Is the Kidney Donor Risk Index a Useful Tool in Non-US Patients?

An external validation of the Kidney Donor Risk Index in the UK transplant population in the presence of semi-competing events

BACKGROUND

Transplantation represents the optimal treatment for many patients with end-stage kidney disease. When a donor kidney is available to a waitlisted patient, clinicians responsible for the care of the potential recipient must make the decision to accept or decline the offer based upon complex and variable information about the donor, the recipient and the transplant process. A clinical prediction model may be able to support clinicians in their decision-making. The Kidney Donor Risk Index (KDRI) was developed in the United States to predict graft failure following kidney transplantation. The survival process following transplantation consists of semi-competing events where death precludes graft failure, but not vice-versa.

METHODS

We externally validated the KDRI in the UK kidney transplant population and assessed whether validation under a semi-competing risks framework impacted predictive performance. Additionally, we explored whether the KDRI requires updating. We included 20,035 adult recipients of first, deceased donor, single, kidney-only transplants between January 1, 2004, and December 31, 2018, collected by the UK Transplant Registry and held by NHS Blood and Transplant. The outcomes of interest were 1- and 5-year graft failure following transplantation. In light of the semi-competing events, recipient death was handled in two ways: censoring patients at the time of death and modelling death as a competing event. Cox proportional hazard models were used to validate the KDRI when censoring graft failure by death, and cause-specific Cox models were used to account for death as a competing event.

RESULTS

The KDRI underestimated event probabilities for those at higher risk of graft failure. For 5-year graft failure, discrimination was poorer in the semi-competing risks model (0.625, 95% CI 0.611 to 0.640;0.611, 95% CI 0.597 to 0.625), but predictions were more accurate (Brier score 0.117, 95% CI 0.112 to 0.121; 0.114, 95% CI 0.109 to 0.118). Calibration plots were similar regardless of whether the death was modelled as a competing event or not. Updating the KDRI worsened calibration, but marginally improved discrimination.

CONCLUSIONS

Predictive performance for 1-year graft failure was similar between death-censored and competing event graft failure, but differences appeared when predicting 5-year graft failure. The updated index did not have superior performance and we conclude that updating the KDRI in the present form is not required.

Multiview Clustering to Identify Novel Kidney Donor Phenotypes for Assessing Graft Survival in Older Transplant Recipients

Key Points

An unsupervised machine learning clustering algorithm identified distinct deceased kidney donor phenotypes among older recipients. Recipients of certain donor phenotypes were at a relatively higher risk of all-cause graft loss even after accounting for recipient factors. The use of unsupervised clustering to support kidney allocation systems may be an important area for future study.

Background

Older transplant recipients are at a relatively increased risk of graft failure after transplantation, and some of this risk may relate to donor characteristics. Unsupervised clustering using machine learning may be a novel approach to identify donor phenotypes that may then be used to evaluate outcomes for older recipients. Using a cohort of older recipients, the purpose of this study was to (1 ) use unsupervised clustering to identify donor phenotypes and (2 ) determine the risk of death/graft failure for recipients of each donor phenotype.

Methods

We analyzed a nationally representative cohort of kidney transplant recipients aged 65 years or older captured using the Scientific Registry of Transplant Recipients between 2000 and 2017. Unsupervised clustering was used to generate phenotypes using donor characteristics inclusive of variables in the kidney donor risk index (KDRI). Cluster assignment was internally validated. Outcomes included all-cause graft failure (including mortality) and delayed graft function. Differences in the distribution of KDRI scores were also compared across the clusters. All-cause graft failure was compared for recipients of donor kidneys from each cluster using a multivariable Cox survival analysis.

Results

Overall, 23,558 donors were separated into five clusters. The area under the curve for internal validation of cluster assignment was 0.89. Recipients of donor kidneys from two clusters were found to be at high risk of all-cause graft failure relative to the lowest risk cluster (adjusted hazards ratio, 1.86; 95% confidence interval, 1.69 to 2.05 and 1.73; 95% confidence interval, 1.61 to 1.87). Only one of these high-risk clusters had high proportions of donors with established risk factors (i.e. , hypertension, diabetes). KDRI scores were similar for the highest and lowest risk clusters (1.40 [1.18–1.67] and 1.37 [1.15–1.65], respectively).

Conclusions

Unsupervised clustering can identify novel donor phenotypes comprising established donor characteristics that, in turn, may be associated with different risks of graft loss for older transplant recipients.

A pre-transplantation risk assessment tool for graft survival in Dutch pediatric kidney recipients

Background

A prediction model for graft survival including donor and recipient characteristics could help clinical decision-making and optimize outcomes. The aim of this study was to develop a risk assessment tool for graft survival based on essential pre-transplantation parameters.

Methods

The data originated from the national Dutch registry (NOTR; Nederlandse OrgaanTransplantatie Registratie). A multivariable binary logistic model was used to predict graft survival, corrected for the transplantation era and time after transplantation. Subsequently, a prediction score was calculated from the β-coefficients. For internal validation, derivation (80%) and validation (20%) cohorts were defined. Model performance was assessed with the area under the curve (AUC) of the receiver operating characteristics curve, Hosmer-Lemeshow test and calibration plots.

Results

In total, 1428 transplantations were performed. Ten-year graft survival was 42% for transplantations before 1990, which has improved to the current value of 92%. Over time, significantly more living and pre-emptive transplantations have been performed and overall donor age has increased (P < .05).The prediction model included 71 829 observations of 554 transplantations between 1990 and 2021. Other variables incorporated in the model were recipient age, re-transplantation, number of human leucocyte antigen (HLA) mismatches and cause of kidney failure. The predictive capacity of this model had AUCs of 0.89, 0.79, 0.76 and 0.74 after 1, 5, 10 and 20 years, respectively (P < .01). Calibration plots showed an excellent fit.

Conclusions

This pediatric pre-transplantation risk assessment tool exhibits good performance for predicting graft survival within the Dutch pediatric population. This model might support decision-making regarding donor selection to optimize graft outcomes.

Trial registration

ClinicalTrials.gov Identifier: NCT05388955.

Predicting Time to and Average Quality of Future Offers for Kidney Transplant Candidates Declining a Current Deceased Donor Kidney Offer: A Retrospective Cohort Study

Background

At the time a kidney offer is made by an organ donation organization (ODO), transplant physicians must inform candidates on the pros and cons of accepting or declining the offer. Although physicians have a general idea of expected wait time to kidney transplantation by blood group in their ODO, there are no tools that provide quantitative estimates based on the allocation score used and donor/candidate characteristics. This limits the shared decision-making process at the time of kidney offer as (1) the consequences of declining an offer in terms of wait-time prolongation cannot be provided and (2) the quality of the current offer cannot be compared with that of offers that could be made to the specific candidate in the future. This is especially relevant to older transplant candidates as many ODOs use some form of utility matching in their allocation score.

Objective

We aimed to develop a novel method to provide personalized estimates of wait time to next offer and quality of future offers for kidney transplant candidates if they refused a current deceased donor offer from an ODO.

Design

A retrospective cohort study.

Setting

Administrative data from Transplant Quebec.

Patients

All patients who were actively registered on the kidney transplant wait list at any point between March 29, 2012 and December 13, 2017.

Measurements

The time to next offer was defined as the number of days between the time of the current offer and the next offer if the current one were declined. The quality of the offers was measured with the 10-variable Kidney Donor Risk Index (KDRI) equation.

Methods

Candidate-specific kidney offer arrival was modeled with a marked Poisson process. To derive the lambda parameter for the marked Poisson process for each candidate, the arrival of donors was examined in the 2 years prior to the time of the current offer. The Transplant Quebec allocation score was calculated for each ABO-compatible offer with the characteristics that the candidate presented at the time of the current offer. Offers where the candidate's score was lower than the scores of actual recipients of the second kidneys transplanted were filtered out from the candidate-specific kidney offer arrival. The KDRIs of offers that remained were averaged to provide an estimate of the quality of future offers, to be compared with that of the current offer.

Results

During the study period, there were 848 unique donors and 1696 transplant candidates actively registered. The models provide the following information: average time to next offer, time to which there is a 95% probability of receiving a next offer, average KDRI of future offers. The C-index of the model was 0.72. When compared with providing average group estimates of wait time and KDRI of future offers, the model reduced the root-mean-square error in the predicted time to next offer from 137 to 84 days and that of predicted KDRI of future offers from 0.64 to 0.55. The precision of the model's predictions was higher when observed times to next offer were 5 months or less.

Limitations

The models assume that patients declining an offer remain wait-listed until the next one. The model only updates wait time every year after the time of an offer and not in a continuous fashion.

Conclusion

By providing personalized quantitative estimates of time to and quality of future offers, our new approach can inform the shared decision-making process between transplant candidates and physicians when a kidney offer from a deceased donor is made by an ODO.

Kidney Donor Profile Index and allograft outcomes: interactive effects of estimated post-transplant survival score and ischaemic time

Background

The Kidney Donor Profile Index (KDPI) is routinely reported by the donation agencies in Australia. We determined the association between KDPI and short-term allograft loss and assessed if this association was modified by the estimated post-transplant survival (EPTS) score and total ischaemic time.

Methods

Using data from the Australia and New Zealand Dialysis and Transplant Registry, the association between KDPI (in quartiles) and 3-year overall allograft loss was examined using adjusted Cox regression analysis. The interactive effects between KDPI, EPTS score and total ischaemic time on allograft loss were assessed.

Results

Of 4006 deceased donor kidney transplant recipients transplanted between 2010 and 2015, 451 (11%) recipients experienced allograft loss within 3 years post-transplant. Compared with recipients of kidneys with a KDPI of 0-25%, recipients who received donor kidneys with a KDPI >75% experienced a 2-fold increased risk of 3-year allograft loss {adjusted hazard ratio [HR] 2.04 [95% confidence interval (CI) 1.53-2.71]}. The adjusted HRs for kidneys with a KDPI of 26-50% and 51-75% were 1.27 (95% CI 0.94-1.71) and 1.31 (95% CI 0.96-1.77), respectively. There were significant interactions between KDPI and EPTS scores (P-value for interaction <.01) and total ischaemic time (P-value for interaction <.01) such that the associations between higher KDPI quartiles and 3-year allograft loss were strongest in recipients with the lowest EPTS scores and longest total ischaemic time.

Conclusion

Recipients with higher post-transplant expected survival and transplants with longer total ischaemia who received donor allografts with higher KDPI scores experienced a greater risk of short-term allograft loss compared with those recipients with reduced post-transplant expected survival and with shorter total ischemia.

Using Information Available at the Time of Donor Offer to Predict Kidney Transplant Survival Outcomes: A Systematic Review of Prediction Models

Statistical models that can predict graft and patient survival outcomes following kidney transplantation could be of great clinical utility. We sought to appraise existing clinical prediction models for kidney transplant survival outcomes that could guide kidney donor acceptance decision-making. We searched for clinical prediction models for survival outcomes in adult recipients with single kidney-only transplants. Models that require information anticipated to become available only after the time of transplantation were excluded as, by that time, the kidney donor acceptance decision would have already been made. The outcomes of interest were all-cause and death-censored graft failure, and death. We summarised the methodological characteristics of the prediction models, predictive performance and risk of bias. We retrieved 4,026 citations from which 23 articles describing 74 models met the inclusion criteria. Discrimination was moderate for all-cause graft failure (C-statistic: 0.570–0.652; Harrell’s C: 0.580–0.660; AUC: 0.530–0.742), death-censored graft failure (C-statistic: 0.540–0.660; Harrell’s C: 0.590–0.700; AUC: 0.450–0.810) and death (C-statistic: 0.637–0.770; Harrell’s C: 0.570–0.735). Calibration was seldom reported. Risk of bias was high in 49 of the 74 models, primarily due to methods for handling missing data. The currently available prediction models using pre-transplantation information show moderate discrimination and varied calibration. Further model development is needed to improve predictions for the purpose of clinical decision-making.

Systematic Review Registration:https://osf.io/c3ehp/l.

Organ donor management goals and delayed graft function in adult kidney transplant recipients

BACKGROUND

Delayed graft function (DGF) after kidney transplantation is a common occurrence and correlates with poor graft and patient outcomes. Donor characteristics and care are known to impact DGF. We attempted to show the relationship between achievement of specific donor management goals (DMG) and DGF.

METHODS

This is a retrospective case-control study using data from 14 046 adult kidney donations after brain death from hospitals in 18 organ procurement organizations (OPOs) which were transplanted to adult recipients between 2012 and 2018. Data on DMG compliance and donor, recipient, and ischemia-related factors were used to create multivariable logistic regression models.

RESULTS

The overall rate of DGF was 29.4%. Meeting DMGs for urine output and vasopressor use were associated with decreased risk of DGF. Sensitivity analyses performed with different imputation methods, omitting recipient factors, and analyzing multiple time points yielded largely consistent results.

CONCLUSIONS

The development of DMGs continues to show promise in improving outcomes in the kidney transplant recipient population. Studies have already shown increased kidney utilization in smaller cohorts, as well as other organs, and shown decreased rates of DGF. Additional research and analysis are required to assess interactions between meeting DMGs and correlation versus causality in DMGs and DGF.

Kidney Transplantation in Times of Covid-19: Decision Analysis in the Canadian Context

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic impacted transplant programs across Canada.

OBJECTIVE

We evaluated the implications of delays in transplantation among Canadian end-stage kidney disease (ESKD) patients to allow pretransplant vaccination.

DESIGN

We used a Markov microsimulation model and ESKD patient perspective to study the effectiveness (quality-adjusted life years [QALY]) of living (LD) or deceased donor (DD) kidney transplantation followed by 2-dose SARS-CoV-2 vaccine versus delay in LD ("Delay LD") or refusal of DD offer ("Delay DD") to receive 2-dose SARS-CoV-2 vaccine pretransplant.

SETTING

Canadian dialysis and transplant centers.

PATIENTS

We simulated a 10 000-waitlisted ESKD patient cohort, which was predictively modeled for a lifetime horizon in monthly cycles.

MEASUREMENTS

Inputs on patient and graft survival estimates by patient, LD or DD characteristics, were extracted from the Treatment of End-Stage Organ Failure in Canada, Canadian Organ Replacement Register, 2009 to 2018. In addition, a literature review provided inputs on quality of life, SARS-CoV-2 transmissibility, new variants of concern, mortality risk, and antibody responses to 2-dose SARS-CoV-2 mRNA vaccines.

METHODS

We conducted base case, scenario, and sensitivity analyses to illustrate the impact of patient, donor, vaccine, and pandemic characteristics on the preferred strategy.

RESULTS

In the average waitlisted Canadian patient, receiving 2-dose SARS-CoV-2 vaccine post-transplant provided an effectiveness of 22.32 (95% confidence interval: 22.00-22.7) for LD and 19.34 (19.02-19.67) QALYs for DD. Delaying transplants for 6 months to allow 2-dose SARS-CoV-2 vaccine before LD and DD transplant yielded effectiveness of 22.83 (21.51-23.14) and 20.65 (20.33-20.96) QALYs, respectively. Scenario analysis suggested a benefit to short delays in DD transplants to receive 2-dose SARS-CoV-2 vaccine in waitlisted patients ≥55 years. Two-way sensitivity analysis suggested decreased effectiveness of the strategy prioritizing 2-dose SARS-CoV-2 vaccine prior to DD transplant the longer the delay and the higher the Kidney Donor Risk Index of the eventual DD transplant. When assessing the impact of SARS-CoV-2 variants of concern (infection rates ≥10-fold and associated mortality ≥3-fold vs base case), we found short delays to allow 2-dose SARS-CoV-2 vaccine administration pretransplant to be preferable.

LIMITATIONS

Risks associated with nosocomial exposure of LDs were not considered. There was uncertainty regarding input parameters related to SARS-CoV-2 infection, new variants, and COVID-19 severity in ESKD patients. Given rollout of population-level SARS-CoV-2 vaccination, we assumed a linear decrease in infection rates over 1 year. Proportions of patients mounting an antibody response to 2-dose SARS-CoV-2 mRNA vaccines were considered in lieu of data on vaccine efficacy in dialysis and following transplantation. Non-age-stratified annual mortality rates were used for waitlisted candidates.

CONCLUSIONS

Our analyses suggest that short delays allowing pretransplant vaccination offered comparable to greater effectiveness than pursuing transplantation without delay, proposing transplant candidates should be prioritized to receive at least 2 doses of SARS-CoV-2 vaccine. Our scenario and sensitivity analyses suggest that caution must be exercised when declining DD offers in patients offered low risk DD and who are likely to incur significant delays in access to transplantation. While population-level herd immunity may decrease infection risk in transplant patients, more data are required on vaccine efficacy against SARS-CoV-2 and variants of concern in ESKD, and how efficacy may be modified by a third vaccine dose, maintenance immunosuppression and timing of induction and rejection therapies.

Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study

BACKGROUND

Kidney transplantation is the optimal treatment for patients with end-stage renal disease. Short- and long-term kidney graft survival is influenced by a number of donor and recipient factors. Predicting the success of kidney transplantation is important for optimizing kidney allocation.

OBJECTIVE

The aim of this study was to predict the risk of kidney graft failure across three temporal cohorts (within 1 year, within 5 years, and after 5 years following a transplant) based on donor and recipient characteristics. We analyzed a large data set comprising over 50,000 kidney transplants covering an approximate 20-year period.

METHODS

We applied machine learning-based classification algorithms to develop prediction models for the risk of graft failure for three different temporal cohorts. Deep learning-based autoencoders were applied for data dimensionality reduction, which improved the prediction performance. The influence of features on graft survival for each cohort was studied by investigating a new nonoverlapping patient stratification approach.

RESULTS

Our models predicted graft survival with area under the curve scores of 82% within 1 year, 69% within 5 years, and 81% within 17 years. The feature importance analysis elucidated the varying influence of clinical features on graft survival across the three different temporal cohorts.

CONCLUSIONS

In this study, we applied machine learning to develop risk prediction models for graft failure that demonstrated a high level of prediction performance. Acknowledging that these models performed better than those reported in the literature for existing risk prediction tools, future studies will focus on how best to incorporate these prediction models into clinical care algorithms to optimize the long-term health of kidney recipients.

Predicting Clinical Outcome in Expanded Criteria Donor Kidney Transplantation: A Retrospective Cohort Study

Background:

The gaps in organ supply and demand necessitate the use of expanded criteria donor (ECD) kidneys.

Objective:

To identify which pre-transplant and post-transplant predictors are most informative regarding short- and long-term ECD transplant outcomes.

Design:

Retrospective cohort study.

Setting:

Single center, Quebec, Canada.

Patients:

The patients were 163 consecutive first-time ECD kidney only transplant recipients who underwent transplantation at McGill University Health Centre (MUHC) between January 1, 2008 and December 31, 2014 and had frozen section wedge procurement biopsies.

Measurements:

Short-term graft outcomes, including delayed graft function and 1-year estimated glomerular filtration rate (eGFR), as well as long-term outcomes including all-cause graft loss (defined as return to dialysis, retransplantation, and death with function).

Methods:

Pre-transplant donor, recipient, and transplant characteristics were assessed as predictors of transplant outcomes. The added value of post-transplant predictors, including longitudinal eGFR, was also assessed using time-varying Cox proportional hazards models.

Results:

In univariate analyses, among the pre-transplant donor characteristics, histopathologic variables did not show evidence of association with delayed graft function, 1-year post-transplant eGFR or all cause graft loss. Recipient age was associated with all-cause graft loss (hazard ratio: 1.038 [95% confidence interval: 1.002-1.075] and the model produced only modest discrimination (C-index: 0.590; standard error [SE]: 0.045). Inclusion of time-dependent post-transplant eGFR improved the model’s prediction accuracy (C-index: 0.711; SE = 0.047). Pre-transplant ECD characteristics were not associated with long-term survival, whereas post-transplant characteristics allowed better model discrimination.

Limitations:

Single-center study, small sample size, and potential incomplete capture of all covariate data.

Conclusions:

Incorporation of dynamic prediction models into electronic health records may enable timely mitigation of ECD graft failure risk and/or facilitate planning for renal replacement therapies. Histopathologic findings on preimplantation biopsies have a limited role in predicting long-term ECD outcomes.

Trial registration:

Not applicable.

Non-standard criteria donors in pediatric kidney transplantation

KT remains the treatment of choice for ESRD in children. However, the demand for kidney transplants continues to outstrip supply, even in the pediatric scenario. We reviewed the applicability of nonSCDs for pediatric KT. There is a lack of studies analyzing this modality among pediatric donors and recipients, where most conclusions are based on predictions from adult data. Nevertheless, marginal donors might be a reasonable option in selected cases. For example, the use of older LDs is an acceptable option, with outcomes comparable to SCDs. Organs donated after cardiac death represent another possibility, albeit with logistic, ethical, and legal limitations in some countries. AKI donors also constitute an option in special situations, although there are no pediatric data on these transplants. Likewise, there are no data on the use of expanded criteria donors in pediatric patients, but this appears not to be a good option, considering the compromised long-term survival.