Smith, Byron H.

ABSTRACT Background Ureteral stents are commonly placed during kidney transplantation (KT) to mitigate urologic complications. The objective of this study was to compare clinical outcomes in KT recipients with either early stent removal when attached to the Foley catheter or late removal via cystoscopy when unattached. Methods We conducted a multi‐center retrospective analysis of consecutive living donor KT patients (8/18/21‐4/21/23) stratified into Foley‐attached and unattached stent groups. Outcomes assessed were ureteral stricture, urine leaks, UTIs at 90 days and allograft failure and BKV DNAemia (BK virus DNAemia) at 1 year. Data are reported as mean and standard deviation. Results Among 247 patients with attached and 260 with unattached stents, baseline demographics (age, sex, diabetes) were similar between groups. There was a difference in race (87.9% vs. 77.8% Caucasian; p = 0.004). Foley catheters remained longer in the attached group (3.7 ± 1.8 vs. 3.0 ± 2.0 days; p < 0.001), but stents were removed earlier (3.7 ± 1.8 vs. 21.9 ± 4.7 days; p < 0.001). Comparing attached versus unattached, there were no differences in ureteral stricture (0.4% vs. 1.5%; p = 0.197), urine leak (0.8% vs. 0.4%; p = 0.533), BKV DNAemia (17.7% vs. 16.9%; p = 0.616), or allograft failure (0% vs. 0.8%; p = 0.167). However, UTI incidence by 90 days was lower in the attached group (5.7% vs. 11.9% p = 0.013). Induction therapy differed significantly between groups, with Alemtuzumab more common in the unattached group (58.1% vs. 19.4%, p < 0.001), Basiliximab more common in the attached group (43.3% vs. 35.0%, p < 0.001), and Thymoglobulin more common in the attached group (36.0% vs. 6.9%, p < 0.001). Baseline steroid use was higher in the unattached group (81.9% vs. 57.9%, p < 0.001). Conclusion Early stent removal via connection to the Foley catheter had similarly low rates of ureteral complications while reducing urinary tract infections and avoiding cystoscopic removal.

Artificial neural networks are now used across many fields, from medicine to chatbots. However, they are often complicated and give “black-box” predictions. To better understand these models, we outline some of the terminology, provide example applications in solid organ transplantation, and offer recommendations for those interested in starting to use such models. Examples and definitions are sourced from PubMed searches as well as a review of several applied texts in machine and deep learning. Neural networks have been applied in transplantation since the 1990s, but more recent applications involve their use to process nontabular data, such as images and text. Although interpretations of model results should take context into account, the power of these models holds great potential for the future in transplantation.

Objective: To develop and validate a risk score for predicting post-transplant recurrence in perihilar cholangiocarcinoma (pCCA) patients. Background: pCCA is an aggressive malignancy with a poor prognosis. Although neoadjuvant chemoradiation (CRT) followed by liver transplantation (LT) offers a potential cure for patients with unresectable, early-stage de novo or primary sclerosing cholangitis (PSC)-associated pCCA, post-transplant recurrence negatively impacts survival. Currently, no risk stratification tools exist. Methods: This retrospective study included consecutive patients with unresectable de novo or PSC-associated pCCA who underwent neoadjuvant CRT followed by LT at Mayo Clinic (1993–2024). Predictors of post-transplant recurrence were identified using multivariable Cox regression with LASSO variable selection. The Perihilar Cholangiocarcinoma Risk Estimation of Tumor Recurrence after Transplant (PRETREAT) risk score was created by assigning points from model coefficients. Score performance was evaluated by discrimination (c-statistic), internally validated with 10-fold cross-validation, and externally validated in a separate cohort. Survival outcomes were compared across 3 PRETREAT risk groups and between 2 time periods. Results: This study included 399 patients (development: 301; validation: 98). The 5-year cumulative incidence of post-transplant recurrence was 29.7% in the development cohort and 27.6% in the validation cohort. Multivariable analysis identified 4 independent predictors: macroscopic residual tumor on explant [hazard ratio (HR): 12.4], vascular encasement (HR: 2.18), lymphovascular invasion (HR: 2.04), and radial tumor diameter (HR: 1.02/mm). The PRETREAT score (0–22), based on these factors, demonstrated excellent performance in both internal and external validation (C-index 0.83 and 0.85, respectively). Patients were stratified into low-risk (0–7), moderate-risk (8–15), and high-risk (16–22) groups. The 5-year recurrence-free survival rates significantly differed across low-risk, moderate-risk, and high-risk groups (89.0%, 38.3%, and 15.4%, respectively; P<0.001). Over the last decade, overall survival improved significantly only in moderate-risk patients (70.4% vs 46.9%, P=0.024), without significant improvement in recurrence-free survival. Conclusions: The PRETREAT score provides a valuable tool for guiding post-transplant management of pCCA, enabling risk-stratified surveillance, and laying the foundation for future clinical trials.

Background and Aims: Among those with primary sclerosing cholangitis (PSC), perihilar cholangiocarcinoma (pCCA) is often diagnosed at a late stage and is a leading source of mortality. Detection of pCCA in PSC when curative action can be taken is challenging. Our aim was to create a deep learning model that analyzed MRI to detect early-stage pCCA and compare its diagnostic performance with expert radiologists. Approach and Results: We conducted a multicenter, international, retrospective cohort study involving adults with large duct PSC who underwent contrast-enhanced MRI. Senior abdominal radiologists reviewed the images. All patients with pCCA had early-stage cancer and were registered for liver transplantation. We trained a 3D DenseNet-121 model, a form of deep learning, using MRI images and assessed its performance in a separate test cohort. The study included 398 patients (training cohort n=150; test cohort n=248). pCCA was present in 230 individuals (training cohort n=64; test cohort n=166). In the test cohort, the respective performances of the model compared to the radiologists were: sensitivity 87.9% versus 50.0%, p <0.001; specificity 84.1% versus 100.0%, p <0.001; area under receiving operating curve 86.0% versus 75.0%, p <0.001. Even when a mass was absent, the model had a higher sensitivity for pCCA than radiologists (91.6% vs. 50.6%, p <0.001) and maintained good specificity (84.1%). Conclusions: The 3D DenseNet-121 MRI model effectively detects early-stage pCCA in PSC patients. Compared to expert radiologists, the model missed fewer cases of cancer.

ABSTRACTIntroductionThe incidence of mortality late in the pandemic, particularly after widespread vaccine availability, is not well understood. Herein, we elucidate the effect of this impact of the COVID pandemic as well as risk factors for mortality during it.MethodsThe primary end point was death with a functioning graft with secondary endpoints of mortality rates in subgroups and at different time intervals during the pandemic.ResultsDespite widespread vaccination, mortality rates for kidney transplant (KTx) recipients almost doubled during the COVID‐19 era (6.40 deaths per 100 person years vs. 3.54 pre‐COVID). Mortality increased in all racial/ethnic groups but increased more in Native Americans, Hispanics, and African Americans compared to non‐Hispanic Caucasians. The highest mortality rate occurred during the Delta and Omicron time frames. In contrast to the general population, mortality was more evenly spread across age groups in KTx recipients.ConclusionsMortality rates during the COVID‐19 era were extremely high, more than doubling in some racial/ethnic groups. We conclude that the KTx population was a particularly vulnerable group during the COVID‐19 era and suggests the need for further research into the management of variants in the future.

ABSTRACTCellular senescence is a biological mechanism of aging and age‐related diseases. The aim of this study was to examine whether senescence biomarkers are associated with frailty and physical function trajectories in patients undergoing kidney transplantation (KT). We also discussed the relationship between senescence biomarkers and KT function. In this multicenter study, we prospectively assessed plasma levels of senescence biomarkers, frailty as measured by the Physical Frailty Phenotype, and physical function as measured by the Short Physical Performance Battery prior to KT. Frailty, physical function, and KT function were also measured 1 year after KT. Variable associations were assessed using Cox and relaxed least absolute shrinkage and selection operation regression. The cohort consisted of 197 participants (mean age 53 ± 13 years, 61.4% male, and 80.2% White race). Higher pre‐KT levels of macrophage‐derived chemokine (MDC/CCL22) and growth differentiation factor‐15 (GDF‐15) were independently associated with less improvement in frailty and/or physical function during the first year after KT. Higher pre‐KT levels tumor necrosis factor receptor superfamily member 6 (FAS) and MMP‐9 levels were independently associated with lower KT function one year after KT. Pre‐KT cellular senescence may contribute to frailty, physical function, and kidney function trajectories during the first year after KT.

Background. The impact of induction type or high-risk viral discordance on older kidney transplant recipients is unclear. Herein, we analyzed the association between induction type, viral discordance, and outcomes for older recipients. Methods. We analyzed the Scientific Registry of Transplant Recipients standard analysis file for all primary kidney transplant recipients older than 55 y who were transplanted between 2005 and 2022. All transplants were crossmatch negative and ABO-compatible. Recipients were discharged on tacrolimus and mycophenolate ± steroids. Recipients were categorized into 3 groups by induction received: rabbit antithymocyte globulin (r-ATG; N = 51 079), interleukin-2 receptor antagonist (IL-2RA; N = 22 752), and alemtuzumab (N = 13 465). Kaplan-Meier curves were generated for recipient and graft survival, and follow-up was censored at 10 y. Mixed-effect Cox proportional hazard models examined the association between induction type, high-risk viral discordance, and outcomes of interest. Models were adjusted for pertinent recipient and donor characteristics. Results. Induction type did not predict recipient survival in the multivariable model, whereas Epstein-Barr virus high-risk discordance predicted 14% higher mortality (1.14 [1.07-1.21], P < 0.01). In the multivariable model for death-censored graft survival, alemtuzumab, but not IL-2RA, was associated with an increased risk of graft loss (1.18 [1.06-1.29], P < 0.01) compared with r-ATG. High-risk cytomegalovirus discordance predicted 10% lower death-censored graft survival (1.10 [1.01-1.19], P < 0.02). Live donor and preemptive transplantation were favorable predictors of survival. Conclusions. In this large cohort of older transplant recipients, alemtuzumab, but not IL-2RA, induction was associated with an increased risk of graft loss compared with r-ATG. Cytomegalovirus and Epstein-Barr virus high-risk viral discordance portended poor graft and recipient survival, respectively.