#### *J. Clin. Med.* **2019**, *8*, 689

#### Search string A

### **Embase.com 944**

('kidney graft'/exp OR 'kidney transplantation'/exp OR (((kidney\* OR renal\*) NEAR/3 (graft\* OR transplant\* OR allograft\* OR allotransplant\* OR recipient\*))):ab,ti) AND ('ureter stent'/exp OR (ureter/de AND stent/de) OR (((ureter\* OR nephroureter\* OR double-j OR jj OR j-j OR pigtail\* OR urinar\*) NEAR/3 stent\*)):ab,ti) NOT ([animals]/lim NOT [humans]/lim)

#### **Medline Ovid 443**

(kidney transplantation/ OR kidney/tr OR (((kidney\* OR renal\*) ADJ3 (graft\* OR transplant\* OR allograft\* OR allotransplant\* OR recipient\*))).ab,ti.) AND ((ureter/ AND stents/) OR (((ureter\* OR nephroureter\* OR double-j OR jj OR j-j OR pigtail\* OR urinar\*) ADJ3 stent\*)).ab,ti.) NOT (exp animals/ NOT humans/)

#### **Cochrane CENTRAL 43**

((((kidney\* OR renal\*) NEAR/3 (graft\* OR transplant\* OR allograft\* OR allotransplant\* OR recipient\*))):ab,ti) AND ((((ureter\* OR nephroureter\* OR double-j OR jj OR j-j OR pigtail\* OR urinar\*) NEAR/3 stent\*)):ab,ti)

#### **Web of Science 268**

TS=(((((kidney\* OR renal\*) NEAR/2 (graft\* OR transplant\* OR allograft\* OR allotransplant\* OR recipient\*)))) AND ((((ureter\* OR nephroureter\* OR double-j OR jj OR j-j OR pigtail\* OR urinar\*) NEAR/2 stent\*))) NOT ((animal\* OR rat OR rats Or mouse OR mice OR murine) NOT (human\* OR patient\*))) AND DT=(article) AND LA=(English)

#### **Google Scholar**

"kidney|renal graft|transplantation|allograft|allotransplant|recipients"

"ureter|ureteral|nephroureteral|jj|pigtail|urinary stent|stents"|"j|double j stent|stents

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Incidence and Mortality of Renal Cell Carcinoma after Kidney Transplantation: A Meta-Analysis**

**Api Chewcharat 1,2, Charat Thongprayoon 3,\*, Tarun Bathini 4, Narothama Reddy Aeddula 5, Boonphiphop Boonpheng 6, Wisit Kaewput 7, Kanramon Watthanasuntorn 8, Ploypin Lertjitbanjong 8, Konika Sharma 8, Aldo Torres-Ortiz 9, Napat Leeaphorn 10, Michael A. Mao 11, Nadeen J. Khoury 12 and Wisit Cheungpasitporn 9**

Received: 31 March 2019; Accepted: 15 April 2019; Published: 17 April 2019

**Abstract:** Background: The incidence and mortality of renal cell carcinoma (RCC) after kidney transplantation (KTx) remain unclear. This study's aims were (1) to investigate the pooled incidence/incidence trends, and (2) to assess the mortality/mortality trends in KTx patients with RCC. Methods: A literature search was conducted using the MEDLINE, EMBASE and Cochrane databases from inception through October 2018. Studies that reported the incidence or mortality of RCC among kidney transplant recipients were included. The pooled incidence and 95% CI were calculated using a random-effect model. The protocol for this meta-analysis is registered with PROSPERO; no. CRD42018108994. Results: A total of 22 observational studies with a total of 320,190 KTx patients were enrolled. Overall, the pooled estimated incidence of RCC after KTx was 0.7% (95% CI: 0.5–0.8%, *I* 2 = 93%). While the pooled estimated incidence of de novo RCC in the native kidney was 0.7% (95% CI: 0.6–0.9%, *I* 2 = 88%), the pooled estimated incidence of RCC in the allograft kidney was 0.2% (95% CI: 0.1–0.4%, *I* 2 = 64%). The pooled estimated mortality rate in KTx recipients with RCC was 15.0% (95% CI: 7.4–28.1%, *I* 2 = 80%) at a mean follow-up time of 42 months after RCC diagnosis. While meta-regression analysis showed a significant negative correlation between year of study and incidence of de novo RCC post-KTx (slopes = −0.05, *p* = 0.01), there were no significant correlations between the year of study and mortality of patients with RCC (*p* = 0.50). Egger's regression asymmetry test was performed and showed no publication bias in all analyses. Conclusions: The overall estimated incidence of RCC after KTX was 0.7%. Although there has been a

potential decrease in the incidence of RCC post-KTx, mortality in KTx patients with RCC has not decreased over time.

**Keywords:** malignancy; post-transplant malignancy; renal cell carcinoma; meta-analysis; kidney transplantation; transplantation; systematic reviews

#### **1. Introduction**

Kidney transplantation (KTx) is the renal replacement therapy of choice for the majority of patients with end-stage renal disease (ESRD) and it significantly improves survival and quality of life [1,2]. The long-term mortality rate is 48% to 82% lower in KTx recipients when compared to ESRD patients on the transplant waitlist [2,3]. However, due to immunosuppression, KTx patients are at a two-fold increased risk of developing malignancy in comparison to the general population [4–6]. Malignancies are among the top three leading causes of death in KTx recipients, following infection and cardiovascular complications [6].

Studies have demonstrated a higher incidence of renal cell carcinoma (RCC) among ESRD patients (0.3%) than its reported incidence in the general population (approximately 0.005%) [7,8]. Thus, the Clinical Practice Guidelines Committee of the American Society of Transplantation (AST) [9] has recommended RCC screening for high-risk candidates, such as ESRD patients on dialysis for more than 3 years [10]. Despite screening for RCC among KTx candidates, de novo RCC has been reported among KTx patients in both native kidneys [11–18], and transplanted kidneys [17,19,20]. However, the incidence and incidence trends of RCC among KTx patients remain unclear [11–42].

Thus, we performed a systematic review to (1) investigate the pooled incidence/incidence trends, and (2) assess the mortality/mortality trends in KTx patients with RCC.

#### **2. Methods**

#### *2.1. Search Strategy and Literature Review*

The protocol for this systematic review is registered with PROSPERO (International Prospective Register of Systematic Reviews; no. CRD42018108994). A systematic literature search of MEDLINE (1946 to October 2018), EMBASE (1988 to October 2018), and the Cochrane Database of Systematic Reviews (database inception to October 2018) was performed to assess (1) the pooled incidence/incidence trends, and (2) to assess the mortality/mortality trends in KTx patients with RCC. The systematic literature review was conducted independently by two investigators (C.T. and W.C) using a search strategy that consolidated the terms "kidney cancer" OR "renal cell carcinoma" AND "kidney transplantation" OR "renal transplantation" which is provided in the online Supplementary Data S1. The database searches were limited to English language articles only. A manual search for conceivably related studies using references of the included articles was also performed. This study was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement [43].

#### *2.2. Selection Criteria*

Eligible studies had to be clinical trials or observational studies (cohort, case-control, or cross-sectional studies) that reported the incidence or mortality of RCC among adult KTx recipients (age >/= 18 years old). Retrieved articles were individually reviewed for eligibility by two investigators (A.C. and C.T.). Discrepancies were addressed and solved by mutual consensus. Inclusion was not limited by the size of study.

#### *2.3. Data Abstraction*

A structured data collecting form was used to obtain the following information from each study: title, name of the first author, year of the study, publication year, country where the study was conducted, RCC definition, incidence of RCC, and mortality in KTx patients with RCC.

#### *2.4. Statistical Analysis*

Analyses were performed utilizing the Comprehensive Meta-Analysis 3.3 software (Biostat Inc, Englewood, NJ, USA). Adjusted point estimates from each study were consolidated by the generic inverse variance approach of DerSimonian and Laird, which designated the weight of each study based on its variance [44]. Given the possibility of between-study variance, we used a random-effect model rather than a fixed-effect model. Forest plots were constructed to visually evaluate the incidence and mortality of RCC among adult KTx recipients. Cochran's Q test and *I* 2 statistic were applied to determine the between-study heterogeneity. A value of *I* 2 of 0–25% represents insignificant heterogeneity, 26–50% low heterogeneity, 51–75% moderate heterogeneity and 76–100% high heterogeneity [45]. The presence of publication bias was assessed using the Egger test [46]. Funnel plots were created to evaluate for the presence or absence of publication bias.

#### **3. Results**

A total of 7815 potentially eligible articles were identified using our search strategy. After the exclusion of 7629 articles based on their title and abstract for clearly not fulfilling the inclusion criteria on the basis of type of article, patient population, study design, or outcome of interest, and 81 due to being duplicates, 105 articles were left for full-length review. Fifty-nine of them were excluded from the full-length review as they did not report the outcome of interest. Twenty-one articles were case reports and three articles were not in English. Thus, 22 cohort studies [11–20,23,28–36,38,39] with a total of 320,190 KTx patients were enrolled. The literature retrieval, review, and selection process are demonstrated in Figure 1. The characteristics of the included studies are presented in Table 1.

**Figure 1.** Outline of our search methodology.

*8*, 530

#### *J. Clin. Med.* **2019**, *8*, 530

**Table 1.** *Cont.*

#### *3.1. Incidence of RCC after KTx*

Eighteen studies provided data on the incidence of RCC after KTx [11–20,28,29,31,32,34,35]. Overall, the pooled estimated incidence of RCC after KTx was 0.7% (95% CI: 0.5–0.8%, *I* 2 = 93%, Figure 2). While the pooled estimated incidence of de novo RCC in the native kidney was 0.7% (95% CI: 0.6–0.9%, *I* 2 = 88%, Figure 3A), the pooled estimated incidence of RCC in the allograft kidney was 0.2% (95% CI: 0.1–0.4%, *I* 2 = 64%, Figure 3B).

**Figure 2.** Forest plots of the included studies [11–20,28,29,31,32,34,35,38,39] assessing incidence rates of RCC after KTx. A diamond data marker represents the overall rate from each included study (square data marker) and 95% confidence interval.

**Figure 3.** Forest plots of the included studies [11–18,28,29,31,32,34,35,38,39] assessing incidence rates of (**A**) de novo RCC in the native kidney and (**B**) RCC in the allograft kidney [17,19,20,38,39]. A diamond data marker represents the overall rate from each included study (square data marker) and 95% confidence interval.

Meta-regression showed a significant negative correlation between year of study and incidence of de novo RCC post-KTx (slopes = −0.05, *p* = 0.01, Figure 4).

**5HJUHVVLRQRI/RJLWHYHQWUDWHRQ<HDU**

**Figure 4.** Meta-regression analyses showed a significant negative correlation between the year of study and incidence of de novo RCC post-KTx (slopes = −0.05, *p* = 0.01). The solid line represents the weighted regression line based on variance-weighted least squares. The inner and outer lines show the 95% confidence interval and prediction interval around the regression line. The circles indicate the log event rates in each study.

#### *3.2. Mortality Rate in KTx Recipients with RCC*

Eleven studies provided data the on mortality rate in KTx recipients with RCC [13,14,16,17,19,20,23,30,33,36,39]. Overall, the pooled estimated mortality rate in KTx recipients with RCC was 15.0% (95% CI: 7.4–28.1%, *I* 2 = 80%, Figure 5) at a mean follow-up time of 42 months after RCC diagnosis. The data on the incidence and mortality of recurrent RCC among KTx recipients with a previous history of RCC prior to KTX were limited. A prior study demonstrated an incidence of recurrent RCC after KTX of 9.1% with an associated 5-year survival of 41.7% [23]. Sensitivity analysis, excluding the study of recurrent RCC among KTx recipients with a previous history of RCC prior to KTX (23), demonstrated a pooled estimated mortality rate of 11.5% in KTx recipients with RCC (95% CI: 6.4–19.8%, *I* 2 = 67%).

**Figure 5.** Forest plots of the included studies [13,14,16,17,19,20,23,30,33,36,39] assessing mortality rate in KTx recipients with RCC. A diamond data marker represents the overall rate from each included study (square data marker) and 95% confidence interval.

Meta-regression showed no significant correlations between the year of study and mortality of patients with RCC (*p* = 0.50, Figure 6). When meta-regression was performed excluding the study of recurrent RCC among KTx recipients with a previous history of RCC prior to KTX [30], there were still no significant correlations between the year of study and mortality of patients with RCC (*p* = 0.56, Figure 7).

#### **5HJUHVVLRQRI/RJLWHYHQWUDWHRQ<HDU**

**Figure 6.** Meta-regression analyses showed no significant correlations between the year of study and mortality of patients with RCC (*p* = 0.50). The solid line represents the weighted regression line based on variance-weighted least-squares. The inner and outer lines show the 95% confidence interval and prediction interval around the regression line. The circles indicate the log event rates in each study.

#### **5HJUHVVLRQRI/RJLWHYHQWUDWHRQ<HDU**

**Figure 7.** Meta-regression analyses, excluding the study of recurrent RCC among KTx recipients with a previous history of RCC prior to KTX, showed no significant correlations between the year of study and mortality of patients with RCC (*p* = 0.56). The solid line represents the weighted regression line based on variance-weighted least-squares. The inner and outer lines show the 95% confidence interval and prediction interval around the regression line. The circles indicate log event rates in each study.

#### *3.3. Evaluation for Publication Bias*

Funnel plots (Supplementary Figures S1 and S2) and Egger's regression asymmetry tests were performed to evaluate publication bias in the analysis evaluating the incidence and mortality of KTx recipients with RCC. There was no significant publication bias, with *p*-values of 0.58 and 0.54, respectively.

#### **4. Discussion**

In this systematic review, we found that RCC after KTx occurs with an incidence of 0.7%. RCC can occur in the native kidney with an incidence of 0.7% or in the allograft kidney with an incidence of 0.2%. Our findings also showed a statistically significant negative correlation between the incidence of RCC after KTx and study year, representing a potential decrease in the RCC incidence among KTx patients. However, mortality in KTx patients with RCC has not decreased over time.

Post-KTx malignancy is a common cause of death [5,6,47–51] and RCC is the most common solid-organ malignancy in this population [52,53]. Due to the increased risk of RCC among ESRD patients [7,8], the Clinical Practice Guidelines Committee of the AST has suggested RCC screening in ESRD patients on dialysis for longer than 3 years [9,10]. In addition, it is suggested that most KTx candidates with a history of RCC should wait at least 2 years from successful cancer treatment to KTx (unless candidates have only small localized incidental tumors, which may not require any waiting period) [54,55]. Candidates with large, invasive or symptomatic RCC may require a longer waiting period of 5 years [54,55]. Despite RCC screening prior to KTx, the findings from our study suggest that RCC can still occur post-KTx at a higher incidence (0.7%) than its reported incidence among ESRD patients (0.3%) [8]. In addition, studies have demonstrated that KTx recipients have a relative increased risk of five- to ten-fold for RCC compared with an age-matched general population, and that the majority of these tumors arise in the setting of acquired kidney cystic disease (AKCD) which develops with chronic renal failure [5,8,35,56–64]. Although RCC occurrence is more frequent in the native kidneys of KTx recipients, RCC can also occur in the renal allograft (incidence of 0.2%) [17,19,20].

While the exact etiology of the increased risk of RCC in KTx remains unclear, it is likely linked to the immunosuppressed state [4]. Reported risk factors for post-KTx RCC include older age, male sex, African descent, excess body weight, smoking, hypertension, history of acquired cystic kidney disease (ACKD), previous RCC prior to KTx, and longer pre-transplant dialysis duration [3,6,18,23,29,31,34,35,65–67]. Studies have shown that causes of ESRD before KTx may also affect the incidence of post-KTx RCC [14,32,35]. While KTx recipients with ESRD due to glomerulonephritis, hypertensive nephrosclerosis, and vascular diseases have been shown to have a higher incidence of post-KTx RCC, recipients with ESRD due to diabetic nephropathy carry a lower risk of post-KTx RCC [14,32,35,68]. KTx recipients are usually under intensified medical surveillance and the higher incidence of RCC among KTx recipients compared to general populations and ESRD patients might be due to detection bias. On the other hand, the lack of consensual RCC screening among KTx recipients may also have underestimated the exact incidence among the KTx patient population. Currently, there are no universal recommendations for RCC screening among KTx patients [3,22,69–72]. While the European Renal Best Practice (ERBP) guidelines recommend native kidney ultrasound as RCC screening in kidney transplant recipients, and the European Association of Urology (EAU) recommends an annual ultrasound of native kidneys and allografts for anyone with ACKD, previous RCC, or von Hippel–Lindau disease [3,71,72], the Kidney Disease Improving Global Outcomes (KDIGO) and AST guidelines for post-KTx care currently do not suggest universal screening for RCC among KTx recipients [22,69,70]. Thus, there are various RCC screening approaches for KTx recipients at different transplant centers. Many cases of RCC have been discovered during investigations for post-transplant erythrocytosis, elevated serum creatinine, hematuria, urinary infection, or incidentally from imaging for other indications [33,73–75]. The majority of studies with available data on surveillance programs performed screening for RCC post-KTx annually by ultrasonography of native and allograft kidneys. Among KTx recipients with ACKD, acquired multicystic dysplasia, or a prior history of RCC required more frequent screenings, every 6 months [16,17,19,20,28,36,76]. Given that the risk is greatest in the first year post-KTx and the majority of RCCs occur in the first 5 years after KTx [15,29,31,65,77], previous reports suggest that KTx recipients should routinely undergo ultrasonography to screen RCC on the native kidney during the first 30 days post-KTx and every 5 years afterwards in the absence of renal cysts, or every 2 years in the presence of renal cysts [65,77–79]. Our study's findings suggest the need for future studies to identify a cost-effective surveillance strategy for RCC among KTx recipients. This strategy would need to take into consideration both native and allograft kidneys, and differentiate KTx recipients with non-simple renal cysts [3,80].

Several limitations of our systematic review are worth mentioning. First, there are statistical heterogeneities in our meta-analysis. Potential sources for heterogeneities were the variations in the renal transplant recipient screening methods, patient characteristics, and differences in the immunosuppressive regimens used at various transplant centers, which may have affected the incidence of RCC and mortality rate in this population. Second, there is a lack of data from included studies on immunosuppressive regimens [81–85]. Mammalian target of rapamycin (mTOR) inhibitors have shown antineoplastic activities [86]. Although the effects of mTOR among KTx recipients have been shown mostly for non-melanoma skin cancer [87–89], future studies evaluating the effects of different immunosuppressive regimens on mortality in KTx patients with RCC are needed. Lastly, this is a meta-analysis of cohort studies and the data from population-based studies were limited. Thus, large population-based studies evaluating the incidence of RCC in KTx patients are required in the future.

In summary, the overall estimated incidence of RCC after KTX was 0.6%, with an associated high mortality rate in KTx recipients of 13.9%. Despite potential improvements in the post-KTx RCC incidence, the mortality in KTx patients with RCC has remained unchanged over time.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2077-0383/8/4/530/s1, Data S1: Search terms for systematic review; Figure S1: Funnel plot evaluating for publication bias evaluating incidence of KTx recipients with RCC; Figure S2: Funnel plot evaluating for publication bias evaluating mortality of KTx recipients with RCC.

**Author Contributions:** Conceptualization, A.C., C.T., W.K., M.A.M., N.J.K. and W.C.; Data curation, A.C. and C.T.; Formal analysis, C.T. and W.C.; Investigation, A.C., C.T. and W.C.; Methodology, A.C., T.B., B.B., W.K., P.L., N.L. and W.C.; Project administration, K.W. and K.S.; Resources, K.W. and K.S.; Software, K.W.; Supervision, T.B., N.R.A., B.B., W.K., A.T.-O., N.L., M.A., N.J. and W.C.; Validation, A.C., C.T., P.L. and W.C.; Visualization, T.B. and K.S.; Writing—original draft, A.C. and N.R.; Writing—review and editing, C.T., T.B., N.R.A., B.B., W.K., K.W., P.L., K.S., A.T.-O., N.L., M.A.M., N.J.K. and W.C.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

#### *Article*

### **Subarachnoid Hemorrhage in Hospitalized Renal Transplant Recipients with Autosomal Dominant Polycystic Kidney Disease: A Nationwide Analysis**

**Wisit Cheungpasitporn 1,\*, Charat Thongprayoon 2, Patompong Ungprasert 3, Karn Wijarnpreecha 4, Wisit Kaewput 5, Napat Leeaphorn 6, Tarun Bathini 7, Fouad T. Chebib 2 and Paul T. Kröner 4**

Received: 21 March 2019; Accepted: 15 April 2019; Published: 17 April 2019

**Abstract:** Background: This study aimed to evaluate the hospitalization rates for subarachnoid hemorrhage (SAH) among renal transplant patients with adult polycystic kidney disease (ADPKD) and its outcomes, when compared to non-ADPKD renal transplant patients. Methods: The 2005–2014 National Inpatient Sample databases were used to identify all hospitalized renal transplant patients. The inpatient prevalence of SAH as a discharge diagnosis between ADPKD and non-ADPKD renal transplant patients was compared. Among SAH patients, the in-hospital mortality, use of aneurysm clipping, hospital length of stay, total hospitalization cost and charges between ADPKD and non-ADPKD patients were compared, adjusting for potential confounders. Results: The inpatient prevalence of SAH in ADPKD was 3.8/1000 admissions, compared to 0.9/1000 admissions in non-ADPKD patients (*p* < 0.01). Of 833 renal transplant patients with a diagnosis of SAH, 30 had ADPKD. Five (17%) ADPKD renal patients with SAH died in hospitals compared to 188 (23.4%) non-ADPKD renal patients (*p* = 0.70). In adjusted analysis, there was no statistically significant difference in mortality, use of aneurysm clipping, hospital length of stay, or total hospitalization costs and charges between ADPKD and non-ADPKD patients with SAH. Conclusion: Renal transplant patients with ADPKD had a 4-fold higher inpatient prevalence of SAH than those without ADPKD. Further studies are needed to compare the incidence of overall admissions in ADPKD and non-ADPKD patients. When renal transplant patients developed SAH, inpatient mortality rates were high regardless of ADPKD status. The outcomes, as well as resource utilization, were comparable between the two groups.

**Keywords:** autosomal dominant polycystic kidney disease; epidemiology; hospitalization; kidney transplantation; subarachnoid hemorrhage

#### **1. Introduction**

Subarachnoid hemorrhage (SAH) is a major clinical problem worldwide, associated with a poor prognosis, long-term morbidity, and extremely high mortality [1–3]. In the United States, over 30,000 cases of SAH occur annually [4,5]. Although global SAH has decreased each year from 1960 through 2017 [6], approximately 30% to 50% of patients who developed SAH died at 60 days. Furthermore, over 30% of survivors subsequently suffered neurologic deficits post-SAH [1–4,7–9]. Major causes of SAH include ruptured intracranial aneurysm, cerebral arteriovenous malformation, and traumatic brain injury [6,10].

Among patients with autosomal dominant polycystic kidney disease (ADPKD), a disorder that affects the kidneys and other organs caused by mutations in PKD1 and PKD2, a wide spectrum of vascular abnormalities have been described including intracranial aneurysms (and dolichoectasias), thoracic aorta and cervicocephalic artery dissections, and coronary artery aneurysms [11,12]. Compared to the general population, the prevalence of intracranial aneurysms in ADPKD patients is approximately five times higher and is estimated at 4% to 22.5% [13–19]. Among ADPKD patients with a family history of SAH/intracranial aneurysms, the frequency is three to five times higher than the general population [12]. Thus, screening is recommended for intracranial aneurysms among ADPKD patients with (1) family or past medical history of intracranial aneurysm presence or rupture; (2) symptoms suggesting intracranial aneurysm; (3) occupations in which loss of consciousness may be fatal; (4) upcoming major elective surgery; and (5) patient subjective concern for possible intracranial aneurysm presence [20–22].

Despite the recommendation for intracranial aneurysm screening prior to major elective surgery, such screening for ADPKD patients is not consistently performed among all transplant centers in the USA during pre-transplant assessment for renal transplantation [23,24]. In addition, previously published studies on the increased risk of SAH after renal transplantation in patients with ADPKD have not yielded relevant information, or have been underpowered [25–31].

Thus, we conducted this study using a nationwide inpatient USA database to evaluate the hospitalization rates for SAH among renal transplant patients with ADPKD and its outcomes, when compared to non-ADPKD renal transplant patients.

#### **2. Methods**

#### *2.1. Data Source*

The 2005–2014 National Inpatient Sample (NIS) databases were used to conduct this retrospective cohort study. The NIS is the largest inpatient all-payer database that is publicly available in the US. This database was developed by the Agency for Healthcare Research and Quality (AHRQ) as part of its Healthcare Cost and Utilization Project (HCUP). The dataset for the studied years contains more than 78 million hospitalizations, which in itself is a 20% stratified sample of over 4000 non-federal acute care hospitals in 44 states of the United States, and is representative of 95% of hospitalizations nationwide. This dataset included codes for principal diagnosis, secondary diagnoses, and codes for procedures performed during the hospitalization.

#### *2.2. Study Population*

Initially, renal transplant patients were identified using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes of v42.0. The ADPKD status in this cohort was identified using the ICD-9-CM code 753.13. The associated diagnosis of SAH was identified using the ICD-9-CM code 430. Patients undergoing elective hospital admission or patients undergoing renal transplantation during the same admission were excluded from the analysis.

#### *2.3. Variable Definition*

Patient characteristics included age, gender, ethnicity, median income in patients' zip code, family history of stroke, and insurance type. Hospital characteristics included hospital region, teaching status, number of hospital beds, urban location, and weekend admission. The Healthcare Cost and Utilization Project (HCUP) divides the US into four census regions based on geographical location: Northeast, Midwest, South and West. The vital status at the end of hospitalization, length of hospital stay (LOS), and total hospitalization charges were abstracted from the database. To account for patient comorbidities, the Deyo adaptation of the Charlson Comorbidity Index was used, which was appropriate for the large database analysis [32].

#### *2.4. Outcomes*

The outcome for primary analysis was to determine the inpatient prevalence of SAH as a discharge diagnosis in renal transplant patients with ADPKD, compared to renal transplant patients without ADPKD. The outcomes for secondary analysis were to compare in-hospital mortality, the use of aneurysm clipping, length of hospital stay, and expenditures between ADPKD and non-ADPKD patients with SAH. Expenditures included total hospitalization charges and hospitalization costs. Total hospitalization charges represented the amount of financial resources that each hospital billed for providing its service for each patient, whereas hospitalization costs represented the amount of money spent by each hospital in providing the patient care. Hospitalization costs were calculated by multiplying the cost-to-charge ratios for the respective hospitals with the total hospitalization charges. Cost-to-charge ratios were provided by the Healthcare Cost and Utilization Project (HCUP) for each hospitalization in the database in order to enable this calculation. Since this study used datasets for 10 different calendar years, costs and charges were adjusted for inflation using the consumer price index and converting them to 2014 \$USD equivalents.

#### *2.5. Statistical Analysis*

Discharge-level weights on the HCUP nationwide databases were used to estimate the total number of renal transplant patients that had an associated diagnosis of SAH. Descriptive statistics were used to identify the patient characteristics. Fisher's exact test was used to compare proportions. Students' *t*-test was used to compare means. A hybrid multivariate logistic regression model was built by first conducting a univariate regression analysis on variables that were identified from other studies as being relevant to the outcome. If these variables impacted the outcome in any direction with a *p*-value of <0.01, they were included in the multivariate logistic regression model. In multivariate logistic regression, odds ratios and means were adjusted for age, gender, insurance type, family history of stroke, the median income in patients' zip code, hospital region, urban location, number of hospital beds and teaching status. All statistical analyses were performed using STATA, Version 13 (StataCorp LP, College Station, TX, USA).

#### **3. Results**

#### *3.1. Inpatient Prevalence of SAH as a Discharge Diagnosis*

Out of 382,516,561 patients admitted to hospitals during the study period, 918,478 were identified as having had a history of renal transplant. ADPKD patients who underwent renal transplant had higher inpatient prevalence of SAH as a discharge diagnosis than non-ADPKD renal transplant patients (3.8 vs. 0.9 cases per 1000 discharges; *p* < 0.01).

#### *3.2. Patient and Hospital Characteristics in SAH Patients*

In total, 833 patients had an associated diagnosis of SAH. These included 30 ADPKD renal transplant patients and 803 non-ADPKD renal transplant patients. The ADPKD renal transplant patients were older, had higher comorbidity burden, and were more likely to be admitted to teaching hospitals than non-ADPKD renal patients. There was no significant difference in terms of weekend admission, the median income in patients' zip code, insurance type, hospital region, urban location, and number of hospital beds between the two cohorts (Table 1).

**Table 1.** Patient and hospital characteristics.

ADPKD, adult polycystic kidney disease.

#### *3.3. Mortality*

A total of 5 (17%) ADPKD renal transplant patients with SAH died in hospitals compared to 188 (23.4%) non-ADPKD renal transplant patients (*p* = 0.70). Similarly, in adjusted analysis, there was no difference in in-hospital mortality between the two groups, with an adjusted odds ratio (aOR) of 0.87 (95% confidence interval (CI) 0.12–6.23; *p* = 0.89) (Table 2).

**Table 2.** Outcomes and procedures of ADPKD and non-ADPKD patients with SAH.

#### *3.4. Use of Aneurysm Clipping*

Ten (33.0%) ADPKD renal transplant patients with SAH underwent aneurysm clipping, compared to 137 (17.1%) non-ADPKD renal transplant patients (*p* = 0.32). On adjusted analysis, the use of aneurysm clipping in ADPKD renal transplant patients was not significantly higher than non-ADPKD renal transplant patients (aOR: 2.02; 95% CI 0.28–14.81; *p* = 0.49) (Table 2).

#### *3.5. Hospital Length of Stay*

The mean LOS in ADPKD renal transplant patients with SAH was 9.8 days, compared to 8.9 days in the non-ADPKD renal transplant SAH patients. Although the mean additional LOS in ADPKD renal transplant patients with SAH was 3.0 days shorter than non-ADPKD renal transplant patients with SAH, this was not statistically significant (95% CI: −10.1–4.1, *p* = 0.41) on adjusted analysis (Table 3).

**Table 3.** Hospital length of stay and expenditure differences between ADPKD and non-ADPKD patients with SAH.

#### *3.6. Total Hospitalization Costs and Charges*

The mean hospital cost for ADPKD renal transplant patients with SAH was \$ 30,519, while the mean hospital cost for non-ADPKD renal transplant patients with SAH was \$ 33,526 (*p* = 0.04). Although the ADPKD renal transplant patients with SAH had a mean hospital cost \$ 1086 lower than the non-ADPKD renal transplant patients, the difference was not statistically significant (95% CI: −22,548–20,376, *p* = 0.92) on adjusted analysis (Table 3).

The mean total hospitalization charge for ADPKD renal transplant patients with SAH was \$ 85,682, while the mean total hospitalization charges for non-ADPKD renal transplant patients with SAH was \$ 112,514. Although ADPKD renal transplant patients with SAH had a mean hospitalization charge \$ 14,944 lower than the non-ADPKD renal transplant patients, this was not statistically significant (95% CI: −73,293–43,404, *p* = 0.62) on adjusted analysis (Table 3).

#### **4. Discussion**

In this study utilizing the USA Nationwide Inpatient Sample database, we demonstrated that renal transplant patients with ADPKD had a 4-fold higher inpatient prevalence of SAH than those without ADPKD. When renal transplant patients developed SAH, the inpatient mortality rate was high (around 20 to 30%), regardless of ADPKD status. In addition, the use of aneurysm clipping for SAH and hospital LOS were comparable among renal transplant patients with and without ADPKD.

In the previous analysis using USA Renal Data System registry data, Lentine et al. reported a decreased risk of SAH in renal transplant patients when compared to end-stage renal disease (ESRD) patients on the transplant waiting list [33]. Additionally, among ESRD patients on dialysis, it has been shown that ADPKD is associated with an increased risk of SAH [34,35]. Despite an overall reduction

in SAH risk after renal transplantation [33], our study demonstrates a higher relative frequency of SAH among ADPKD patients compared to those without ADPKD and aims to raise awareness that SAH remains an important concern in the post-transplantation population.

The risk of intracranial aneurysms among ADPKD patients increased with age [36,37] and its prevalence is substantially increased after 45 years of age, especially among ADPKD Caucasian patients [38]. The average age of patients with ADPKD and ESRD is greater than 45 years [34,35,38]. However, intracranial aneurysm screening during pre-transplant evaluation for renal transplantation was a requirement for some but not all transplant centers in the USA, despite recommendation for intracranial aneurysm screening prior to major elective surgery [23,24]. Furthermore, a very recent study by Flahault et al. including 495 ADPKD patients suggested that systematic screening was cost-effective and provided a gain of 0.68 quality-adjusted life years compared to targeted screening in only those with a familial history of intracranial aneurysms [21]. The investigators concluded that intracranial aneurysm screening could be proposed to all ADPKD patients regardless of family history of stroke [21].

In our study involving renal transplanted patients, after adjusting for potential confounders including family history of stroke, the relative frequency of SAH among ADPKD patients remained significantly higher compared to those without ADPKD. This may suggest potential development of SAH in renal transplant patients with ADPKD, regardless of family history of stroke. When patients develop SAH, the mortality rate is high regardless of renal transplant status [27,33,39–41]. In our study, nearly 25% of renal transplant patients with SAH died during hospital admission. After adjusting for potential confounders, we found no differences in in-hospital mortality, rate of aneurysm clipping, hospital LOS, hospital costs and total hospitalization charges among ADPKD and non-ADPKD renal transplant patients with SAH.

Several limitations of this study must be acknowledged. Firstly, although the utilization of the NIS database enables an assessment of inpatient relative frequency and burden of SAH in renal transplant patients in the USA, potential inaccuracies in ICD-9-CM coding are significant limitations to our study. Secondly, the data relating to types of mutation among ADPKD patients were limited in this study. Since patients with *PKD2*-associated ADPKD usually develop ESRD at an older age compared to renal-transplanted ADPKD patients (mean age: 79.7 vs. 58.9 years) [22,42,43], it is likely that our study represents the outcomes of SAH among renal transplant patients with *PKD1*-associated ADPKD. Thirdly, this is an analysis of an inpatient USA database and, thus, it does not consider the broader USA outpatient renal transplant population nor the renal transplant population in other countries. Because of the nature of the NIS database, our study included only inpatient renal transplant patients and might be subject to selection bias. Without knowing the total number of all ADPKD and non-ADPKD renal transplant patients, the overall admission rates for any reasons cannot be calculated. Fourthly, although the findings of our study highlight the burden of SAH in ADPKD renal transplant patients, it cannot be concluded that screening all ADPKD patients prior to renal transplantation is cost-effective. Nevertheless, physicians should take into account the risk of SAH among ADPKD renal transplant patients. Furthermore, the practice of intracranial aneurysm screening in ADPKD patients might vary between hospitals. However, information regarding the practice of intracranial aneurysm screening was not available in our study. Lastly, given the administrative nature of the dataset, it was not possible to investigate the effects of medication, such as immunosuppressants, on economic burden and mortality among ADPKD and non-ADPKD renal transplant patients with SAH.

#### **5. Conclusions**

In conclusion, in this study using the USA Nationwide Inpatient Sample database, we demonstrate higher inpatient relative frequency of SAH among ADPKD renal transplant patients when compared with non-ADPKD renal transplant patients. In-hospital mortality due to SAH among renal transplant patients was high, regardless of ADPKD status. The use of aneurysm clipping for SAH and hospital length of stay were comparable between ADPKD renal transplant patients and those without ADPKD. **Author Contributions:** Conceptualization, W.C., N.L., F.T.C. and P.T.K.; Data curation, P.T.K.; Investigation, W.C., C.T., W.K. and P.T.K.; Methodology, W.C., C.T., P.U., K.W., T.B., F.T.C. and P.T.K.; Project administration, W.C. and P.U.; Resources, P.T.K.; Software, P.T.K.; Supervision, N.L., F.T.C. and P.T.K.; Validation, W.C. and P.T.K.; Visualization, N.L.; Writing—original draft, W.C. and C.T.; Writing—review & editing, P.U., K.W., W.K., N.L., T.B., F.T.C. and P.T.K.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

#### *Article*

### **Epidemiology, Risk Factors, and Outcomes of Opportunistic Infections after Kidney Allograft Transplantation in the Era of Modern Immunosuppression: A Monocentric Cohort Study**

**Philippe Attias 1,**†**, Giovanna Melica 2,**†**, David Boutboul 3,4, Nathalie De Castro 5, Vincent Audard 1,6, Thomas Stehlé 1,6, Géraldine Gaube 2, Slim Fourati 7,8, Françoise Botterel 7,8,9, Vincent Fihman 7,8,9, Etienne Audureau 10, Philippe Grimbert 1,6,11 and Marie Matignon 1,6,\***

Received: 13 March 2019; Accepted: 22 April 2019; Published: 30 April 2019

**Abstract:** Epidemiology of opportunistic infections (OI) after kidney allograft transplantation in the modern era of immunosuppression and the use of OI prevention strategies are poorly described. We retrospectively analyzed a single-center cohort on kidney allograft adult recipients transplanted between January 2008 and December 2013. The control group included all kidney recipients transplanted in the same period, but with no OI. We analyzed 538 kidney transplantations (538 patients). The proportion of OI was 15% (80 and 72 patients). OI occurred 12.8 (6.0–31.2) months after transplantation. Viruses were the leading cause (*n* = 54, (10%)), followed by fungal (*n* = 15 (3%)), parasitic (*n* = 6 (1%)), and bacterial (*n* = 5 (0.9%)) infections. Independent risk factors for OI were extended criteria donor (2.53 (1.48–4.31), *p* = 0.0007) and BK viremia (6.38 (3.62–11.23), *p* < 0.0001). High blood lymphocyte count at the time of transplantation was an independent protective factor (0.60 (0.38–0.94), *p* = 0.026). OI was an independent risk factor for allograft loss (2.53 (1.29–4.95), *p* = 0.007) but not for patient survival. Post-kidney transplantation OIs were mostly viral and occurred beyond one year after transplantation. Pre-transplantation lymphopenia and extended

criteria donor are independent risk factors for OI, unlike induction therapy, hence the need to adjust immunosuppressive regimens to such transplant candidates.

**Keywords:** kidney transplantation; opportunistic infection; allograft survival; BK virus nephropathy

#### **1. Introduction**

Kidney allograft recipients are exposed to a broad range of infectious pathogens that give rise to infections with unusual and more severe presentations [1]. Opportunistic infections (OIs) include infections caused by uncommon pathogens and those caused by common pathogens but with unusual and more severe forms [2]. The reported incidence of OIs is variable, from 10% to 25% [3,4]. Currently, prevention strategies against cytomegalovirus (CMV), herpes simplex viruses (HSV), and *Pneumocystis* spp. are recommended and result in a significant reduction of post-transplantation OIs [5] and 50% decrease in the risk of death due to infectious causes. However, infections remain the most common cause of non-cardiovascular deaths (15–20%) [5,6].

After solid-organ transplantation (SOT), OIs flourish in the first 12 months boosted by the immunosuppressive status [2] since less than 20% of SOT recipients receive no induction therapy and up to 60% of kidney transplant recipients receive a T-cell depleting agent [7,8]. Anti-thymocyte globulin primarily induces rapid, profound, and long-lasting depletion of T-lymphocytes in peripheral blood and lymphoid organs, and apparently it does not spare B-cell and NK cell populations [9,10]. Thanks to such therapies, patient and kidney allograft survival after kidney transplantation have markedly improved and acute allograft rejection has decreased [11–13]. On the other hand, one could argue that the long duration of immunosuppression might be the culprit for the increased incidence of OIs.

The epidemiology of OIs after SOT was previously described in two large cohorts on transplant recipients. The first one was conducted 10 years ago and included SOT recipients treated with alemtuzumab [4]. They showed that receiving lung or intestinal transplants was independent risk factors for OIs [4]. Published in the era of modern immunosuppression and after the wide use of prevention strategies, the second study included abdominal SOT recipients (kidney, pancreas, and liver), hence the heterogeneous patient profiles and immunosuppressive regimens [3]. The authors highlighted the delayed onset of OIs where most infections occurred after six months without any impact on recipient's survival and graft function [3]. A recent pediatric cohort on kidney allograft recipients has confirmed the absence of impact of viral OIs (CMV, Epstein Barr virus (EBV), and BK virus (BKV)) on kidney allograft survival [14]. In other studies on kidney allograft recipients, only selected OIs, secondary to specific pathogens (*Nocardia, Aspergillus, Cryptococcus neoformans*), have been reported [15–17].

Given the lack of clinical and epidemiological data on OIs after kidney allograft transplantation, we conducted a large monocentric cohort study on all kidney allograft recipients in our center to analyze the epidemiology of OIs and their impact on kidney recipient survival and allograft function.

#### **2. Materials and Methods**

#### *2.1. Study Design and Patients*

We conducted a single center retrospective cohort enrolling all adult kidney allograft recipients registered between January 2008 and December 2013. We excluded cases with primary allograft non-function happening within seven days after transplantation. Expanded criteria donor (ECD) was defined as donors older than 60 years or between 50 and 60 years, with two of the three following criteria: (i) hypertension; (ii) pre-retrieval serum creatinine > 1.50 mg/dL; and (iii) cerebrovascular cause of brain death [18]. Glomerular filtration rate was estimated (eGFR) using MDRD formula [19]. Acute rejection episodes were classified according to updated Banff classification [20]. Allograft loss was considered if eGFR was below 15 mL/min/1.73 m2. All recipients were followed at least one year after transplantation unless death or graft loss occurred earlier.

#### *2.2. Infectious Prophylaxis*

The management for CMV prophylaxis followed international recommendations [21]. Prophylaxis involved the administration of oral valganciclovir to high (D+/R-) and intermediate (R+ treated with thymoglobulin) risk patients. Duration of prophylaxis was 6 months in high risk patients and 3 months in intermediate ones.

Participants with past history of tuberculosis were treated with isoniazid for three months after transplantation. *Pneumocystis jirovecii* prophylaxis included trimethoprim-sulfamethoxazole (400 mg) or pentacarinat aerosol for 12 months after transplantation and till CD4 count dropped to <200/μL.

#### *2.3. Opportunistic Infections*

OIs were defined according to current literature [1] and international guidelines [22,23]. All episodes were retrospectively and blindly validated (review of all medical reports without the patient name and the final conclusion (clinical and biological data) of infections that happened in kidney-transplant recipients included in the study) by an infectious disease specialist part of the study group. The following OIs were considered:

We included BKV infection, as BK virus, highly seroprevalent in humans, appears to cause clinical disease only in immunocompromised patients and almost all after kidney transplantation (tubulointerstitial nephritis called BKV-induced nephropathy directly related to plasma viral load) [24]. In our center, during the first year after kidney transplantation, BK viruria tests were performed at 1, 2, 3, 6, 9, and 12 months. BK viremia was checked once BK viruria was positive. If BK viruria (associated with BK viremia or not) was positive, a blood test was performed every two weeks.

We also considered Kaposi sarcoma, as one of the four types was organ transplant-associated and usually regresses with reduction in immunosuppression [25].

#### *2.4. Endpoints*

Clinical endpoints were an OI episode, death, and allograft loss. Recipients with at least one episode of OI were compared with the control group which included all other kidney allograft recipients engrafted at the same time period.

#### *2.5. Statistical Analysis*

Continuous variables are presented as mean (± Standard Deviation (SD)) or median (Interquartile Range (IQR)). Categorical variables are presented as counts (%). Baseline donor, recipient, and kidney transplant characteristics were compared between OI and control groups using Student *t*-test or Wilcoxon test for continuous variables, and Chi-2 or Fisher's exact tests for categorical variables, as appropriate. Time-to-event survival analyses were conducted to determine predictors of OI occurrence, patient overall survival, and allograft survival. Survival curves were plotted using Kaplan–Meier method and logrank tests to assess significance upon group comparison. Time varying Cox proportional hazard models were built for each endpoint, and hazard ratios (HR) along with their 95% confidence intervals (95% CI) were calculated. Factors yielding *p* < 0.2 in the univariate analyses were then considered in the multivariate analyses' models, using a stepwise backward approach by sequentially removing variables not significant at *p* < 0.1 until the final model was reached. Variables with available

repeated data over time were entered both as time-fixed (value at the time of transplantation) and as time-varying (all available time points) variables into the Cox model. No imputation of missing data was done. Competing risk survival analysis (e.g., Fine–Gray methodology) cannot be directly applied on time-varying variables, therefore only results from Cox models are reported for allograft survival. All tests were two-tailed, and the significance level was reached with *p* value < 0.05. The analysis was performed using Stata SE v15.1 (College Station, TX, USA).

#### **3. Results**

#### *3.1. Whole Cohort*

A flow-chart of the study population is presented in Figure 1. Between January 2008 and December 2013, 557 kidney transplantations were performed in 557 patients (*n*), of whom 19 showed early primary allograft non-function. Overall, only 538 transplantations in 538 patients were included. Mean age was 52 ± 14 years. Mean follow-up was 55 ± 24 months. At the end of follow-up period, patient survival was 88% with 65 deaths, allograft survival was 87% with 72 allograft losses, and mean eGFR was 48 ± 20 mL/min/1.73 m2. Tables 1 and 2 described the whole cohort.

**Figure 1.** Flow chart of the study population. Between January 2008 and December 2013, 557 kidney transplantations were performed in *n* = 557 patients. Nineteen patients were excluded because of primary allograft non-function within the first week after transplantation. The final cohort included 538 transplantations in 538 patients.

#### *3.2. Opportunistic Infections*

Eighty OI episodes were reported in 15% of patients (*n* = 72). The median time to post-transplantation OI was 12.8 (6.0–31.2) months, and in 39 patients (48.8%), OI occurred over the first post-transplantation year.

Viruses were the leading cause of OI episodes, *n* = 54 (68%), representing 10% of the whole cohort. Median time to viral OI onset was 14 (7–31) months after transplantation. Of those viral OIs, we recorded 21 (39%) shingles (4%-whole cohort), 18 (33%) BKV nephropathy (BKVN) (3%-whole cohort), 6 (11%) Kaposi sarcoma (1%-whole cohort), 3 (6%) CMV disease (0.5%-whole cohort), 3 (6%) norovirus gastroenteritis (0.5%-whole cohort), and 1 (2%) of each of the following: JC virus causing progressive multifocal leukoencephalopathy (PML) (0.2%-whole cohort), VZV retinitis (0.2%-whole cohort), and HSV-1 esophagitis (0.2%-whole cohort).

Fungal infections were the second most common OIs, registered in 15 patients (19%) (3%-whole cohort), in the first 6 (2–25) months after transplantation, which is significantly earlier than viral infections (*p* = 0.04). We counted five (33%) invasive candidiasis (0.9%-whole cohort), four (27%) invasive aspergillosis (IA) (0.7%-whole cohort), three (20%) cryptococcosis (0.5%-whole cohort), two (13%) *Pneumocystosis* pneumonia (PCP) (0.3%-whole cohort), and one (7%) disseminated *Trichophyton Rubrum* infection (0.2%-whole cohort).

Among the six (7%) parasitic infections (1%-whole cohort) occurring 16 (5–23) months after transplantation, four were cryptosporidiosis (0.7%-whole cohort) and two microsporidiosis with gastrointestinal involvement (0.3%-whole cohort). Finally, five (6%) bacterial infections (0.9%-whole cohort) were described, of which two (40%) were tuberculosis (0.3%-whole cohort), two (40%) were nocardiosis (0.3%-whole cohort), and one (20%) was disseminated atypical mycobacteria infection (0.15%-whole cohort). Time to post-transplantation infection was 11 (9–34) months. Seven (10%) recipients had more than one post-transplantation OI episode.

The comparison between OI and control groups is shown in Tables 1 and 2. Donors were significantly older in OI group than in control group (*p* = 0.02), with a similar statistical trend in recipients (*p* = 0.056). At the time of transplantation, blood lymphocytes count was significantly lower in OI group (*p* = 0.04). Numbers and percentages of CD4 and CD8 T-cells were similar in both groups; the same was found for the immunosuppressive treatments after transplantation (induction and maintenance).

The estimated GFR in OIs group was significantly lower than in control group at any given time (i.e., at 12-months or last available follow-up data). Acute rejection incidence and CMV viremia were similar in both groups. At the end of follow-up, event rates, allograft loss, and time to death after transplantation were similar in both groups.

In time-to-event analysis, the univariate risk factors for OIs after kidney transplantation (Table 3) were older recipient age (HR 1.02 (1–1.04), *p* = 0.03), older donor age (1.02 (1.01–1.04), *p* = 0.02), and ECD (2.76 (1.68–4.54), *p* < 0.0001). Higher CD4+ T-cells during follow-up and higher blood lymphocyte count at the time of transplantation were protective factors against OI (0.31 (0.11–0.83) and 0.61 (0.40–0.95), respectively). At the time of transplant, blood lymphocytes count was significantly lower in patients with OI (Table 1 (OI) Median 1.2 (IQR 0.9–1.6) vs. (Controls) 1.3 (1.0–1.8); *p* = 0.04) while CD4/CD8 numbers (%) were similar in both groups (Table 1) or using time-to-event analysis (Table 3). Induction and maintenance immunosuppressive regimens, acute rejection episode, and CMV viremia were not OI risk factors.

Independent risk factors for OI according to multivariate analysis were ECD (2.53 (1.48–4.31), *p* = 0.0007), and BK viremia (6.38 (3.62–11.23), *p* < 0.0001). High blood lymphocyte count at the time of transplantation was an independent protective risk factor (0.60 (0.38–0.94), *p* = 0.026). The multivariable analysis conducted only on patients with available pre-transplantation CD4 T-cell counts (*n* = 456) showed that ECD (2.92 (1.62–5.27), *p* = 0.0004) and BK viremia (5.11 (2.72–9.57), *p* < 0.0001) were independent risk factors for OI. In contrast, a higher CD4 T-cell percentage during follow-up (time-varying variable) (0.98 (0.96–0.99), *p* = 0.015) and, to a lesser extent, a higher lymphocyte count at the time of transplantation (0.68 (0.44–1.07), *p* = 0.09) were independent protective factors.

*J. Clin. Med.* **2019**, *8*, 594

#### *3.3. Patients and Allograft Survival*

In OI group, patient survival was significantly lower than in control group (Figure 2a, *p* = 0.009). After OI episode, 10 patients (14%) died, of whom three (30%) deaths were related to an OI episode (one PML, one PCP, and one IA). Other causes of death included cardio-vascular disease (*n* = 3), hemorrhagic shock (*n* = 1), traumatism (*n* = 1), bacterial infections (*n* = 1), and neoplasia (*n* = 1). OI was not an independent risk factor for death as shown by the multivariable analysis (Table 4). OI lost its statistical significance after multivariable adjustment for recipient age at transplantation, TCD8 cells (/1000) during follow-up, neutrophils (/1000) during follow-up, HCV+ status, former kidney transplantation and diabetes. Consequently, and in accordance with our statistical analysis strategy (section #2), OI was left out of Table 4 showing only results from the final multivariable model after a stepwise backward approach was applied.

**Figure 2.** Patient, allograft, and event-free survival in both groups (Kaplan–Meier survival analysis): (**a**) in OI group, patient survival was significantly lower than in control group (*p* = 0.009); (**b**) allograft survival was significantly lower in OI group (*p* = 0.0002); and (**c**) allograft survival without BK virus nephropathy was not significantly lower in OI group (*p* = 0.87).

**Table 4.** Patient and allograft survival independent risk factors (time varying Cox model).

Allograft survival was significantly lower in OI group (Figure 2b, *p* = 0.0002). After OI episode, allograft loss occurred in 13 (18%) patients, around 31 (5–63) months after transplantation. Causes of allograft loss were five (38%) BKVN, five (38%) chronic allograft dysfunction, two (16%) refractory acute rejection, and one (8%) unknown cause. OI episode was an independent risk factor for allograft loss with HR = 2.53 (1.29–4.95) (*p* = 0.007) (Table 4).

#### *3.4. Analysis Excluding BKVN*

As BKVN is well-known to cause a chronic destructive infection [24], we performed another analysis excluding BKVN events (Table 3). ECD and low blood lymphocytes count at the time of transplantation remained the two independent risk factors for OI episode (4.09 (2.06–8.09), *p* < 0.0001 and 0.64 (0.38–1.06), *p* = 0.08, respectively). OI was not found to be a risk factor for allograft loss (*p* = 0.87; Figure 2c and Table S1).

#### **4. Discussion**

We present here the results of a monocentric cohort analysis conducted on more than 500 kidney allograft recipients. We showed that, in the era of modern immunosuppression and the wide use of infectious disease prophylactic strategies, OIs occurred more than one year after transplantation and that pre-transplantation lymphopenia was an independent risk factor for OI episode, which was not the case for induction therapy. Moreover, OIs were an independent risk factor for allograft loss but had no effect on patient survival.

Although OIs are well defined in the setting of HIV [23], no classification of post-SOT OIs is currently available [2]. However, we tried in our work to carefully apply the current OI definitions on post-SOT settings taking into account the standardized immunosuppressive regimen and the type of SOT. On this point, former studies on allograft recipients were quite heterogenous concerning the infections considered and the type of SOT [3,4,14]. To our knowledge, no study evaluating the risk factors for OIs versus more severe common infections in engrafted patients has been published. Therefore, no conclusion regarding physiopathology and risk factors is available. In our cohort, we used HIV classification to define OI updated with BKVN, an immunosuppression-induced infection after kidney transplantation [23,24]. This selection process allowed us to provide reliable data on incidence and spectrum of OI after kidney transplantation and could be routinely used by clinicians to customize the prevention strategies to the patient condition.

OI proportion in our cohort was significantly lower than the most recently published incidence rate of around 25% [3]. Several explanations may account for this low incidence. First, the post-transplantation CMV, PCP, and bacterial prophylaxis strategies we use in our center are in fulfilment with the international recommendations (e.g., trimethoprim-sulfamethoxazole for Nocardia) [5,21]. Secondly, the immunocompromised recipients were exposed to a lower level of CNI, a strategy previously described to significantly decrease OI incidence [26]. At last, solid-organs failure before transplantation induced variable degrees of immune suppression. For instance, liver cirrhosis is associated with dysfunction of the defensive mechanisms against infections and higher incidence of sepsis [27] unlike end-stage renal failure [28]. Therefore, fungal infections risk is lower after kidney transplantation compared with other SOT populations [29].

Thus, we updated the description of post- kidney transplantation OIs to align it with the new strategies of immunosuppressive therapy. In our cohort, the incidence of CMV disease was significantly lower than previously described, probably because of the application of the regularly-updated prevention recommendations [2]. However, viral infections remained the first cause of OIs, mainly cutaneous shingles and BKVN. No prevention strategy is currently recommended for shingles. BKVN is clearly problematic after kidney transplantation since it thrives in immune suppression status, has a great impact on kidney allograft survival, and there is no curative treatment for it [24]. IA incidence is also lower in our cohort [29], whereas other OIs incidence was in the previously described range [16] after kidney transplantation.

Interestingly, time to OI onset was long, more than one year after transplantation. The latest review has reported a peak of OI at 6–12 months after transplantation [2]. Again, prevention strategies could probably postpone post-transplantation infections onset. However, post-transplantation fungal infection developed significantly earlier as in former studies, which confirmed that those infections flourish by the peak of immunosuppression [29]. No prevention strategy is currently recommended for those infections as well as PCP.

Thereafter, we aimed to identify independent risk factors for post-kidney transplantation OI. We found that ECD and low pre-transplantation lymphocyte count were independent risk factors; the type of induction immunosuppressive treatments and the recipient age were not. In kidney allograft recipients, older donor age, irrespective of recipient age, increases the rate of acute allograft rejection and infections [30,31]. The underlying immune system seems to be more important than immunosuppressive therapy. Aged transplanted mice could have an impaired anti-infectious response with accumulation of memory CD4+ T-cells and reduced Th1 anti-donor immune response [32,33]. These immunological effects could significantly decrease anti-infectious response in recipients transplanted from ECD. High CD4+ T-cells count was significantly a protective factor, but there was no effect of CD8+ T-cells count while CD4/ CD8 numbers (%) at the time of transplant were similar in both groups. The total count in lymphocytes had a superior predictive value for OI than the separate levels of CD4/CD8. However, the study population for analyses on CD4/CD8 was slightly decreased due to missing information on these variables, thus possibly resulting in a moderate loss of statistical power. High late stage differentiated CD28+CD57+CD4+ T-cells rates at the time of transplantation is independently associated with a decreased risk of OI [28]. Analysis of naive CD4+ T-cells remains to be determined since such phenotype has been associated with a high risk of infection in patients with common variable immunodeficiency [34]. Surprisingly, immunosuppressive induction using depletive monoclonal agents was not associated with OI incidence. Comparing the risk of infection with depletive and non-depletive therapies yielded controversial data although the most recent work shows that thymoglobulin was not associated with higher infection risk [35–37]. Almost all of our patients were treated with induction therapy. No induction therapy in immunocompromised kidney allograft recipients could be an option [38]. Whether the absence of induction could be associated with a significantly lower incidence of OI need to be elucidated.

How lymphopenia before transplant could influence OI occurring more than one year after transplantation remains unknown. Again, the wide use of prophylaxis (trimethoprim-sulfamethoxazole and valganciclovir) prevents early infection (mostly PCP, Nocardia, and CMV disease). Considering late infection, we believed that lymphopenia before transplantation could be a cumulative effect of immunosuppressive therapies in older patients.