Predictor Of Graft Loss Research Articles

Value of Factor V in the diagnosis of early graft dysfunction after liver transplantation: Internal validation.

Primary graft dysfunction is a major early complication following liver transplantation, potentially leading to retransplantation or patient death. Coagulation Factor V (FV) and ALT have emerged as important biomarkers in assessing liver function, yet their role as early predictors of graft loss has not been fully validated. The aim of this study is to conduct an internal validation of published results on the applicability of FV and ALT for diagnosing graft dysfunction and its predictive ability for graft loss within the first 90 days. We conducted a retrospective cohort study including 513 adult recipients from 2012 to 2023 at the Regional University Hospital of Málaga. FV and ALT levels were measured on postoperative day 2, and patients were categorized based on FV <37.5 and ALT >1539. The association with 90-day graft loss was analyzed. Graft loss occurred in 43 patients (8.4%) within the first 90 days. The combination of FV <37.5 and ALT >1539 on postoperative day 2 demonstrated a specificity of 99% and a test efficiency of 94% in predicting graft loss. Patients meeting both criteria had a 74-fold increased risk of graft loss, with most losses occurring within the first week, and a median survival of 4 days. These findings suggest that FV and ALT on postoperative day 2 are reliable early markers for predicting graft loss, enabling risk stratification and guiding critical decisions regarding early retransplantation in the immediate postoperative period.

Clinical outcomes after a biopsy diagnosis of antibody-mediated rejection in pediatric heart transplant recipients

BackgroundExtending graft survival after heart transplant (HT) is of paramount importance to achieve survival well into adulthood for childhood recipients. Acute rejection is a significant adverse event, and biopsy remains the most specific means for diagnosing acute cellular rejection (ACR) versus antibody-mediated rejection (AMR). MethodsAll children in the Pediatric Heart Transplant Society (PHTS) Registry who underwent HT between 1/2015 and 6/2022 and had ≥1 episode of treated rejection were included. Survival after rejection was compared between those with AMR and those with ACR-only. Secondary outcomes of infection, malignancy, and cardiac allograft vasculopathy (CAV) were assessed. Risk factors for graft loss after AMR were identified using Cox proportional hazard modeling. ResultsAmong 906 children treated for rejection during follow-up through 12/2022, 697 (77%) with complete biopsy information were included. AMR was present on biopsy in 261 (37%) patients; ACR-only was present in 436 (63%) patients. Time to treated rejection was earlier in those with AMR, median time from HT to rejection 0.11 versus 0.29 years, p=0.0006. When rejection occurred within the 1st year, survival after AMR was lower than survival after ACR-only. Predictors of graft loss after AMR were younger age at HT, diagnosis of congenital heart disease, and rejection with hemodynamic compromise. There was no difference in time to CAV, infection, or malignancy after treated rejection between groups. ConclusionsThe largest analysis of pediatric HT recipients treated for rejection with biopsy data to identify AMR underscores the continued importance of AMR on survival. AMR is associated with higher graft loss versus ACR when occurring in the first-year post HT. Predictors of graft loss after AMR identify patients who may benefit from increased surveillance, more aggressive rejection treatment, or augmented maintenance immunosuppression.

Prognostic scores for ursodeoxycholic acid-treated patients predict graft loss and mortality in recurrent primary biliary cholangitis after liver transplantation

Recurrent primary biliary cholangitis (rPBC) develops in approximately 30% of patients and negatively impacts graft and overall patient survival after liver transplantation (LT). There is a lack of data regarding the response rate to ursodeoxycholic acid (UDCA) in rPBC. We evaluated a large, international, multi-center cohort to assess the performance of PBC scores in predicting the risk of graft and overall survival after LT in patients with rPBC. A total of 332 patients with rPBC after LT were evaluated from 28 centers across Europe, North and South America. The median age at the time of rPBC was 58.0 years [IQR 53.2-62.6], and 298 patients (90%) were female. The biochemical response was measured with serum levels of alkaline phosphatase (ALP) and bilirubin, and Paris-2, GLOBE and UK-PBC scores at 1 year after UDCA initiation. During a median follow-up of 8.7 years [IQR 4.3-12.9] after rPBC diagnosis, 52 patients (16%) had graft loss and 103 (31%) died. After 1 year of UDCA initiation the histological stage at rPBC (hazard ratio [HR] 3.97, 95% CI 1.36-11.55, p= 0.01), use of prednisone (HR 3.18, 95% CI 1.04-9.73, p= 0.04), ALP xULN (HR 1.59, 95% CI 1.26-2.01, p <0.001), Paris-2 criteria (HR 4.14, 95% CI 1.57-10.92, p= 0.004), GLOBE score (HR 2.82, 95% CI 1.71-4.66, p <0.001), and the UK-PBC score (HR 1.06, 95% CI 1.03-1.09, p <0.001) were associated with graft survival in the multivariate analysis. Similar results were observed for overall survival. Patients with rPBC and disease activity, as indicated by standard PBC risk scores, have impaired outcomes, supporting efforts to treat recurrent disease in similar ways to pre-transplant PBC. One in three people who undergo liver transplantation for primary biliary cholangitis develop recurrent disease in their new liver. Patients with recurrent primary biliary cholangitis and incomplete response to ursodeoxycholic acid, according to conventional prognostic scores, have worse clinical outcomes, with higher risk of graft loss and mortality in similar ways to the disease before liver transplantation. Our results supportsupport efforts to treat recurrent disease in similar ways to pre-transplant primary biliary cholangitis.

Comparing the prognostic performance of iBOX and biopsy-proven acute rejection for long-term kidney graft survival

Biopsy-proven acute rejection (BPAR) occurs in approximately 10% of kidney transplant recipients in the first year, making superiority trials unfeasible. iBOX, a quantitative composite of estimated glomerular filtration rate, proteinuria, antihuman leukocyte antigen donor-specific antibody, and + full/− abbreviated kidney histopathology, is a new proposed surrogate endpoint. BPAR’s prognostic ability was compared with iBOX in a pooled cohort of 1534 kidney transplant recipients from 4 data sets, including 2 prospective randomized controlled trials. Discrimination analyses showed mean c-statistic differences between both iBOX compared with BPAR of 0.25 (95% confidence interval: 0.17-0.32) for full iBOX and 0.24 (95% confidence interval: 0.16-0.32) for abbreviated iBOX, indicating statistically significantly higher c-statistic values for the iBOX prognosis of death-censored graft survival. Mean (± standard error) c-statistics were 0.81 ± 0.03 for full iBOX, 0.80 ± 0.03 for abbreviated iBOX, and 0.57 ± 0.03 for BPAR. In calibration analyses, predicted graft loss events from both iBOX models were not significantly different from those observed. However, for BPAR, the predicted events were significantly (P < .01) different (observed: 64; predicted: 70; full iBOX: 76; abbreviated iBOX: 173 BPAR). IBOX at 1-year posttransplant is superior to BPAR in the first year posttransplant in graft loss prognostic performance, providing valuable additional information and facilitating the demonstration of superiority of novel immunosuppressive regimens.

(118) - Developing a Molecular Classifier to Predict Graft Loss in Lung Transplant Mucosal Biopsies

(118) - Developing a Molecular Classifier to Predict Graft Loss in Lung Transplant Mucosal Biopsies

Protocol liver biopsy predicts graft survival after liver transplantation.

The use of protocol liver biopsy to monitor liver allograft status remains controversial. There is limited data from modern transplantation populations that includes protocol biopsies to evaluate its value in predicting clinical outcomes. All protocol liver biopsies were identified from 875 patients who underwent liver transplantation at Helsinki University Hospital between 2000 and 2019. Each histologic component was analyzed for its ability to predict long-term outcomes, especially graft survival. We determined the frequency of significant biopsy findings based on the Banff working group definition. Liver function tests (LFTs) and clinical markers were evaluated for their ability to predict significant biopsy findings. In total, 867 protocol liver biopsies were analyzed. Significant findings were identified in 20.1% of the biopsies. In the first protocol biopsy, steatohepatitis (hazard ratio [HR] 3.504, p=.03) and moderate or severe congestion (HR 3.338, p=.04) predicted graft loss. The presence of cholangitis (HR 2.563, p=.04), necrosis (HR 7.635, p<.001), mild congestion (HR 4.291, p=.009), and significant biopsy finding (HR 2.540, p=.02) predicted inferior death-censored graft survival. While the degree of elevation of LFTs was positively associated with significant biopsy findings, the discrimination was poor (AUC.572-.622). Combined LFTs and clinical risk factors remained suboptimal for discriminating significant biopsy findings (AUC.696). Our findings support the use of protocol liver biopsies after liver transplantation since they frequently revealed changes associated with long-term outcomes, even when LFTs were normal.

Clinical performance of the iPREDICTLIVING tool for the prediction of the post-transplant recipient and living donor outcomes in a European cohort.

A living donor kidney transplant (LDKT) is the best treatment for ESRD. A prediction tool based on clinical and demographic data available pre-KT was developed in a Norwegian cohort with three different models to predict graft loss, recipient death, and donor candidate's risk of death, the iPREDICTLIVING tool. No external validations are yet available. We sought to evaluate its predictive performance in our cohort of 352 pairs LKDT submitted to KT from 1998 to 2019. The model for censored graft failure (CGF) showed the worse discriminative performance with Harrell's C of.665 and a time-dependent AUC of.566, with a calibration slope of.998. For recipient death, at 10 years, the model had a Harrell's C of.776, a time-dependent AUC of.773, and a calibration slope of 1.003. The models for donor death were reasonably discriminative, although with a poor calibration, particularly for 20 years of death, with a Harrell's C of.712 and AUC of.694 with a calibration slope of.955. These models have moderate discriminative and calibration performance in our population. The tool was validated in this Northern Portuguese cohort, Caucasian, with a low incidence of diabetes and other comorbidities. It can improve the informed decision-making process at the living donor consultation joining clinical and other relevant information.

Deep learning-enabled classification of kidney allograft rejection on whole slide histopathologic images.

Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning system for automated assessment of whole-slide images (WSIs) from kidney allograft biopsies to enable detection and subtyping of rejection and to predict the prognosis of rejection. We collected H&E-stained WSIs of kidney allograft biopsies at 400x magnification from January 2015 to September 2023 at two hospitals. These biopsy specimens were classified as T cell-mediated rejection, antibody-mediated rejection, and other lesions based on the consensus reached by two experienced transplant pathologists. To achieve feature extraction, feature aggregation, and global classification, we employed multi-instance learning and common convolution neural networks (CNNs). The performance of the developed models was evaluated using various metrics, including confusion matrix, receiver operating characteristic curves, the area under the curve (AUC), classification map, heat map, and pathologist-machine confrontations. In total, 906 WSIs from 302 kidney allograft biopsies were included for analysis. The model based on multi-instance learning enables detection and subtyping of rejection, named renal rejection artificial intelligence model (RRAIM), with the overall 3-category AUC of 0.798 in the independent test set, which is superior to that of three transplant pathologists under nearly routine assessment conditions. Moreover, the prognosis models accurately predicted graft loss within 1 year following rejection and treatment response for rejection, achieving AUC of 0.936 and 0.756, respectively. We first developed deep-learning models utilizing multi-instance learning for the detection and subtyping of rejection and prediction of rejection prognosis in kidney allograft biopsies. These models performed well and may be useful in assisting the pathological diagnosis.

Necroptosis-related genes allow novel insights into predicting graft loss and diagnosing delayed graft function in renal transplantation

Ischemia–reperfusion injury (IRI) is an inevitable pathophysiological phenomenon in kidney transplantation. Necroptosis is an undoubtedly important contributing mechanism in renal IRI. We first screened differentially expressed necroptosis-related genes (DENRGs) from public databases. Eight DENRGs were validated by independent datasets and verified by qRT-PCR in a rat IRI model. We used univariate and multivariate Cox regression analyses to establish a prognostic signature, and graft survival analysis was performed. Immune infiltrating landscape analysis and gene set enrichment analysis (GSEA) were performed to understand the underlying mechanisms of graft loss, which suggested that necroptosis may aggravate the immune response, resulting in graft loss. Subsequently, a delayed graft function (DGF) diagnostic signature was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) and exhibited robust efficacy in validation datasets. After comprehensively analyzing DENRGs during IRI, we successfully constructed a prognostic signature and DGF predictive signature, which may provide clinical insights for kidney transplant.

A large-scale retrospective study enabled deep-learning based pathological assessment of frozen procurement kidney biopsies to predict graft loss and guide organ utilization

Lesion scores on procurement donor biopsies are commonly used to guide organ utilization for deceased-donor kidneys. However, frozen sections present challenges for histological scoring, leading to inter- and intra-observer variability and inappropriate discard. Therefore, we constructed deep-learning based models to recognize kidney tissue compartments in hematoxylin &eosin-stained sections from procurement needle biopsies performed nationwide in years 2011-2020. To do this, we extracted whole-slide abnormality features from 2431 kidneys, and correlated with pathologists’ scores and transplant outcomes. A Kidney Donor Quality Score (KDQS) was derived and used in combination with recipient demographic and peri-transplant characteristics to predict graft loss or assist organ utilization. The performance on wedge biopsies was additionally evaluated. Our model identified 96% and 91% of normal/sclerotic glomeruli respectively; 94% of arteries/arterial intimal fibrosis; 90% of tubules. Whole-slide features of Sclerotic Glomeruli (GS)%, Arterial Intimal Fibrosis (AIF)%, and Interstitial Space Abnormality (ISA)% demonstrated strong correlations with corresponding pathologists’ scores of all 2431 kidneys, but had superior associations with post-transplant estimated glomerular filtration rates in 2033 and graft loss in 1560 kidneys. The combination of KDQS and other factors predicted one- and four-year graft loss in a discovery set of 520 kidneys and a validation set of 1040 kidneys. By using the composite KDQS of 398 discarded kidneys due to “biopsy findings”, we suggest that if transplanted, 110 discarded kidneys could have had similar survival to that of other transplanted kidneys. Thus, our composite KDQS and survival prediction models may facilitate risk stratification and organ utilization while potentially reducing unnecessary organ discard.

Utility of MEST and MEST-C Scoring in IgA Nephropathy in Kidney Transplantation: A Mini Review

IgAN is a major cause of end-stage kidney disease (ESKD) leading to kidney transplantation in a significant proportion of patients. However, its recurrence in transplanted kidneys can lead to graft loss. The rate of graft loss attributable to IgAN after transplantation is variably reported in different retrospective cohorts. Previous reports describe recurrence rates of 22-58% with a 1.3% to 16% rate of graft loss. Accurate diagnosis and prediction of graft loss are important for planning effective therapies to improve graft survival in IgAN post transplantation. The Oxford classification using MEST and MEST-C in native kidney disease IgAN has been established for well over a decade. We propose investigating if this classification system can be applied to kidney allografts to standardize the categorization of transplant IgAN. More importantly, successful use of this classification could assist in selecting patients for prospective interventional trials and defining better treatments. In this literature review, we explore the available literature on the Oxford classification and its utility in describing the disease and predicting graft loss in IgA nephropathy within the context of kidney transplantation.

Effect of Early Enteral Nutrition on Graft Loss After Living Donor Liver Transplantation: A Propensity Score Matching Analysis

This study aimed to elucidate the effect of early enteral nutrition on graft loss within 12 h after living-donor liver transplantation (LDLT) using propensity score-matching analysis and subsequently examine the risk factors for graft loss after LDLT. We retrospectively reviewed the data of 467 LDLT patients who were assigned to the early and non-early groups based on the optimal cutoff value of 12 h for the starting time of early enteral nutrition after LDLT to predict graft loss. The 1-year graft survival rate of the early group before propensity score-matching was 92.1%, whereas the 1-year graft survival rate of the non-early group was 86.2%. There was no significant difference between the 2 groups (P=.067). The incidences of early allograft dysfunction (EAD), small-for-size graft (SFSG) syndrome, acute cellular rejection (ACR), and sepsis were not statistically different between the 2 groups (P=.12, .91, .46, and .056, respectively). After propensity score-matching, the 1-year graft survival rate of the early group was 94.4%, whereas the 1-year graft survival rate of the non-early group was 85.4% (P=.034). The incidences of EAD, SFSG syndrome, and ACR were not statistically different between the 2 groups (P=.43, .81, and .24, respectively). However, the incidence of sepsis was statistically different between the 2 groups (non-early: 10.7% vs early: 3.6%, P=.038). Early enteral nutrition within 12 h after LDLT may contribute to better graft survival in LDLT patients by preventing sepsis.

Novel transcriptomic signatures associated with premature kidney allograft failure

The power to predict kidney allograft outcomes based on non-invasive assays is limited. Assessment of operational tolerance (OT) patients allows us to identify transcriptomic signatures of true non-responders for construction of predictive models. In this observational retrospective study, RNA sequencing of peripheral blood was used in a derivation cohort to identify a protective set of transcripts by comparing 15 OT patients (40% females), from the TOMOGRAM Study (NCT05124444), 14 chronic active antibody-mediated rejection (CABMR) and 23 stable graft function patients ≥15 years (STA). The selected differentially expressed transcripts between OT and CABMR were used in a validation cohort (n=396) to predict 3-year kidney allograft loss at 3 time-points using RT-qPCR. Archetypal analysis and classifier performance of RNA sequencing data showed that OT is clearly distinguishable from CABMR, but similar to STA. Based on significant transcripts from the validation cohort in univariable analysis, 2 multivariable Cox models were created. A 3-transcript (ADGRG3, ATG2A, and GNLY) model from POD 7 predicted graft loss with C-statistics (C) 0.727 (95% CI, 0.638-0.820). Another 3-transcript (IGHM, CD5, GNLY) model from M3 predicted graft loss with C 0.786 (95% CI, 0.785-0.865). Combining 3-transcripts models with eGFR at POD 7 and M3 improved C-statistics to 0.860 (95% CI, 0.778-0.944) and 0.868 (95% CI, 0.790-0.944), respectively. Identification of transcripts distinguishing OT from CABMR allowed us to construct models predicting premature graft loss. Identified transcripts reflect mechanisms of injury/repair and alloimmune response when assessed at day 7 or with a loss of protective phenotype when assessed at month3. Supported by the Ministry of Health of the Czech Republic under grant NV19-06-00031.

The Role of Donor Gamma-Glutamyl Transferase as a Risk Factor for Early Graft Function after Liver Transplantation.

Growing interest has been recently reported in the potential detrimental role of donor gamma-glutamyl transferase (GGT) peak at the time of organ procurement regarding the risk of poor outcomes after liver transplantation (LT). However, the literature on this topic is scarce and controversial data exist on the mechanisms justifying such a correlation. This study aims to demonstrate the adverse effect of donor GGT in a large European LT cohort regarding 90-day post-transplant graft loss. This is a retrospective international study investigating 1335 adult patients receiving a first LT from January 2004 to September 2018 in four collaborative European centers. Two different multivariable logistic regression models were constructed to evaluate the risk factors for 90-day post-transplant graft loss, introducing donor GGT as a continuous or dichotomous variable. In both models, donor GGT showed an independent role as a predictor of graft loss. In detail, the log-transformed continuous donor GGT value showed an odds ratio of 1.46 (95% CI = 1.03-2.07; p = 0.03). When the donor GGT peak value was dichotomized using a cut-off of 160 IU/L, the odds ratio was 1.90 (95% CI = 1.20-3.02; p = 0.006). When the graft-loss rates were investigated, significantly higher rates were reported in LT cases with donor GGT ≥160 IU/L. In detail, 90-day graft-loss rates were 23.2% vs. 13.9% in patients with high vs. low donor GGT, respectively (log-rank p = 0.004). Donor GGT was also added to scores conventionally used to predict outcomes (i.e., MELD, D-MELD, DRI, and BAR scores). In all cases, when the score was combined with the donor GGT, an improvement in the model accuracy was observed. Donor GGT could represent a valuable marker for evaluating graft quality at transplantation. Donor GGT should be implemented in scores aimed at predicting post-transplant clinical outcomes. The exact mechanisms correlating GGT and poor LT outcomes should be better clarified and need prospective studies focused on this topic.

Predictors of graft failure after first detection of de novo donor-specific HLA antibodies in kidney transplant recipients.

De novo donor-specific antibodies (dnDSAs) may cause antibody-mediated rejection and graft dysfunction. Little is known about the clinical course after first detection of dnDSAs during screening in asymptomatic patients. We aimed to assess the value of estimated glomerular filtration rate (eGFR) and proteinuria to predict graft failure in patients with dnDSAs and their potential utility as surrogate endpoints. All 400 kidney transplant recipients with dnDSAs at our centre (1 March 2000-31 May 2021) were included in this retrospective study. The dates of graft loss, rejection, doubling of creatinine, ≥30% eGFR decline, proteinuria ≥500mg/g and ≥1000mg/g were registered from the first dnDSA appearance. During 8.3years of follow-up, graft failure occurred in 33.3% of patients. Baseline eGFR and proteinuria correlated with 5-year graft loss (area under the receiver operating characteristics curve 0.75 and 0.80, P<.001). Creatinine doubled after a median of 2.8years [interquartile range (IQR) 1.5-5.0] from dnDSA and the time from doubling creatinine to graft failure was 1.0year (IQR 0.4-2.9). Analysing eGFR reduction ≥30% as a surrogate endpoint (148/400), the time from dnDSA to this event was 2.0years (IQR 0.6-4.2), with a positive predictive value (PPV) of 45.9% to predict graft loss, which occurred after 2.0years (IQR 0.8-3.2). The median time from proteinuria ≥500mg/g and ≥1000mg/g to graft failure was identical, 1.8years, with a PPV of 43.8% and 49.0%, respectively. Composite endpoints did not improve PPV. Multivariable analysis showed that rejection was the most important independent risk factor for all renal endpoints and graft loss. Renal function, proteinuria and rejection are strongly associated with graft failure in patients with dnDSA and may serve as surrogate endpoints.

#5887 RESULTS OF KIDNEY TRANSPLANTATION IN VERY-HIGHLY SENSITIZED PATIENTS INCLUDED IN AN EXCHANGE PROGRAM BASED ON VIRTUAL CROSSMATCH

Abstract Background and Aims In 2012, a kidney transplant (KT) exchange program for very-highly sensitized patients (panel reactive antibody [PRA] ≥95%) began in Andalusia between the 5 KT centers based on virtual crossmatch. Method Retrospective cohort study of KT recipients within the Andalusian very-highly sensitized patients’ program from 2012 to December 2021. Survival was analyzed using Kaplan-Meier. We performed a multivariable analysis using Cox regression for pre-KT risk factors for graft survival. Results During the study period, 213 KT were performed. The median PRA was 98%. of them, 18.7% were KT from type-III non-heart-beating donors and 69.5% were retransplanted patients. The median time on renal replacement therapy until KT was 1509 days and, from their inclusion in the very-highly sensitized patients’ program, 334 days. The median post-KT follow-up time was 1103 days. Patient/graft survival was 97.5%/91.7% and 82%/76.5% at one year and 5 years, respectively. Graft survival was lower in patients who had received more than one KT (P = .029). However, we found no differences when comparing PRA (&amp;lt; or ≥98%) or type of donor. In the multivariable analysis, the number of previous KT was a predictor of graft loss. We had more detailed information on renal function and post-KT complications in patients from 3 centers (n=104). Six patients (5.7%) developed donor-specific antibodies (DSA) (1 class I, 3 class II, 2 class I and II) after a median of 267.5 days post-KT. Ten patients (9.3%) presented acute rejection (AR) diagnosed by biopsy: 4 T-cell-mediated rejection (3.7%) and 6 antibody-mediated rejection (5.6%). Active antibody-mediated chronic rejection (CR) was diagnosed in 5 patients and BK nephropathy in 2. Three graft losses were due to immunological causes (1 RA, 2 RC). Median renal function at one year was: Crp 1.3 mg/dl and glomerular filtration rate 55.1 ml/min. Conclusion Despite being a high-risk group, the survival results have been very satisfactory. The number of previous KT is a risk factor for graft survival.

A novel prognostic nomogram predicts premature failure of kidney allografts with IgA nephropathy recurrence.

Recurrence of immunoglobulin A nephropathy (IgAN) limits graft survival in kidney transplantation. However, predictors of a worse outcome are poorly understood. Among 442 kidney transplant recipients (KTRs) with IgAN, 83 (18.8%) KTRs exhibited biopsy-proven IgAN recurrence between 1994 and 2020 and were enrolled in the derivation cohort. A multivariable Cox model predicting allograft loss based on clinical data at the biopsy and a web-based nomogram were developed. The nomogram was externally validated using an independent cohort (n=67). Patient age <43years {hazard ratio [HR] 2.20 [95% confidence interval (CI) 1.41-3.43], P<.001}, female gender [HR 1.72 (95% CI 1.07-2.76), P=.026] and retransplantation status [HR 1.98 (95% CI 1.13-3.36), P=.016] were identified as independent risk factors for IgAN recurrence. Patient age <43years [HR 2.77 (95% CI 1.17-6.56), P=.02], proteinuria >1g/24hours [HR 3.12 (95% CI 1.40-6.91), P=.005] and C4d positivity [HR 2.93 (95% CI 1.26-6.83), P=.013] were found to be associated with graft loss in patients with IgAN recurrence. A nomogram predicting graft loss was constructed based on clinical and histological variables, with a C statistic of 0.736 for the derivation cohort and 0.807 for the external validation cohort. The established nomogram identified patients with recurrent IgAN at risk for premature graft loss with good predictive performance.

25(OH)D-but not 1,25(OH)2D-Is an independent risk factor predicting graft loss in stable kidney transplant recipients.

Vitamin D deficiency (VDD) or vitamin D insufficiency is common in kidney transplant recipients (KTRs). The impact of VDD on clinical outcomes in KTRs remain poorly defined and the most suitable marker for assessing vitamin D nutritional status in KTRs is unknown so far. We conducted a prospective study including 600 stable KTRs (367 men, 233 women) and a meta-analysis to pool existing evidence to determine whether 25(OH)D or 1,25(OH)2D predicted graft failure and all-cause mortality in stable KTRs. Compared with a higher 25(OH)D concentration, a low concentration of 25(OH)D was a risk factor for graft failure (HR 0.946, 95% CI 0.912-0.981, p = 0.003), whereas 1,25 (OH)2D was not associated with the study end-point graft loss (HR 0.993, 95% CI 0.977-1.009, p = 0.402). No association was found between either 25(OH)D or 1,25 (OH)2D and all-cause mortality. We furthermore conducted a meta-analysis including 8 studies regarding the association between 25(OH)D or 1,25(OH)2D and graft failure or mortality, including our study. The meta-analysis results were consistent with our study in finding that lower 25(OH)D levels were significantly associated with the risk of graft failure (OR = 1.04, 95% CI: 1.01-1.07), but not associated with mortality (OR = 1.00, 95% CI: 0.98-1.03). Lower 1,25(OH)2D levels were not associated with the risk of graft failure (OR = 1.01, 95% CI: 0.99-1.02) and mortality (OR = 1.01, 95% CI: 0.99-1.02). Baseline 25(OH)D concentrations but not 1,25(OH)2D concentrations were independently and inversely associated with graft loss in adult KTRs.

Ferroptosis related gene signature in T cell-mediated rejection after kidney transplantation

BackgroundT cell-mediated rejection is an important factor affecting early transplant kidney survival. Ferroptosis has been shown to play a pathogenic role in a variety of diseases, which was not reported in TCMR. Here we developed a model for assessing activation of ferroptosis-related genes in TCMR to find a better screening method and explore the contribution of ferroptosis in TCMR.MethodsWe performed unsupervised consensus clustering according to expression of ferroptosis-related genes based on RNA-seq data from kidney transplant biopsies, and developed an assessment model characterized by ferroptosis gene expression through PCA, which was evaluated in multiple external datasets as well as blood and urine samples. Pathway enrichment and immune cell infiltration analysis were used to explore the possible targets and pathways involved in ferroptosis and TCMR.ResultsA ferroptosis gene expression scoring model was established. The diagnostic specificity and sensitivity of TCMR in renal biopsy samples were both over 80%, AUC = 0.843, and AUC was around 0.8 in multi-dataset validation, and was also close to 0.7 in blood and urine samples, while in predicting of graft survival at 3 years, scoring model had a good prognostic effect as well. Pathway enrichment and PPI network speculated that TLR4, CD44, IFNG, etc. may be the key genes of ferroptosis in TCMR.ConclusionsFerroptosis scoring model could better diagnose TCMR and predict graft loss, and could be used as a potential screening method in blood and urine samples. We speculate that ferroptosis plays an important role in TCMR.

Factor V as a predictor of graft loss after liver transplant

Factor V as a predictor of graft loss after liver transplant