Trajectories of glomerular filtration rate and progression to end stage kidney disease after kidney transplantation

Value of original and modified pathological scoring systems for prognostic prediction in paraffin-embedded donor kidney core biopsy

BACKGROUND

No study has validated, compared and adapted scoring systems for prognosis prediction based on donor kidney core biopsy (CB), with less glomeruli than wedge biopsy.

METHODS

A total of 185 donor kidney CB specimens were reviewed using seven scoring systems. The association between the total score, item scores, score-based grading, and allograft prognosis was investigated. In specimens with less than ten glomeruli (88/185, 47.6%), scoring systems were modified by adjusting weights of the item scores.

RESULTS

The Maryland aggregate pathology index (MAPI) score-based grading and periglomerular fibrosis (PGF) associated with delayed graft function (DGF) (Grade: OR = 1.59, p < 0.001; PGF: OR = 1.06, p = 0.006). Total score, score-based grading and chronic lesion score in scoring systems associated with one-year and 3-year eGFR after transplantation. Total-score-based models had similar predictive capacities for eGFR in all scoring systems, except MAPI and Ugarte. Score of glomerulosclerosis (GS), interstitial fibrosis (IF), tubular atrophy (TA), and arteriolar hyalinosis (AH) had good eGFR predictive capacities. In specimens with less than ten glomeruli, modified scoring systems had better eGFR predictive capacities than original scoring systems.

CONCLUSIONS

Scoring systems could predict allograft prognosis in paraffin-embedded CB with ten more glomeruli. A simple and pragmatic scoring system should include GS, IF, TA and AH, with weights assigned based on predictive capacity for prognosis. Replacing GS scores with tubulointerstitial scores could significantly improve the predictive capacity of eGFR. The conclusion should be further validated in frozen section.

An observational cohort study examined the change point of kidney function stabilization in the initial period after transplantation

Baseline kidney function following kidney transplantation is often used in research and clinical decision-making yet is not well defined. Here, a method to determine baseline function was proposed and validated on three single-center retrospective cohorts consisting of 922 patients from Belgium (main cohort) and two validation cohorts of 987 patients from the Netherlands and 519 patients from Germany. For each transplant, a segmented regression model was fitted on the estimated glomerular filtration rate (eGFR) evolution during the first-year post-transplantation. This yielded estimates for change point timing, rate of eGFR change before and after change point and eGFR value at change point, now considered the "baseline function". Associations of eGFR evolution with recipient/donor characteristics and the graft failure rate were assessed with linear regression and Cox regression respectively. The change point occurred on average at an eGFR value of 43.7±14.6 mL/min/1.73m2, at a median time of 6.5 days post-transplantation. Despite significant associations with several baseline donor-recipient characteristics (particularly, donor type; living vs deceased), the predictive value of these characteristics for eGFR value and timing of the change point was limited. This followed from a large heterogeneity within eGFR trajectories, which in turn indicated that favorable levels of kidney function could be reached despite a suboptimal initial evolution. Segmented regression consistently provided a good fit to early eGFR evolution, and its estimate of the change point can be a useful reference value in future analyses. Thus, our study shows that baseline kidney function after transplantation is heterogeneous and partly related to pretransplant donor characteristics.

Management of Failing Kidney and Pancreas Transplantations

Survival rates for allografts have improved over the last 2 decades, yet failing allografts remains a challenge in the field of transplant. The risks of mortality and morbidity associated with failed allografts are compounded by infectious complications and metabolic abnormalities, emphasizing the need for a standardized approach to management. Management of failing allografts lacks consensus, highlighting the need for unified protocols to guide treatment protocols and minimize risks with postdialysis initiation. The decision to wean off immunosuppression depends on various factors, including living donor availability and infectious risks, necessitating improved coordination of care and a standard guideline. Treatment of failed pancreas focuses on glycemic control, with insulin as the mainstay, while considering surgical interventions such as graft pancreatectomy in advanced symptomatic cases. Navigating the complexities of failed allograft management demands a multidisciplinary approach and standardized stepwise protocol. Addressing the gaps in management plans for failing allografts and employing a systematic approach to transplant decisions will enhance patient outcomes and facilitate informed decision-making.

Chronic Lung Allograft Dysfunction: Clinical Manifestations and Immunologic Mechanisms

The term "chronic lung allograft dysfunction" has emerged to describe the clinical syndrome of progressive, largely irreversible dysfunction of pulmonary allografts. This umbrella term comprises 2 major clinical phenotypes: bronchiolitis obliterans syndrome and restrictive allograft syndrome. Here, we discuss the clinical manifestations, diagnostic challenges, and potential therapeutic avenues to address this major barrier to improved long-term outcomes. In addition, we review the immunologic mechanisms thought to propagate each phenotype of chronic lung allograft dysfunction, discuss the various models used to study this process, describe potential therapeutic targets, and identify key unknowns that must be evaluated by future research strategies.

Identification of liver transplant biopsy phenotypes associated with distinct liver biological markers and allograft survival

The intricate association between histologic lesions and circulating antihuman leucocyte antigen donor-specific antibodies (DSA) in liver transplantation (LT) requires further clarification. We conducted a probabilistic, unsupervised approach in a comprehensively well-annotated LT cohort to identify clinically relevant archetypes. We evaluated 490 pairs of LT biopsies with DSA testing from 325 recipients transplanted between 2010 and 2020 across 3 French centers and an external cohort of 202 biopsies from 128 recipients. Unsupervised archetypal analysis integrated all clinico-immuno-histologic parameters of each biopsy to identify biopsy archetypes. The median time after LT was 1.17 (interquartile range, 0.38-2.38) years. We identified 7 archetypes distinguished by clinico-immuno-histologic parameters: archetype #1: severe T cell-mediated rejection (15.9%); #2: chronic rejection with ductopenia (1.8%); #3: architectural and microvascular damages (3.5%); #4: (sub)normal (55.9%); #5: mild T cell-mediated rejection (4.9%); #6: acute antibody-mediated rejection (6.5%); and #7: chronic rejection with DSA (11.4%). Cell infiltrates vary in the archetype. These archetypes were associated with distinct liver biological markers and allograft outcomes. These findings remained consistent when stratified using the patient's age or indications for LT, with good performance in the external cohort (mean highest probability assignment = 0.58, standard deviation ± 0.17). In conclusion, we have identified clinically meaningful archetypes, providing valuable insights into the intricate DSA-histology association, which may help standardize liver allograft pathology classification.

Development and validation of LCMM prediction algorithms to estimate recovery pattern of postoperative AKI in type A aortic dissection: a retrospective study

Background

Postoperative acute kidney injury (PO-AKI) is a prevalent complication among patients with acute type A aortic dissection (aTAAD) for which unrecognized trajectories of renal function recovery, and their heterogeneity, may underpin poor success in identifying effective therapies.

Methods

This was a retrospective, single-center cohort study in a regional Great Vessel Center including patients undergoing aortic dissection surgery. Estimated glomerular filtration rate (eGFR) recovery trajectories of PO-AKI were defined through the unsupervised latent class mixture modeling (LCMM), with an assessment of patient and procedural characteristics, complications, and early-term survival. Internal validation was performed by resampling.

Results

A total of 1,295 aTAAD patients underwent surgery and 645 (49.8%) developed PO-AKI. Among the PO-AKI cohort, the LCMM identified two distinct eGFR trajectories: early recovery (ER-AKI, 51.8% of patients) and late or no recovery (LNR-AKI, 48.2% of patients). Binary logistic regression identified five critical determinants regarding poor renal recovery, including chronic kidney disease (CKD) history, renal hypoperfusion, circulation arrest time, intraoperative urine, and myoglobin. LNR-AKI was associated with increased mortality, continuous renal replacement therapies, mechanical ventilation, ICU stay, and hospital stay. The assessment of the predictive model was good, with an area under the curve (AUC) of 0.73 (95% CI: 0.69-0.76), sensitivity of 61.74%, and specificity of 75.15%. The internal validation derived a consistent average AUC of 0.73. The nomogram was constructed for clinicians' convenience.

Conclusion

Our study explored the PO-AKI recovery patterns among surgical aTAAD patients and identified critical determinants that help to predict individuals at risk of poor recovery of renal function.

How to deal with renal toxicities from immune-based combination treatments in metastatic renal cell carcinoma. A nephrological consultation for Oncologists

We are witnessing a revolution in the treatment of metastatic renal cell carcinoma (mRCC). Indeed, several immune-based combinations (ICI [immune checkpoint inhibitor] + ICI, or ICI + antiangiogenic agents) have been approved as first-line therapy for mRCC after demonstrating superior efficacy over the previous standard. Despite all the improvements made, safety remains a critical issue, adverse events (AEs) being the main reason for drug discontinuations or dose reductions, ultimately resulting in an increased risk of losing efficacy. Thus, a good understanding of the AEs associated with the use of immune-based combinations, their prevention, and management, are key in order to maximize therapeutic effectiveness. Among these AEs, renal ones are relatively frequent, but always difficult to be diagnosed, not to take into account that it is often difficult to determine which drug is to blame for such toxicities. Chronic kidney disease (CKD) is a common finding in patients with RCC, either as a pre-existing condition and/or as a consequence of cancer and its treatment; furthermore, CKD, especially in advanced stages and in patients undergoing dialysis, may influence the pharmacokinetics and pharmacodynamics properties of anticancer agents. Finally, managing cancer therapy in kidney transplanted patients is another challenge. In this review, we discuss the therapy management of immune-based combinations in patients with CKD, on dialysis, or transplanted, as well as their renal toxicities, with a focus on their prevention, detection and practical management, taking into account the crucial role of the consulting nephrologist within the multidisciplinary care of these patients.

Outcomes of kidneys used for transplantation: an analysis of survival and function

Introduction

Kidney transplant recipients expect to survive the procedure with sufficient renal function for reliable dialysis freedom.

Methods

Transplant outcomes (survival and estimated renal function) were assessed after live and deceased donor transplantation from the US national database. Outcomes were stratified by age (donor and recipient) and donor type.

Results

Aggregate recipient outcomes were better transplanting living vs deceased donated kidneys. However, when stratified by the one-year renal function (within KDIGO CKD stage stratifications), surviving recipients had clinically similar dialysis-freedom, irrespective of donor type or age. The major outcome differences for recipients of age-stratified live and deceased kidneys was 1) the increasing frequency of one-year graft failures and 2) the increasing likelihood of severely limited renal function (CKD 4/5) with advancing donor age. Over 30% of recipients of deceased kidneys >65 years had either one-year graft failure or severely limited renal function contrasted to less than 15% of recipients of live kidneys aged >65 years.

Conclusions

Evolving techniques to reduce adverse events after urgent vs elective procedures, plus improved transplant outcome predictability with increased-age deceased donor kidneys using advanced predictive analytics (using age-stratified live kidney transplantation outcomes as a relevant reference point) should facilitate similar kidney transplant outcomes, irrespective of donor type.

Stratification of Kidney Transplant Recipients Into Five Subgroups Based on Temporal Disease Trajectories

Background

Kidney transplantation is the treatment of choice for patients with end-stage renal disease. Considerable clinical research has focused on improving graft survival and an increasing number of kidney recipients die with a functioning graft. There is a need to improve patient survival and to better understand the individualized risk of comorbidities and complications. Here, we developed a method to stratify recipients into similar subgroups based on previous comorbidities and subsequently identify complications and for a subpopulation, laboratory test values associated with survival.

Methods

First, we identified significant disease patterns based on all hospital diagnoses from the Danish National Patient Registry for 5752 kidney transplant recipients from 1977 to 2018. Using hierarchical clustering, these longitudinal patterns of diseases segregate into 3 main clusters of glomerulonephritis, hypertension, and diabetes. As some recipients are diagnosed with diseases from >1 cluster, recipients are further stratified into 5 more fine-grained trajectory subgroups for which survival, stratified complication patterns as well as laboratory test values are analyzed.

Results

The study replicated known associations indicating that diabetes and low levels of albumin are associated with worse survival when investigating all recipients. However, stratification of recipients by trajectory subgroup showed additional associations. For recipients with glomerulonephritis, higher levels of basophils are significantly associated with poor survival, and these patients are more often diagnosed with bacterial infections. Additional associations were also found.

Conclusions

This study demonstrates that disease trajectories can confirm known comorbidities and furthermore stratify kidney transplant recipients into clinical subgroups in which we can characterize stratified risk factors. We hope to motivate future studies to stratify recipients into more fine-grained, homogenous subgroups to better discover associations relevant for the individual patient and thereby enable more personalized disease-management and improve long-term outcomes and survival.

Trajectories of kidney function and risk of mortality

BACKGROUND

We aimed to identify patterns within the rate of kidney function decline, determinants of these patterns and their association with all-cause mortality risk in the general population.

METHODS

Participants aged ≥ 45 years with at least one assessment of creatinine-based estimated glomerular filtration rate (eGFR) taken between 1997 and 2018 were selected from a population-based cohort study. Analyses were performed using several distinct latent class trajectory modelling methods. Cumulative incidences were calculated with 45 years of age as the starting point.

RESULTS

In 12 062 participants (85 922 eGFR assessments, mean age 67.0 years, 58.7% women, median follow-up 9.6 years), four trajectories of eGFR change with age were identified: slow eGFR decline [rate of change in mL/min/1.73 m2 per year (RC), -0.9; 95% CI, -0.9 to -0.9; reference group], intermediate eGFR decline (RC, -2.5; 95% CI, -2.7 to -2.5) and fast eGFR decline (RC, -4.3; 95% CI, -4.4 to -4.1), and an increase/stable eGFR (RC, 0.3; 95% CI, 0.3 to 0.4). Women were more likely to have an increase/stable eGFR [odds ratio (OR), 1.94; 95% CI, 1.53 to 2.46] whereas men were more likely to have a fast eGFR decline (OR, 1.86; 95% CI, 1.33 to 2.60). Participants with diabetes, cardiovascular disease (CVD) or hypertension were more likely to have an intermediate or fast eGFR decline. All-cause mortality risks (cumulative incidence at age of 70 years) were 32.3% (95% CI, 21.4 to 47.9, slow eGFR decline), 6.7% (95% CI, 3.5 to 12.4, intermediate eGFR decline), 68.8% (95% CI, 44.4 to 87.8, fast eGFR decline) and 9.5% (95% CI, 5.5 to 15.7, increase/stable eGFR).

CONCLUSION

Sex, hypertension, diabetes and CVD were identified as trajectory membership determinants. Having fast eGFR decline was associated with the highest risk of all-cause mortality, highlighting the need for extensive monitoring and prevention of kidney function decline in individuals at risk of having fast eGFR decline.

Multi-phasic eGFR trajectory during follow up and long-term graft failure after kidney transplantation

BACKGROUND

The prevailing assumption is that following kidney transplantation the pattern of kidney function decline is consistent. Nevertheless, numerous factors leading to graft loss may emerge, altering the trajectory of kidney function. In this study, we aim to assess alterations in estimated glomerular filtration rate (eGFR) trajectory over an extended period of follow-up and examine its correlation with graft survival.

METHODS

We calculated eGFR using all creatinine values available from 1-year post transplantation to the end of follow-up. For pattern analysis, we used a piecewise linear model.

RESULTS

Nine hundred eighty-eight patients were included in the study. After a median follow-up of 5.2 years, 297 (30.1%) patients had a multi-phasic eGFR trajectory. Change in eGFR trajectory was associated with increased risk for graft failure (HR 7.15, 95% CI 5.17-9.89, p < .001), longer follow-up time, younger age, longer cold ischemia time, high prevalence of acute rejection, longer hospitalization and a lower initial eGFR. Of the 988 patients included in the study, 494 (50.0%) had a mono-phasic stable trajectory, 197 (19.9%) had a mono-phasic decreasing trajectory, 184 (18.6%) had bi-phasic decreasing trajectory (initial stability and then decline, 46(4.7%) had a bi-phasic stabilized (initial decline and then stabilization) and 67(6.8%) had a more complex trajectory (tri-phasic). Out of the total 144 patients who experienced graft loss, the predominant pattern was a bi-phasic decline characterized by a bi-linear trajectory (66 events, 45.8%).

CONCLUSIONS

Changes in eGFR trajectory during long-term follow-up can serve as a valuable tool for assessing the underlying mechanisms contributing to graft loss.

Challenges in the management of the kidney allograft: from decline to failure: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference

In March 2022, Kidney Disease: Improving Global Outcomes (KDIGO) held a virtual Controversies Conference to address the important but rarely examined phase during which the kidney transplant is failing or has failed. In addition to discussing the definition of a failing allograft, 4 broad areas were considered in the context of a declining functioning graft: prognosis and kidney failure trajectory; immunosuppression strategies; management of medical and psychological complications, and patient factors; and choice of kidney replacement therapy or supportive care following graft loss. Identifying and paying special attention to individuals with failing allografts was felt to be important in order to prepare patients psychologically, manage immunosuppression, address complications, prepare for dialysis and/or retransplantation, and transition to supportive care. Accurate prognostication tools, although not yet widely available, were embraced as necessary to define allograft survival trajectories and the likelihood of allograft failure. The decision of whether to withdraw or continue immunosuppression after allograft failure was deemed to be based most appropriately on risk-benefit analysis and likelihood of retransplantation within a few months. Psychological preparation and support was identified as a critical factor in patient adjustment to graft failure, as was early communication. Several models of care were noted that enabled a medically supportive transition back to dialysis or retransplantation. Emphasis was placed on the importance of dialysis-access readiness before initiation of dialysis, in order to avoid use of central venous catheters. The centrality of the patient to all management decisions and discussions was deemed to be paramount. Patient "activation," which can be defined as engaged agency, was seen as the most effective way to achieve success. Unresolved controversies, gaps in knowledge, and areas for research were also stressed in the conference deliberations.

Immune monitoring of allograft status in kidney transplant recipients

Kidney transplant patients require careful management of immunosuppression to avoid rejection while minimizing the risk of infection and malignancy for the best long-term outcome. The gold standard for monitoring allograft status and immunosuppression adequacy is a kidney biopsy, but this is invasive and costly. Conventional methods of allograft monitoring, such as serum creatinine level, are non-specific. Although they alert physicians to the need to evaluate graft dysfunction, by the time there is a clinical abnormality, allograft damage may have already occurred. The development of novel and non-invasive methods of evaluating allograft status are important to improving graft outcomes. This review summarizes the available conventional and novel methods for monitoring allograft status after kidney transplant. Novel and less invasive methods include gene expression, cell-free DNA, urinary biomarkers, and the use of artificial intelligence. The optimal method to manage patients after kidney transplant is still being investigated. The development of less invasive methods to assess allograft function has the potential to improve patient outcomes and allow for a more personalized approach to immunosuppression management.

Longitudinal estimated glomerular filtration rate (eGFR) modeling in long-term renal function to inform clinical trial design in kidney transplantation

Kidney transplantation is the preferred treatment for individuals with end-stage kidney disease. From a modeling perspective, our understanding of kidney function trajectories after transplantation remains limited. Current modeling of kidney function post-transplantation is focused on linear slopes or percent decline and often excludes the highly variable early timepoints post-transplantation, where kidney function recovers and then stabilizes. Using estimated glomerular filtration rate (eGFR), a well-known biomarker of kidney function, from an aggregated dataset of 4904 kidney transplant patients including both observational studies and clinical trials, we developed a longitudinal model of kidney function trajectories from time of transplant to 6 years post-transplant. Our model is a nonlinear, mixed-effects model built in NONMEM that captured both the recovery phase after kidney transplantation, where the graft recovers function, and the long-term phase of stabilization and slow decline. Model fit was assessed using diagnostic plots and individual fits. Model performance, assessed via visual predictive checks, suggests accurate model predictions of eGFR at the median and lower 95% quantiles of eGFR, ranges which are of critical clinical importance for assessing loss of kidney function. Various clinically relevant covariates were also explored and found to improve the model. For example, transplant recipients of deceased donors recover function more slowly after transplantation and calcineurin inhibitor use promotes faster long-term decay. Our work provides a generalizable, nonlinear model of kidney allograft function that will be useful for estimating eGFR up to 6 years post-transplant in various clinically relevant populations.

Prediction models for the recipients' ideal perioperative estimated glomerular filtration rates for predicting graft survival after adult living-donor kidney transplantation

Introduction

The impact of the perioperative estimated glomerular filtration rate (eGFR) on graft survival in kidney transplant recipients is yet to be evaluated. In this study, we developed prediction models for the ideal perioperative eGFRs in recipients.

Methods

We evaluated the impact of perioperative predicted ideal and actual eGFRs on graft survival by including 1,174 consecutive adult patients who underwent living-donor kidney transplantation (LDKT) between January 2008 and December 2020. Prediction models for the ideal perioperative eGFR were developed for 676 recipients who were randomly assigned to the training and validation sets (ratio: 7:3). The prediction models for the ideal best eGFR within 3 weeks and those at 1, 2, and 3 weeks after LDKT in 474 recipients were developed using 10-fold validation and stepwise multiple regression model analyzes. The developed prediction models were validated in 202 recipients. Finally, the impact of perioperative predicted ideal eGFRs/actual eGFRs on graft survival was investigated using Fine-Gray regression analysis.

Results

The correlation coefficients of the predicted ideal best eGFR within 3 weeks and the predicted ideal eGFRs at 1, 2, and 3 weeks after LDKT were 0.651, 0.600, 0.598, and 0.617, respectively. Multivariate analyzes for graft loss demonstrated significant differences in the predicted ideal best eGFR/actual best eGFR within 3 weeks and the predicted ideal eGFRs/actual eGFRs at 1, 2, and 3 weeks after LDKT.

Discussion

The predicted ideal best eGFR/actual best eGFR within 3 weeks and the predicted ideal eGFRs/actual eGFRs at 1, 2, and 3 weeks after LDKT were independent prognostic factors for graft loss. Therefore, the perioperative predicted ideal eGFR/actual eGFR may be useful for predicting graft survival after adult LDKT.

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.

Four trajectories of 24-hour urine protein levels in real-world lupus nephritis cohorts

OBJECTIVES

A 24-hour urine protein (24hUP) is a key measurement in the management of lupus nephritis (LN); however, trajectories of 24hUP in LN is poorly defined.

METHODS

Two LN cohorts that underwent renal biopsies at Renji Hospital were included. Patients received standard of care in a real-world setting and 24hUP data were collected over time. Trajectory patterns of 24hUP were determined using the latent class mixed modelling (LCMM). Baseline characters were compared among trajectories and multinomial logistic regression was used to determine independent risk factors. Optimal combinations of variables were identified for model construction and user-friendly nomograms were developed.

RESULTS

The derivation cohort composed of 194 patients with LN with 1479 study visits and a median follow-up of 17.5 (12.2-21.7) months. Four trajectories of 24hUP were identified, that is, Rapid Responders, Good Responders, Suboptimal Responders and Non-Responders, with the KDIGO renal complete remission rates (time to complete remission, months) of 84.2% (4.19), 79.6% (7.94), 40.4% (not applicable) and 9.8% (not applicable), respectively (p<0.001). The 'Rapid Responders' distinguish itself from other trajectories and a nomogram, composed of age, systemic lupus erythematosus duration, albumin and 24hUP yielded C-indices >0.85. Another nomogram to predict 'Good Responders' yielded C-indices of 0.73~0.78, which composed of gender, new-onset LN, glomerulosclerosis and partial remission within 6 months. When applied to the validation cohort with 117 patients and 500 study visits, nomograms effectively sorted out 'Rapid Responders' and 'Good Responders'.

CONCLUSION

Four trajectories of LN shed some light to guide the management of LN and further clinical trials design.

Heart Failure-Type Symptom Score Trajectories in CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study

RATIONALE & OBJECTIVE

Quality of life in chronic kidney disease (CKD) is impaired by a large burden of symptoms including some that overlap with the symptoms of heart failure (HF). We studied a group of individuals with CKD to understand the patterns and trajectories of HF-type symptoms in this setting.

STUDY DESIGN

Prospective cohort study.

SETTING & PARTICIPANTS

3,044 participants in the Chronic Renal Insufficiency Cohort (CRIC) without prior diagnosis of HF.

PREDICTORS

Sociodemographics, medical history, medications, vital signs, laboratory values, echocardiographic and electrocardiographic parameters.

OUTCOME

Trajectory over 5.5 years of a HF-type symptom score (modified Kansas City Cardiomyopathy Questionnaire [KCCQ] Overall Summary Score with a range of 0-100 where<75 reflects clinically significant symptoms).

ANALYTICAL APPROACH

Latent class mixed models were used to model trajectories. Multinomial logistic regression was used to model relationships of predictors with trajectory group membership.

RESULTS

Five trajectories of KCCQ score were identified in the cohort of 3,044 adults, 45% of whom were female, and whose median age was 61 years. Group 1 (41.7%) had a stable high score (minimal symptoms, average score of 96); groups 2 (35.6%) and 3 (15.6%) had stable but lower scores (mild symptoms [average of 81] and clinically significant symptoms [average of 52], respectively). Group 4 (4.9%) had a substantial worsening in symptoms over time (mean 31-point decline), and group 5 (2.2%) had a substantial improvement (mean 33-point increase) in KCCQ score. A majority of group 1 was male, without diabetes or obesity, and this group had higher baseline kidney function. A majority of groups 2 and 3 had diabetes and obesity. A majority of group 4 was male and had substantial proteinuria. Group 5 had the highest proportion of baseline cardiovascular disease (CVD).

LIMITATIONS

No validation cohort available, CKD management changes in recent years may alter trajectories, and latent class models depend on the missing at random assumption.

CONCLUSIONS

Distinct HF-type symptom burden trajectories were identified in the setting of CKD, corresponding to different baseline characteristics. These results highlight the diversity of HF-type symptom experiences in individuals with CKD.

Non-Traditional Non-Immunological Risk Factors for Kidney Allograft Loss-Opinion

Rates of late allograft loss have improved slowly in the last decades. Well described traditional risk factors that influence allograft survival include cardiovascular events, rejection, infections and post-transplant neoplasia. Here, we critically evaluate the influence of several non-immunological, non-traditional risk factors and describe their impact on allograft survival and cardiovascular health of kidney transplant recipients. We assessed the following risk factors: arterial stiffness, persistent arteriovenous access, mineral bone disease, immunosuppressive drugs residual levels variability, hypomagnesemia, glomerular pathological alterations not included in Banff criteria, persistent inflammation and metabolic acidosis.

Increased frequency of CD39+CD73+ regulatory T cells and Deltex-1 gene expression level in kidney transplant recipients with excellent long-term graft function

BACKGROUND

The ability of regulatory T cells (Tregs) to limit inflammatory responses has been demonstrated. However, different subpopulations of this cell have varying abilities to suppress alloreactive immune responses. The primary goal of this study was to assess the frequency of CD4⁺FOXP3⁺CD39⁺CD73⁺ Tregs and Deltex-1 gene expression on long-term renal transplant function.

METHODS

A total of 49 subjects were divided into 3 groups: (i) the excellent long-term graft function (ELTGF) group, (ii) the chronic graft dysfunction (CGD) group, and (iii) the healthy control (HC) group. Following sample collection, peripheral blood mononuclear cells (PBMCs) were isolated, and the Deltex-1 gene expression level and the frequency of CD4⁺FOXP3⁺CD39⁺CD73⁺ Tregs were evaluated.

RESULTS

The ELTGF group had more CD4⁺FOXP3⁺ Tregs than the CGD group, but the difference was not statistically significant (P = 0.07). However, the frequency of CD4⁺FOXP3⁺CD39⁺CD73⁺ Tregs and the ratio of these cells to total CD4⁺ lymphocytes significantly increased in the ELTGF group than in the CGD group (P = 0.04 and P = 0.02 respectively). In addition, the expression level of the Deltex-1 gene was significantly lower in the CGD group than in the other 2 groups (P = 0.01 and P = 0.04 respectively).

CONCLUSIONS

Given the increased frequency of CD4⁺FOXP3⁺CD39⁺CD73⁺ Tregs and the expression level of the Deltex-1 gene in the ELTGF group, it appears that these factors probably improved function and long-term survival of the transplanted organ through the suppression of alloreactive responses and reduction of inflammation. In other words, one of the immunological mechanisms involved in the CGD group may be a deficiency in Tregs.

Paving the Way for Personalized Medicine in First Kidney Transplantation: Interest of a Creatininemia Latent Class Analysis in Early Post-transplantation

Plasma creatinine is a marker of interest in renal transplantation but data on its kinetics in the first days following transplantation are scarce. The aim of this study was to identify clinically relevant subgroups of creatinine trajectories following renal transplantation and to test their association with graft outcome. Among 496 patients with a first kidney transplant included in the French ASTRE cohort at the Poitiers University hospital, 435 patients from donation after brain death were considered in a latent class modeling. Four distinct classes of creatinine trajectories were identified: "poor recovery" (6% of patients), "intermediate recovery" (47%), "good recovery" (10%) and "optimal recovery" (37%). Cold ischemia time was significantly lower in the "optimal recovery" class. Delayed graft function was more frequent and the number of hemodialysis sessions was higher in the "poor recovery" class. Incidence of graft loss was significantly lower in "optimal recovery" patients with an adjusted risk of graft loss 2.42 and 4.06 times higher in "intermediate recovery" and "poor recovery" patients, respectively. Our study highlights substantial heterogeneity in creatinine trajectories following renal transplantation that may help to identify patients who are more likely to experience a graft loss.

Risk of end-stage kidney disease in kidney transplant recipients versus patients with native chronic kidney disease: multicentre unmatched and propensity-score matched analyses

BACKGROUND

In kidney transplant recipients (KTR), the end-stage kidney disease (ESKD) risk dependent on the risk factors acting in native chronic kidney disease (CKD) remains undefined.

METHODS

We compared risk and determinants of ESKD between 757 adult KTR and 1940 patients with native CKD before and after propensity-score (PS) analysis matched for unmodifiable risk factors [(age, sex, diabetes, cardiovascular disease and estimated glomerular filtration rate (eGFR)].

RESULTS

In unmatched cohorts, eGFR was lower in CKD versus KTR (45.9 ± 11.3 versus 59.2 ± 13.4 mL/min/1.73 m2, P < 0.001). During a median follow-up of 5.4 years, the unadjusted cumulative incidence of ESKD was consistently lower in unmatched KTR versus CKD. Conversely, in PS-matched analysis, the risk of ESKD in KTR was 78% lower versus CKD at 1 year of follow-up while progressively increased over time resulting similar to that of native CKD patients after 5 years and 2.3-fold higher than that observed in CKD at 10 years. R2 analysis in unmatched patients showed that the proportion of the outcome variance explained by traditional ESKD determinants was smaller in KTR versus native CKD (31% versus 70%). After PS matching, the risk of ESKD [hazard ratio (HR), 95% confidence interval (95% CI)] was significantly associated with systolic blood pressure (1.02, 1.01-1.02), phosphorus (1.31, 1.05-1.64), 24-h proteinuria (1.11, 1.05-1.17) and haemoglobin (0.85, 0.78-0.93) irrespective of KTR status. Similar data were obtained after matching also for modifiable risk factors.

CONCLUSIONS

In KTR, when compared with matched native CKD patients, the risk of ESKD is lower in the first 5 years and higher later on. Traditional determinants of ESKD account for one-third of the variability of time-to-graft failure.

Conservative Kidney Management in Kidney Transplant Populations

Conservative kidney management (CKM) has been increasingly accepted as a therapeutic option for seriously ill patients with advanced chronic kidney disease. CKM is active medical management of advanced chronic kidney disease without dialysis, with a focus on delaying the worsening of kidney disease and minimizing symptom burden. CKM may be considered a suitable option for kidney transplant recipients with poorly functioning and declining allografts, defined as patients with low estimated glomerular filtration rate (<20 mL/min per 1.73 m2) who are approaching allograft failure. CKM may be a fitting option for transplant patients facing high morbidity and mortality with or without dialysis resumption, and it should be offered as a choice for this patient population. In this review, we describe clinical considerations in caring for patients with poorly functioning and declining kidney allografts, especially the unique decision-making process around kidney replacement therapies. We discuss ways to incorporate CKM as an option for these patients. We also discuss financial and policy considerations in providing CKM for this population. Patients with poorly functioning and declining kidney allografts should be supported throughout transitions of care by an interprofessional and multidisciplinary team attuned to their unique challenges. Further research on when, who, and how to integrate CKM into existing care structures for patients with poorly functioning and declining kidney allografts is needed.

Integration of artificial intelligence and multi-omics in kidney diseases

Kidney disease is a leading cause of death worldwide. Currently, the diagnosis of kidney diseases and the grading of their severity are mainly based on clinical features, which do not reveal the underlying molecular pathways. More recent surge of ∼omics studies has greatly catalyzed disease research. The advent of artificial intelligence (AI) has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically actionable knowledge. This review discusses how AI and multi-omics can be applied and integrated, to offer opportunities to develop novel diagnostic and therapeutic means in kidney diseases. The combination of new technology and novel analysis pipelines can lead to breakthroughs in expanding our understanding of disease pathogenesis, shedding new light on biomarkers and disease classification, as well as providing possibilities of precise treatment.

Pretransplant serum levels of endothelial cell activation markers are associated with graft loss and mortality after kidney transplantation

Long-term allograft survival remains a challenge in kidney transplantation. In this study, we aimed to identify biomarkers for potentially modifiable pathways involved in the outcome of kidney transplantation. We tested the hypothesis that a pre-existing systemic environment with endothelial cell activation in the recipient is associated with the outcome after kidney transplantation. In a retrospective study cohort of 611 kidney transplanted patients, we investigated associations between serum levels of soluble intercellular adhesion molecule-1 (sICAM-1) and soluble vascular cell adhesion molecule-1 (sVCAM-1) before transplantation and delayed graft function, acute rejection, graft loss and mortality after transplantation. We adjusted associations for age, sex, preformed donor-specific antibodies (DSA), pretransplant diabetes, cardiovascular disease and dialysis. Additionally, we investigated if associations between endothelial cell activation markers and outcomes differed in recipients with and without preformed DSA. Serum levels of endothelial cell activation markers were associated with delayed graft function and mortality but not with rejection. Additionally, high levels of sICAM-1 were associated with graft loss. Associations were most pronounced in recipients without DSA, adjusted for potential confounders. Data suggest that endothelial cell activation at the time of transplantation is associated with graft loss and mortality after kidney transplantation, especially in transplant candidates without preformed DSA.

Distinctive kidney function trajectories following left ventricular assist device implantation

BACKGROUND

The aim of this study was to assess for distinct kidney function trajectories following left ventricular assist device (LVAD) placement. Cohort studies of LVAD recipients demonstrate that kidney function tends to increase early after LVAD placement, followed by decline and limited sustained improvement. Inter-individual differences in kidney function response may be obscured.

METHODS

We identified continuous flow LVAD implantations in US adults (2016-2017) from INTERMACS (Interagency Registry for Mechanically Assisted Circulatory Support). Primary outcomes were estimated glomerular filtration rate (eGFR) trajectories pre-implantation to ∼12 months. Latent class mixed models were applied to primary and validation samples. Clinical differences among trajectory groups were investigated.

RESULTS

Among 4,615 LVAD implantations, 5 eGFR trajectory groups were identified. The 2 largest groups (Groups 1 and 2) made up >80% of the cohort, and were similar to group average trajectories previously reported, with early eGFR rise followed by decline and stabilization. Three novel trajectory groups were found: worsening followed by sustained low kidney function (Group 3, 10.1%), sustained improvement (Group 4, 3.3%), and worsening followed by variation (Group 5, 1.7%). These groups differed in baseline characteristics and outcomes. Group 4 was younger and had more cardiogenic shock and pre-implantation dialysis; Group 3 had higher rates of pre-existing chronic kidney disease, along with older age.

CONCLUSIONS

Novel eGFR trajectories were identified in a national cohort, possibly representing distinct cardiorenal processes. Type 1 cardiorenal syndrome may have been predominant in Group 4, and parenchymal kidney disease may have been predominant in Group 3.

Comparison of artificial intelligence and human-based prediction and stratification of the risk of long-term kidney allograft failure

BACKGROUND

Clinical decisions are mainly driven by the ability of physicians to apply risk stratification to patients. However, this task is difficult as it requires complex integration of numerous parameters and is impacted by patient heterogeneity. We sought to evaluate the ability of transplant physicians to predict the risk of long-term allograft failure and compare them to a validated artificial intelligence (AI) prediction algorithm.

METHODS

We randomly selected 400 kidney transplant recipients from a qualified dataset of 4000 patients. For each patient, 44 features routinely collected during the first-year post-transplant were compiled in an electronic health record (EHR). We enrolled 9 transplant physicians at various career stages. At 1-year post-transplant, they blindly predicted the long-term graft survival with probabilities for each patient. Their predictions were compared with those of a validated prediction system (iBox). We assessed the determinants of each physician's prediction using a random forest survival model.

RESULTS

Among the 400 patients included, 84 graft failures occurred at 7 years post-evaluation. The iBox system demonstrates the best predictive performance with a discrimination of 0.79 and a median calibration error of 5.79%, while physicians tend to overestimate the risk of graft failure. Physicians' risk predictions show wide heterogeneity with a moderate intraclass correlation of 0.58. The determinants of physicians' prediction are disparate, with poor agreement regardless of their clinical experience.

CONCLUSIONS

This study shows the overall limited performance and consistency of physicians to predict the risk of long-term graft failure, demonstrated by the superior performances of the iBox. This study supports the use of a companion tool to help physicians in their prognostic judgement and decision-making in clinical care.

DSA in solid organ transplantation: is it a matter of specificity, amount, or functional characteristics?

PURPOSE OF REVIEW

The present review describes the clinical relevance of human leukocyte antigen (HLA) donor-specific antibodies (HLA-DSAs) as biomarkers of alloimmunity and summarizes recent improvements in their characterization that provide insights into immune risk assessment, precision diagnosis, and prognostication in transplantation.

RECENT FINDINGS

Recent studies have addressed the clinical utility of HLA-DSAs as biomarkers for immune risk assessment in pretransplant and peritransplant, diagnosis and treatment evaluation of antibody-mediated rejection, immune monitoring posttransplant, and risk stratification.

SUMMARY

HLA-DSAs have proved to be the most advanced immune biomarkers in solid organ transplantation in terms of analytical validity, clinical validity and clinical utility. Recent studies are integrating multiple HLA-DSA characteristics including antibody specificity, HLA class, quantity, immunoglobulin G subclass, and complement-binding capacity to improve risk assessment peritransplant, diagnosis and treatment evaluation of antibody-mediated rejection, immune monitoring posttransplant, and transplant prognosis evaluation. In addition, integration of HLA-DSAs to clinical, functional and histological transplant parameters has further consolidated the utility of HLA-DSAs as robust biomarkers and allows to build new tools for monitoring, precision diagnosis, and risk stratification for individual patients. However, prospective and randomized-controlled studies addressing the clinical benefit and cost-effectiveness of HLA-DSA-based monitoring and patient management strategies are required to demonstrate that the use of HLA-DSAs as biomarkers can improve current clinical practice and transplant outcomes.

Evolution of the stage of chronic kidney disease from the diagnosis of hypertension in primary care

Objective

To analyze the evolution of the stages of CKD and the progression of the estimation of glomerular filtration rate (eGFR) in patients with newly diagnosed hypertension.

Design

Retrospective cohort.

Site

Family Medicine Unit No. 31, Mexican Social Security Institute, Mexico City.

Participants

Patients with hypertension who have been diagnosed in primary care and have developed chronic kidney disease.

Main measurements

The eGFR was calculated with the CKD Epi formula in three moments, the first measurement was at the time of diagnosis of hypertension, the second measurement was made when it arrived a change in CKD stage and the last one at the end of the study, with which the evolution time from one stage to another was obtained, as well as the drop in eGFR.

Results

The sample consisted of 207 electronic health records of patients, with an average follow-up of 10.2 years from the moment of diagnosis of hypertension until the end of the study. The average time to go from one baseline stage of CKD to another was 7 years (average decline in eGFR of 5.8 ml/min/year) and to have a second stage change was 3.2 years (average decline in eGFR of 6.8 ml/min/year), with a statistically significant repeated measures ANOVA (p < 0.001).

Conclusions

Patients with newly diagnosed hypertension remain longer in the initial stages of CKD, to later evolve and change more quickly.

Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in Japan

OBJECTIVES

Trajectories of estimated glomerular filtration rate (eGFR) decline vary highly among patients with chronic kidney disease (CKD). It is clinically important to identify patients who have high risk for eGFR decline. We aimed to identify clusters of patients with extremely rapid eGFR decline and develop a prediction model using a machine learning approach.

DESIGN

Retrospective single-centre cohort study.

SETTINGS

Tertiary referral university hospital in Toyoake city, Japan.

PARTICIPANTS

A total of 5657 patients with CKD with baseline eGFR of 30 mL/min/1.73 m² and eGFR decline of ≥30% within 2 years.

PRIMARY OUTCOME

Our main outcome was extremely rapid eGFR decline. To study-complicated eGFR behaviours, we first applied a variation of group-based trajectory model, which can find trajectory clusters according to the slope of eGFR decline. Our model identified high-level trajectory groups according to baseline eGFR values and simultaneous trajectory clusters. For each group, we developed prediction models that classified the steepest eGFR decline, defined as extremely rapid eGFR decline compared with others in the same group, where we used the random forest algorithm with clinical parameters.

RESULTS

Our clustering model first identified three high-level groups according to the baseline eGFR (G1, high GFR, 99.7±19.0; G2, intermediate GFR, 62.9±10.3 and G3, low GFR, 43.7±7.8); our model simultaneously found three eGFR trajectory clusters for each group, resulting in nine clusters with different slopes of eGFR decline. The areas under the curve for classifying the extremely rapid eGFR declines in the G1, G2 and G3 groups were 0.69 (95% CI, 0.63 to 0.76), 0.71 (95% CI 0.69 to 0.74) and 0.79 (95% CI 0.75 to 0.83), respectively. The random forest model identified haemoglobin, albumin and C reactive protein as important characteristics.

CONCLUSIONS

The random forest model could be useful in identifying patients with extremely rapid eGFR decline.

TRIAL REGISTRATION

UMIN 000037476; This study was registered with the UMIN Clinical Trials Registry.

Early Estimated Glomerular Filtration Rate Trajectories After Kidney Transplant Biopsy as a Surrogate Endpoint for Graft Survival in Late Antibody-Mediated Rejection

Background

Late antibody-mediated rejection (ABMR) after kidney transplantation is a major cause of long-term allograft loss with currently no proven treatment strategy. Design for trials testing treatment for late ABMR poses a major challenge as hard clinical endpoints require large sample sizes. We performed a retrospective cohort study applying commonly used selection criteria to evaluate the slope of the estimated glomerular filtration rate (eGFR) within an early and short timeframe after biopsy as a surrogate of future allograft loss for clinical trials addressing late ABMR.

Methods

Study subjects were identified upon screening of the Vienna transplant biopsy database. Main inclusion criteria were (i) a solitary kidney transplant between 2000 and 2013, (ii) diagnosis of ABMR according to the Banff 2015 scheme at >12 months post-transplantation, (iii) age 15-75 years at ABMR diagnosis, (iv) an eGFR > 25 mL/min/1.73 m2 at ABMR diagnosis, and (v) a follow-up for at least 36 months after ABMR diagnosis. The primary outcome variable was death-censored graft survival. A mixed effects model with linear splines was used for eGFR slope modeling and association of graft failure and eGFR slope was assessed applying a multivariate competing risk analysis with landmarks set at 12 and 24 months after index biopsy.

Results

A total of 70 allografts from 68 patients were included. An eGFR loss of 1 ml/min/1.73 m2 per year significantly increased the risk for allograft failure, when eGFR slopes were modeled over 12 months [HR 1.1 (95% CI: 1.01-1.3), p = 0.020] or over 24 months [HR 1.3 (95% CI: 1.1-1.4), p = 0.001] after diagnosis of ABMR with landmarks set at both time points. Covariables influencing graft loss in all models were histologic evidence of glomerulonephritis concurring with ABMR as well as the administration of anti-thymocyte globulin (ATG) at the time of transplantation.

Conclusion

Our study supports the use of the eGFR slope modeled for at least 12 months after biopsy-proven diagnosis of late ABMR, as a surrogate parameter for future allograft loss. The simultaneous occurrence of glomerulonephritis together with ABMR at index biopsy and the use of ATG at the time of transplantation-likely representing a confounder in pre-sensitized recipients-were strongly associated with worse transplant outcomes.

Forecasting of Patient-Specific Kidney Transplant Function With a Sequence-to-Sequence Deep Learning Model

IMPORTANCE

Like other clinical biomarkers, trajectories of estimated glomerular filtration rate (eGFR) after kidney transplant are characterized by intra-individual variability. These fluctuations hamper the distinction between alarming graft functional deterioration or harmless fluctuation within the patient-specific expected reference range of eGFR.

OBJECTIVE

To determine whether a deep learning model could accurately predict the patient-specific expected reference range of eGFR after kidney transplant.

DESIGN, SETTING, AND PARTICIPANTS

A multicenter diagnostic study consisted of a derivation cohort of 933 patients who received a kidney transplant between 2004 and 2013 with 100 867 eGFR measurements from University Hospitals Leuven, Belgium, and 2 independent test cohorts: with 39 999 eGFR measurements from 1 170 patients, 1 from University Hospitals Leuven, Belgium, receiving transplants between 2013 and 2018 and 1 from Hannover Medical School, Germany, receiving transplants between 2003 and 2007. Patients receiving a single kidney transplant, with consecutive eGFR measurements were included. Data were analyzed from February 2019 to April 2021.

EXPOSURES

In the derivation cohort 100 867 eGFR measurements were available for analysis and 39 999 eGFR measurements from the independent test cohorts.

MAIN OUTCOMES AND MEASURES

A sequence-to-sequence model was developed for prediction of a patient-specific expected range of eGFR, based on previous eGFR values. The primary outcome was the performance of the deep learning sequence-to-sequence model in the 2 independent cohorts.

RESULTS

In this diagnostic study, a total of 933 patients in the training sets (mean [SD] age, 53.5 [13.3] years; 570 male [61.1%]) and 1170 patients in the independent test sets (cohort 1 [n = 621]: mean [SD] age, 58.5 [12.1] years; 400 male [64.4%]; cohort 2 [n = 549]: mean [SD] age, 50.1 [13.0] years; 316 male [57.6%]) who received a single kidney transplant most frequently from deceased donors, the sequence-to-sequence models accurately predicted future patient-specific eGFR trajectories within the first 3 months after transplant, based on the previous graft eGFR values (root mean square error, 6.4-8.9 mL/min/1.73 m2). The sequence-to-sequence model predictions outperformed the more conventional autoregressive integrated moving average prediction model, at all input/output number of eGFR values.

CONCLUSIONS AND RELEVANCE

In this diagnostic study, a sequence-to-sequence deep learning model was developed and validated for individual forecasting of kidney transplant function. The patient-specific sequence predictions could be used in clinical practice to guide physicians on deviations from the expected intra-individual variability, rather than relating the individual results to the reference range of the healthy population.

Artificial intelligence and kidney transplantation

Artificial intelligence and its primary subfield, machine learning, have started to gain widespread use in medicine, including the field of kidney transplantation. We made a review of the literature that used artificial intelligence techniques in kidney transplantation. We located six main areas of kidney transplantation that artificial intelligence studies are focused on: Radiological evaluation of the allograft, pathological evaluation including molecular evaluation of the tissue, prediction of graft survival, optimizing the dose of immunosuppression, diagnosis of rejection, and prediction of early graft function. Machine learning techniques provide increased automation leading to faster evaluation and standardization, and show better performance compared to traditional statistical analysis. Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.

Exploring the Complexity of Death-Censored Kidney Allograft Failure

BACKGROUND

Few studies have thoroughly investigated the causes of kidney graft loss (GL), despite its importance.

METHODS

A novel approach assigns each persistent and relevant decline in renal function over the lifetime of a renal allograft to a standardized category, hypothesizing that singular or multiple events finally lead to GL. An adjudication committee of three physicians retrospectively evaluated indication biopsies, laboratory testing, and medical history of all 303 GLs among all 1642 recipients of transplants between January 1, 1997 and December 31, 2017 at a large university hospital to assign primary and/or secondary causes of GL.

RESULTS

In 51.2% of the patients, more than one cause contributed to GL. The most frequent primary or secondary causes leading to graft failure were intercurrent medical events in 36.3% of graft failures followed by T cell-mediated rejection (TCMR) in 34% and antibody-mediated rejection (ABMR) in 30.7%. In 77.9%, a primary cause could be attributed to GL, of which ABMR was most frequent (21.5%). Many causes for GL were identified, and predominant causes for GL varied over time.

CONCLUSIONS

GL is often multifactorial and more complex than previously thought.

A validation study of the 4-variable and 8-variable kidney failure risk equation in transplant recipients in the United Kingdom

Background

There is emerging evidence that the 4-variable Kidney Failure Risk Equation (KFRE) can be used for risk prediction of graft failure in transplant recipients. However, geographical validation of the 4-variable KFRE in transplant patients is lacking, as is whether the more extensive 8-variable KFRE improves predictive accuracy. This study aimed to validate the 4- and 8-variable KFRE predictions of the 5-year death-censored risk of graft failure in patients in the United Kingdom.

Methods

A retrospective cohort study involved 415 transplant recipients who had their first renal transplant between 2003 and 2015 and were under follow-up at Salford Royal NHS Foundation Trust. The KFRE risk scores were calculated on variables taken 1-year post-transplant. The area under the receiver operating characteristic curves (AUC) and calibration plots were evaluated to determine discrimination and calibration of the 4- and 8-variable KFREs in the whole cohort as well as in a subgroup analysis of living and deceased donor recipients and in patients with an eGFR< 45 ml/min/1.73m².

Results

There were 16 graft failure events (4%) in the whole cohort. The 4- and 8-variable KFREs showed good discrimination with AUC of 0.743 (95% confidence interval [CI] 0.610–0.876) and 0.751 (95% CI 0.629–0.872) respectively. In patients with an eGFR< 45 ml/min/1.73m², the 8-variable KFRE had good discrimination with an AUC of 0.785 (95% CI 0.558–0.982) but the 4-variable provided excellent discrimination in this group with an AUC of 0.817 (0.646–0.988). Calibration plots however showed poor calibration with risk scores tending to underestimate risk of graft failure in low-risk patients and overestimate risk in high-risk patients, which was seen in the primary and subgroup analyses.

Conclusions

Despite adequate discrimination, the 4- and 8-variable KFREs are imprecise in predicting graft failure in transplant recipients using data 1-year post-transplant. Larger, international studies involving diverse patient populations should be considered to corroborate these findings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-021-02259-4.

Predicting long-term kidney allograft outcomes: pitfalls and progress

Early identification of kidney transplant recipients at risk of progressive allograft dysfunction may allow clinicians to provide closer monitoring and more aggressive risk factor modification. In this issue, Raynaud et al. presented a latent class model that clustered kidney transplant recipients into 8 risk categories of post-transplant kidney function loss. This commentary discusses some of the advantages, but also challenges, of the use of latent class analyses, including the clinical applicability of models that are often derived from such approaches.

The Multi-Therapeutic Role of MSCs in Diabetic Nephropathy

Diabetic nephropathy (DN) is one of the most common diabetes mellitus (DM) microvascular complications, which always ends with end-stage renal disease (ESRD). Up to now, as the treatment of DN in clinic is still complicated, ESRD has become the main cause of death in diabetic patients. Mesenchymal stem cells (MSCs), with multi-differentiation potential and paracrine function, have attracted considerable attention in cell therapy recently. Increasing studies concerning the mechanisms and therapeutic effect of MSCs in DN emerged. This review summarizes several mechanisms of MSCs, especially MSCs derived exosomes in DN therapy, including hyperglycemia regulation, anti-inflammatory, anti-fibrosis, pro-angiogenesis, and renal function protection. We also emphasize the limitation of MSCs application in the clinic and the enhanced therapeutic role of pre-treated MSCs in the DN therapy. This review provides balanced and impartial views for MSC therapy as a promising strategy in diabetic kidney disease amelioration.