Increased Pretransplant Inflammatory Biomarkers Predict Death With Function After Kidney Transplantation.

BACKGROUND: Chronic systemic inflammation is associated with mortality in patients with chronic kidney disease, cardiovascular disease, and diabetes. The goal of this study was to examine the relationship between pretransplant inflammatory biomarkers (growth differentiation factor-15 [GDF-15], interleukin-6 [IL-6], soluble tumor necrosis factor receptor-1, monokine induced by gamma interferon/chemokine [C-X-C motif] ligand 9 [MIG/CXCL9], monocyte chemoattractant protein-1, soluble FAS, tumor necrosis factor-α, interleukin-15, and interleukin-1ß) and death with function (DWF) after kidney transplantation (KT). METHODS: We retrospectively measured inflammatory biomarker levels in serum collected up to 1 y before KT (time from blood draw to KT was 130 ±â€…110 d) in recipients transplanted between January 2006 and December 2018. Kaplan-Meier estimation, Cox regression, and Gradient Boosting Machine modeling were used to examine the relationship between inflammatory biomarkers and DWF. RESULTS: Our cohort consisted of 1595 KT recipients, of whom 62.9% were male and 83.2% were non-Hispanic White. Over a mean follow-up of 7.4 ±â€…3.9 y, 21.2% of patients (n = 338) experienced DWF. Patients with the highest quartile levels of GDF-15 (>4766 pg/mL), IL-6 (>6.11 pg/mL), and MIG/CXCL9 (> 5835 pg/mL) had increased rates of DWF, and each predicted mortality independently of the others. When adjusted for clinical factors (age, diabetes, etc), the highest quartile levels of GDF-15 and IL-6 remained independently associated with DWF. Adding inflammatory markers to a clinical Cox model improved the C-statistic for DWF from 0.727 to 0.762 using a Gradient Boosting Machine modeling approach. CONCLUSIONS: These findings suggest that pre-KT serum concentrations of GDF-15, IL-6, and MIG/CXCL9 may help to risk stratify and manage patients undergoing KT and suggests that chronic inflammation may play a role in mortality in KT recipients.

Electrocardiography-based Artificial Intelligence Algorithms Aid in Prediction of Long-term Mortality After Kidney Transplantation.

BACKGROUND: Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT. METHODS: We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms' outputs based on a single preoperative ECG were correlated with patient mortality data. RESULTS: Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00-9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality (P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751). CONCLUSIONS: The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.

Highly Sensitized Candidates Remain at Risk for Microvascular Inflammation Even When Donor-specific Antibody Is Avoided: A Matched Cohort Study.

BACKGROUND: Microvascular inflammation (MVI) is a key feature of antibody-mediated rejection (AMR) among patients with HLA donor-specific antibody (DSA), but MVI at AMR thresholds (Banff glomerulitis [g] + peritubular capillaritis [ptc] score ≥ 2) without DSA has been increasingly recognized. We aimed to determine the incidence of MVI among highly sensitized kidney transplant recipients without DSA. METHODS: We performed a single-center, retrospective, matched cohort study comparing outcomes of kidney transplant recipients with cPRA ≥90% with preexisting DSA (n = 49), cPRA ≥90% without preexisting DSA (n = 47), and matched controls with cPRA = 0 without preexisting DSA (n = 49). Controls were matched by age, donor type, and transplant date. Indication and surveillance biopsies combined with annual de novo DSA screening were obtained. RESULTS: Kidney transplant recipients with a cPRA ≥90% and no evidence of preexisting or de novo DSA had a higher incidence of MVI (glomerulitis + peritubular capillaritis ≥ 2) than patients with cPRA = 0 [35% (17/49) versus 12% (6/49), P = 0.0003] over a median (interquartile range) follow-up of 5 (4-6) y posttransplant. Among this cPRA ≥90% group without DSA, MVI persisted in 54% of cases on follow-up biopsy (7/13), and 24% (4/13) of cases developed transplant glomerulopathy (Banff cg score > 0). CONCLUSIONS: Highly sensitized transplant recipients have a high incidence of persistent and progressive MVI, even without DSA. The mechanisms underlying these histologic features needs to be elucidated, but this information is important to consider when making decisions about transplantation among highly sensitized individuals.

A Machine Learning-Driven Virtual Biopsy System For Kidney Transplant Patients.

In kidney transplantation, day-zero biopsies are used to assess organ quality and discriminate between donor-inherited lesions and those acquired post-transplantation. However, many centers do not perform such biopsies since they are invasive, costly and may delay the transplant procedure. We aim to generate a non-invasive virtual biopsy system using routinely collected donor parameters. Using 14,032 day-zero kidney biopsies from 17 international centers, we develop a virtual biopsy system. 11 basic donor parameters are used to predict four Banff kidney lesions: arteriosclerosis, arteriolar hyalinosis, interstitial fibrosis and tubular atrophy, and the percentage of renal sclerotic glomeruli. Six machine learning models are aggregated into an ensemble model. The virtual biopsy system shows good performance in the internal and external validation sets. We confirm the generalizability of the system in various scenarios. This system could assist physicians in assessing organ quality, optimizing allograft allocation together with discriminating between donor derived and acquired lesions post-transplantation.

Exploring the single-cell immune landscape of kidney allograft inflammation using imaging mass cytometry.

Kidney allograft inflammation, mostly attributed to rejection and infection, is an important cause of graft injury and loss. Standard histopathological assessment of allograft inflammation provides limited insights into biological processes and the immune landscape. Here, using imaging mass cytometry with a panel of 28 validated biomarkers, we explored the single-cell landscape of kidney allograft inflammation in 32 kidney transplant biopsies and 247 high-dimensional histopathology images of various phenotypes of allograft inflammation (antibody-mediated rejection, T cell-mediated rejection, BK nephropathy, and chronic pyelonephritis). Using novel analytical tools, for cell segmentation, we segmented over 900 000 cells and developed a tissue-based classifier using over 3000 manually annotated kidney microstructures (glomeruli, tubules, interstitium, and arteries). Using PhenoGraph, we identified 11 immune and 9 nonimmune clusters and found a high prevalence of memory T cell and macrophage-enriched immune populations across phenotypes. Additionally, we trained a machine learning classifier to identify spatial biomarkers that could discriminate between the different allograft inflammatory phenotypes. Further validation of imaging mass cytometry in larger cohorts and with more biomarkers will likely help interrogate kidney allograft inflammation in more depth than has been possible to date.

Race-free estimated glomerular filtration rate equation in kidney transplant recipients: development and validation study.

OBJECTIVE: To compare the performance of a newly developed race-free kidney recipient specific glomerular filtration rate (GFR) equation with the three current main equations for measuring GFR in kidney transplant recipients. DESIGN: Development and validation study SETTING: 17 cohorts in Europe, the United States, and Australia (14 transplant centres, three clinical trials). PARTICIPANTS: 15 489 adults (3622 in development cohort (Necker, Saint Louis, and Toulouse hospitals, France), 11 867 in multiple external validation cohorts) who received kidney transplants between 1 January 2000 and 1 January 2021. MAIN OUTCOME MEASURE: The main outcome measure was GFR, measured according to local practice. Performance of the GFR equations was assessed using P30 (proportion of estimated GFR (eGFR) within 30% of measured GFR (mGFR)) and correct classification (agreement between eGFR and mGFR according to GFR stages). The race-free equation, based on creatinine level, age, and sex, was developed using additive and multiplicative linear regressions, and its performance was compared with the three current main GFR equations: Modification of Diet in Renal Disease (MDRD) equation, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2009 equation, and race-free CKD-EPI 2021 equation. RESULTS: The study included 15 489 participants, with 50 464 mGFR and eGFR values. The mean GFR was 53.18 mL/min/1.73m2 (SD 17.23) in the development cohort and 55.90 mL/min/1.73m2 (19.69) in the external validation cohorts. Among the current GFR equations, the race-free CKD-EPI 2021 equation showed the lowest performance compared with the MDRD and CKD-EPI 2009 equations. When race was included in the kidney recipient specific GFR equation, performance did not increase. The race-free kidney recipient specific GFR equation showed significantly improved performance compared with the race-free CKD-EPI 2021 equation and performed well in the external validation cohorts (P30 ranging from 73.0% to 91.3%). The race-free kidney recipient specific GFR equation performed well in several subpopulations of kidney transplant recipients stratified by race (P30 73.0-91.3%), sex (72.7-91.4%), age (70.3-92.0%), body mass index (64.5-100%), donor type (58.5-92.9%), donor age (68.3-94.3%), treatment (78.5-85.2%), creatinine level (72.8-91.3%), GFR measurement method (73.0-91.3%), and timing of GFR measurement post-transplant (72.9-95.5%). An online application was developed that estimates GFR based on recipient's creatinine level, age, and sex (https://transplant-prediction-system.shinyapps.io/eGFR_equation_KTX/). CONCLUSION: A new race-free kidney recipient specific GFR equation was developed and validated using multiple, large, international cohorts of kidney transplant recipients. The equation showed high accuracy and outperformed the race-free CKD-EPI 2021 equation that was developed in individuals with native kidneys. TRIAL REGISTRATION: ClinicalTrials.gov NCT05229939.

Early post-transplant recurrence of ANCA vasculitis while on belatacept maintenance immunosuppression.

Post-transplant recurrence of ANCA-associated vasculitis (AAV) is infrequent, with recurrence within weeks of transplantation being even rarer. We describe an unusual case of AAV recurrence within 2 weeks post-transplant. Our patient received a deceased donor kidney transplant (KDPI 60%) after 6 years on hemodialysis for end-stage renal disease from AAV. She was induced with thymoglobulin and steroids, and maintained on belatacept, mycophenolate and prednisone. Time-zero biopsy showed acute tubular injury. Due to persistent delayed graft function by post-operative day 14, she underwent repeat biopsy, which showed focal segmental necrotizing and crescentic glomerulonephritis, with positive MPO, PR3 and negative anti-glomerular basement membrane antibodies. As her findings were in keeping with recurrent AAV, she underwent induction with rituximab, prednisone and intravenous immunoglobulin, with repeat rituximab 14 days later because of increasing B-lymphocyte counts. Belatacept was replaced with tacrolimus due to concerns with autoimmunity. Fortunately, renal function began to recover 4 days after treatment. In addition to highlighting potential immunologic mechanisms in AAV and the use of rituximab in post-transplant recurrence, our case suggests that for systemic autoimmune disease, patients maintained on belatacept must be monitored closely for recurrence, particularly in the setting of delayed graft function.

Changes in Glomerular Volume, Sclerosis, and Ischemia at 5 Years after Kidney Transplantation: Incidence and Correlation with Late Graft Failure.

SIGNIFICANCE STATEMENT: Glomerular volume, ischemic glomeruli, and global glomerulosclerosis are not consistently assessed on kidney transplant biopsies. The authors evaluated morphometric measures of glomerular volume, the percentage of global glomerulosclerosis, and the percentage of ischemic glomeruli and assessed changes in these measures over time to determine whether such changes predict late allograft failure. All three features increased from transplant to five-year biopsy. Kidneys with smaller glomeruli at 5 years had more global glomerulosclerosis and a higher percentage of ischemic-appearing glomeruli. Smaller glomeruli and increasing percentages of global glomerulosclerosis and ischemic glomeruli at 5 years predicted allograft failure. Only increased percentage of ischemic glomeruli predicted allograft failure at 5 years independent of all Banff scores. Glomerular changes reflect pathologic processes that predicted allograft loss; measuring them quantitatively might enhance the current Banff system and provide biomarkers for intervention trials. BACKGROUND: Histology can provide insight into the biology of renal allograft loss. However, studies are lacking that use quantitative morphometry to simultaneously assess changes in mean glomerular volume and in the percentages of globally sclerosed glomeruli (GSG) and ischemic-appearing glomeruli in surveillance biopsies over time to determine whether such changes are correlated with late graft failure. METHODS: We used digital scans of surveillance biopsies (at implantation and at 1 and 5 years after transplantation) to morphometrically quantify glomerular volume and the percentages of GSG and ischemic-appearing glomeruli in a cohort of 835 kidney transplants. Cox proportional hazards models assessed the risk of allograft failure with these three glomerular features. RESULTS: From implantation to 5 years, mean glomerular volume increased by nearly 30% (from 2.8×10 6 to 3.6×10 6 µm 3 ), mean percentage of GSG increased from 3.2% to 13.2%, and mean percentage of ischemic-appearing glomeruli increased from 0.8% to 9.5%. Higher percentages of GSG and ischemic-appearing glomeruli at 5-year biopsy predicted allograft loss. The three glomerular features at 5-year biopsy were related; the percentage of GSG and the percentage of ischemic glomeruli were positively correlated, and both were inversely correlated to glomerular volume. At 5 years, only 5.3% of biopsies had ≥40% ischemic glomeruli, but 45% of these grafts failed (versus 11.6% for <40% ischemic glomeruli). Higher Banff scores were more common with increasing percentages of GSG and ischemia, but at 5 years, only the percentage of ischemic glomeruli added to predictive models adjusted for Banff scores. CONCLUSIONS: Glomerular changes reflect important pathologic processes that predict graft loss. Measuring glomerular changes quantitatively on surveillance biopsies, especially the proportion of ischemic-appearing glomeruli, may enhance the current Banff system and be a useful surrogate end point for clinical intervention trials. PODCAST: This article contains a podcast at.

Effect of Maintaining Immunosuppression After Kidney Allograft Failure on Mortality and Retransplantation.

Few studies have addressed immunosuppression management after allograft failure (AF). Immunosuppression withdrawal to minimize complications must be balanced with the risk of sensitization and potentially reduced retransplantation. We aimed to determine relationships between immunosuppression, death, sensitization, and retransplantation among patients with AF. Methods: We performed a single-center retrospective study of patients transplanted from October 2007 to May 2017 with AF. We collected data on demographics, immunosuppression, calculated panel reactive antibody (cPRA) levels, death, retransplantation, and dialysis. Cox regression models were used to evaluate factors associated with death and retransplantation. Results: From October 2007 to May 2017, 1354 solitary ABO-compatible transplants were performed, of which 97 failed. Ten percent of patients received a preemptive retransplant. Among those who returned to dialysis (n = 87), 35% died, 25% received another transplant, and 30% remained on dialysis. After AF, 46% of patients discontinued immunosuppression. The cPRA was unchanged if immunosuppression was maintained, but immunosuppression discontinuation was associated with increased cPRA from a median (interquartile range) of 18 (0-99) to 96 (88.5-100.0; P = 0.003). Age at failure (hazard ratio, 1.1; confidence interval, 1.0-1.1) and cardiovascular disease were associated with death (hazard ratio, 2.9; confidence interval, 1.2-7.0) in multivariate analysis. Importantly, immunosuppression maintenance was not associated with increased death or retransplantation despite the increase in cPRA that occurred when immunosuppression was discontinued. Conclusions: Kidney transplant recipients with AF have a high mortality rate after dialysis initiation. Although immunosuppression withdrawal was associated with increased cPRA, it was not associated with reduced retransplantation. Therefore, it is reasonable to discontinue immunosuppression after AF despite sensitization if retransplantation is delayed.

Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction.

Machine learning (ML) models have recently shown potential for predicting kidney allograft outcomes. However, their ability to outperform traditional approaches remains poorly investigated. Therefore, using large cohorts of kidney transplant recipients from 14 centers worldwide, we developed ML-based prediction models for kidney allograft survival and compared their prediction performances to those achieved by a validated Cox-Based Prognostication System (CBPS). In a French derivation cohort of 4000 patients, candidate determinants of allograft failure including donor, recipient and transplant-related parameters were used as predictors to develop tree-based models (RSF, RSF-ERT, CIF), Support Vector Machine models (LK-SVM, AK-SVM) and a gradient boosting model (XGBoost). Models were externally validated with cohorts of 2214 patients from Europe, 1537 from North America, and 671 from South America. Among these 8422 kidney transplant recipients, 1081 (12.84%) lost their grafts after a median post-transplant follow-up time of 6.25 years (Inter Quartile Range 4.33-8.73). At seven years post-risk evaluation, the ML models achieved a C-index of 0.788 (95% bootstrap percentile confidence interval 0.736-0.833), 0.779 (0.724-0.825), 0.786 (0.735-0.832), 0.527 (0.456-0.602), 0.704 (0.648-0.759) and 0.767 (0.711-0.815) for RSF, RSF-ERT, CIF, LK-SVM, AK-SVM and XGBoost respectively, compared with 0.808 (0.792-0.829) for the CBPS. In validation cohorts, ML models' discrimination performances were in a similar range of those of the CBPS. Calibrations of the ML models were similar or less accurate than those of the CBPS. Thus, when using a transparent methodological pipeline in validated international cohorts, ML models, despite overall good performances, do not outperform a traditional CBPS in predicting kidney allograft failure. Hence, our current study supports the continued use of traditional statistical approaches for kidney graft prognostication.

Guiding Kidney Transplantation Candidates for Effective Weight Loss: A Clinical Cohort Study.

Background: Obesity is increasingly common in kidney transplant candidates and may limit access to transplantation. Obesity and diabetes are associated with a high risk for post-transplant complications. The best approach to weight loss to facilitate active transplant listing is unknown, but bariatric surgery is rarely considered due to patient- and physician-related apprehension, among other factors. Methods: We aimed to determine the magnitude of weight loss, listing, and transplant rates in 28 candidates with a mean BMI of 44.4±4.6 kg/m2 and diabetes treated conservatively for 1 year post weight-loss consultations (group 1). Additionally, we evaluated 15 patients (group 2) who met the inclusion criteria but received bariatric intervention within the same time frame. All patients completed a multidisciplinary weight management consultation with at least 1 year of follow-up. Results: In the conservatively managed group (group 1), the mean weight at the time of initial consultation was 126.5±18.5 kg, and the mean BMI was 44.4±4.6 kg/m2. At 1 year post weight-loss consultation, the mean weight decreased by 4.4±8.2 kg to 122.9±17 kg, and the mean BMI was 43±4.8 kg/m2, with a total mean body weight decrease of 3% (P=0.01). Eighteen patients (64%) did not progress to become candidates for active listing/transplantation during the follow-up time of 4±2.9 years, with 15 (54%) subsequently developing renal failure/diabetes-related comorbidities prohibitive for transplantation. In contrast, mean total body weight decreased by 19% at 6 months post bariatric surgery, and the mean BMI was 34.2±4 and 32.5±3.7 kg/m2 at 6 and 12 months, respectively. Bariatric surgery was strongly associated with subsequent kidney transplantation (HR=8.39 [95% CI 1.71 to 41.19]; P=0.009). Conclusions: A conservative weight-loss approach involving multidisciplinary consultation was ineffective in most kidney transplant candidates with diabetes, suggesting that a more proactive approach is needed.

Development and Validation of a Kidney-Transplant Specific Measure of Treatment Burden.

BACKGROUND: Treatment burden refers to the work involved in managing one's health and its impact on well-being and has been associated with nonadherence in patients with chronic illnesses. No kidney transplant (KT)-specific measure of treatment burden exists. The aim of this study was to develop a KT-specific supplement to the Patient Experience with Treatment and Self-Management (PETS), a general measure of treatment burden. METHODS: After drafting and pretesting KT-specific survey items, we conducted a cross-sectional survey study involving KT recipients from Mayo Clinic in Minnesota, Arizona, and Florida. Exploratory factor analysis (EFA) was used to identify domains for scaling the KT-specific supplement. Construct and known-groups validity were determined. RESULTS: Survey respondents (n = 167) had a mean age of 61 years (range 22-86) and received a KT on average 4.0 years ago. Three KT-specific scales were identified (transplant function, self-management, adverse effects). Higher scores on the KT-specific scales were correlated with higher PETS treatment burden, worse physical and mental health, and lower self-efficacy (p < 0.0001). Patients taking more medications reported higher transplant self-management burden. CONCLUSIONS: We developed a KT-specific supplement to the PETS general measure of treatment burden. Scores may help providers identify recipients at risk for nonadherence.

Kidney Transplant Outcomes of Patients With Multiple Myeloma.

Introduction: Data on kidney transplantation (KTx) outcomes of patients with multiple myeloma (MM) are very limited. Methods: We investigated the outcomes of patients with MM who underwent KTx between 1994 and 2019. Results: A total of 12 transplants from 11 patients were included. At the time of KTx, 6 were classified as having stringent complete response (CR), 2 as CR, 2 as very good partial response (VGPR), and 2 as partial response (PR). With a median follow-up of 40 (minimum-maximum, 5-92) months after KTx, hematologic progression occurred in 9 transplants (75%). There were 3 grafts (25%) that failed, and 5 patients (45.5%) experienced death with functioning allografts. Graft survival at 1 and 5 years was 82.5% and 66%, respectively. Progression-free survival (PFS) rates of the cohort at 1, 3, and 5 years were 83.3%, 55.6%, and 44.4%, respectively. The estimated median PFS of patients who received bortezomib at any time (pre-KTx and/or post-KTx) was not reached, whereas it was 24 months for those who never received bortezomib (P = 0.281). Overall survival (OS) rates of the cohort at 1, 3, and 5 years were 81.8%, 61.4%, and 61.4%, respectively. OS of patients who received bortezomib at any time was 87.5%, 72.9%, and 72.9%, and that for those who never received bortezomib was 66.7%, 33.3%, and 33.3% (P = 0.136). All deaths occurred owing to hematologic progression or treatment-related complications. Conclusion: Kidney transplant outcomes of patients with myeloma who received bortezomib before or after KTx seem to be more favorable. Nevertheless, relapse after KTx in MM is still common. More studies are needed to better determine who benefits from a KTx.

Death With Function and Graft Failure After Kidney Transplantation: Risk Factors at Baseline Suggest New Approaches to Management.

BACKGROUND: Improving both patient and graft survival after kidney transplantation are major unmet needs. The goal of this study was to assess risk factors for specific causes of graft loss to determine to what extent patients who develop either death with a functioning graft (DWFG) or graft failure (GF) have similar baseline risk factors for graft loss. METHODS: We retrospectively studied all solitary renal transplants performed between January 1, 2006, and December 31, 2018, at 3 centers and determined the specific causes of DWFG and GF. We examined outcomes in different subgroups using competing risk estimates and cause-specific Cox models. RESULTS: Of the 5752 kidney transplants, graft loss occurred in 21.6% (1244) patients, including 12.0% (691) DWFG and 9.6% (553) GF. DWFG was most commonly due to malignancy (20.0%), infection (19.7%), cardiac disease (12.6%) with risk factors of older age and pretransplant dialysis, and diabetes as the cause of renal failure. For GF, alloimmunity (38.7%), glomerular diseases (18.6%), and tubular injury (13.9%) were the major causes. Competing risk incidence models identified diabetes and older recipients with higher rates of both DWFG and nonalloimmune GF. CONCLUSIONS: These data suggest that at baseline, 2 distinct populations can be identified who are at high risk for renal allograft loss: a younger, nondiabetic patient group who develops GF due to alloimmunity and an older, more commonly diabetic population who develops DWFG and GF due to a mixture of causes-many nonalloimmune. Individualized management is needed to improve long-term renal allograft survival in the latter group.

Kidney Transplantation in Patients With Monoclonal Gammopathy of Renal Significance (MGRS)-Associated Lesions: A Case Series.

RATIONALE & OBJECTIVE: Data on kidney transplantation outcomes among patients with monoclonal gammopathy of renal significance (MGRS) are lacking. STUDY DESIGN: Case series of patients with MGRS, some of whom received clone-directed therapies before kidney transplantation. SETTING & PARTICIPANTS: 28 patients who underwent kidney transplantation from 1987 through 2016 after diagnosis with MGRS-associated lesions including light-chain deposition disease (LCDD), C3 glomerulopathy with monoclonal gammopathy (C3G-MG), and light-chain proximal tubulopathy (LCPT). FINDINGS: Of the 19 patients with LCDD, 10 were treated before kidney transplantation and 9 were treatment-naive. Among the treated patients with LCDD, 3 (30%) experienced histologic recurrence, 2 (20%) grafts failed, and 2 (20%) died during a median follow-up of 70 (range, 3-162) months after transplant. In the treatment-naive LCDD group, 8 (89%) had histologic recurrence, 6 (67%) grafts failed, and 4 (44%) patients died during a median follow-up of 60 (range, 35-117) months. Of the 5 patients who had a complete response before transplant, none died, and only 1 experienced graft failure, 162 months after transplant. Of 5 patients with C3G-MG, 3 were treatment-naive before transplant. Both patients who were treated before transplant had histologic recurrence, and 1 experienced graft failure and died. Among the 3 patients with treatment-naive C3G-MG, histologic recurrence occurred in all, and graft loss and death were observed in 2 and 1, respectively. In the LCPT group (n=4), histologic recurrence was observed in all 3 patients who did not receive clone-directed therapies before transplant, and 2 of these patients died, 1 with a functioning kidney. The 1 patient with LCPT who received therapy before transplant did not have histologic recurrence or graft loss and survived. LIMITATIONS: Small sample size, nonstandardized clinical management, retrospective design. CONCLUSIONS: Recurrence is very common in all MGRS-associated lesions after kidney transplant. Achieving a complete hematologic response may reduce the risks of recurrence, graft loss, and death. More studies are needed to determine the effects of hematologic response on outcomes for each MGRS-associated lesion.

Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study.

BACKGROUND: Kidney allograft failure is a common cause of end-stage renal disease. We aimed to develop a dynamic artificial intelligence approach to enhance risk stratification for kidney transplant recipients by generating continuously refined predictions of survival using updates of clinical data. METHODS: In this observational study, we used data from adult recipients of kidney transplants from 18 academic transplant centres in Europe, the USA, and South America, and a cohort of patients from six randomised controlled trials. The development cohort comprised patients from four centres in France, with all other patients included in external validation cohorts. To build deeply phenotyped cohorts of transplant recipients, the following data were collected in the development cohort: clinical, histological, immunological variables, and repeated measurements of estimated glomerular filtration rate (eGFR) and proteinuria (measured using the proteinuria to creatininuria ratio). To develop a dynamic prediction system based on these clinical assessments and repeated measurements, we used a Bayesian joint models-an artificial intelligence approach. The prediction performances of the model were assessed via discrimination, through calculation of the area under the receiver operator curve (AUC), and calibration. This study is registered with ClinicalTrials.gov, NCT04258891. FINDINGS: 13 608 patients were included (3774 in the development cohort and 9834 in the external validation cohorts) and contributed 89 328 patient-years of data, and 416 510 eGFR and proteinuria measurements. Bayesian joint models showed that recipient immunological profile, allograft interstitial fibrosis and tubular atrophy, allograft inflammation, and repeated measurements of eGFR and proteinuria were independent risk factors for allograft survival. The final model showed accurate calibration and very high discrimination in the development cohort (overall dynamic AUC 0·857 [95% CI 0·847-0·866]) with a persistent improvement in AUCs for each new repeated measurement (from 0·780 [0·768-0·794] to 0·926 [0·917-0·932]; p<0·0001). The predictive performance was confirmed in the external validation cohorts from Europe (overall AUC 0·845 [0·837-0·854]), the USA (overall AUC 0·820 [0·808-0·831]), South America (overall AUC 0·868 [0·856-0·880]), and the cohort of patients from randomised controlled trials (overall AUC 0·857 [0·840-0·875]). INTERPRETATION: Because of its dynamic design, this model can be continuously updated and holds value as a bedside tool that could refine the prognostic judgements of clinicians in everyday practice, hence enhancing precision medicine in the transplant setting. FUNDING: MSD Avenir, French National Institute for Health and Medical Research, and Bettencourt Schueller Foundation.

Identification of Genetic Causes of Focal Segmental Glomerulosclerosis Increases With Proper Patient Selection.

OBJECTIVE: To increase the likelihood of finding a causative genetic variant in patients with a focal segmental glomerulosclerosis (FSGS) lesion, clinical and histologic characteristics were analyzed. PATIENTS AND METHODS: Individuals 18 years and older with an FSGS lesion on kidney biopsy evaluated at Mayo Clinic from November 1, 1999, through October 31, 2019, were divided into 4 groups based on clinical and histologic characteristics: primary FSGS, secondary FSGS with known cause, secondary FSGS without known cause, and undetermined FSGS. A targeted gene panel and a customized gene panel retrieved from exome sequencing were performed. RESULTS: The overall rate of detection of a monogenic cause was 42.9% (21/49). Individuals with undetermined FSGS had the highest rate of positivity (87.5%; 7/8) followed by secondary FSGS without an identifiable cause (61.5%; 8/13) and secondary FSGS with known cause (33.3%; 5/15). Four of 5 (80%) individuals in the latter group who had positive genetic testing results also had a family history of kidney disease. Univariate analysis showed that family history of kidney disease (odds ratio [OR], 13.8; 95% CI, 3.7 to 62.4; P<.001), absence of nephrotic syndrome (OR, 8.2; 95% CI, 1.9 to 58.1; P=.004), and female sex (OR, 5.1; 95% CI, 1.5 to 19.9; P=.01) were strong predictors of finding a causative genetic variant in the entire cohort. The most common variants were in the collagen genes (52.4%; 11/21), followed by the podocyte genes (38.1%; 8/21). CONCLUSION: In adults with FSGS lesions, proper selection of patients increases the rate of positive genetic testing significantly. The majority of individuals with undetermined FSGS in whom the clinical presentation and histologic parameters are discordant had a genetic diagnosis.

Characterization of ABH-subtype donor-specific antibodies in ABO-A-incompatible kidney transplantation.

ABO-incompatible (ABOi) transplantation requires preemptive antibody reduction; however, the relationship between antibody-mediated rejection (AMR) and ABO-antibodies, quantified by hemagglutination (HA), is inconsistent, possibly reflecting variable graft resistance to AMR or HA assay limitations. Using an ABH-glycan microarray, we quantified ABO-A antigen-subtype (A-subtype)-specific IgM and IgG in 53 ABO-O recipients of ABO-A kidneys, before and after antibody removal (therapeutic plasma exchange [TPE] or ABO-A-trisaccharide immunoadsorption [IA]) and 1-year posttransplant. IgM binding to all A-subtypes correlated highly (R2  ≥ .90) and A-subtype antibody specificities was reduced equally by IA versus TPE. IgG binding to the A-subtypes (II-IV) expressed in kidney correlated poorly (.27 ≤ R2  ≤ .69). Reduction of IgG specific to A-subtype-II was equivalent for IA and TPE, whereas IgG specific to A-subtypes-III/IV was not as greatly reduced by IA (p < .005). One-year posttransplant, IgG specific to A-II remained the most reduced antibody. Immunostaining revealed only A-II on vascular endothelium but A-subtypes II-III/IV on tubular epithelium. These results show that ABO-A-trisaccharide is sufficient for IgM binding to all A-subtypes; this is true for IgG binding to A-II, but not subtypes-III/IV, which exhibits varying degrees of specificity. We identify A-II as the major, but importantly not the sole, antigen relevant to treatment and immune modulation in adult ABO-A-incompatible kidney transplantation.