Who can tolerate a marginal kidney? Predicting survival after deceased donor kidney transplant by donor-recipient combination

Decreasing efficiency in deceased donor kidney offer notifications under the new distance-based kidney allocation system

Organ Procurement Organizations (OPOs) recover deceased donor kidneys and place them with matched recipients according to ranked match runs of patients, but offer notification practices differ across the OPOs and have changed following updates to allocation policy (kidney allocation system 250 [KAS250]). This national registry study used batch notification data to quantify time spent on kidney allocation and identify variations in batch notification practices across OPOs before and after the KAS250 allocation system era. Overall allocation time between the first and last offer notifications increased from a median of 1 to 7 hours under the KAS250 allocation system. For match runs of unplaced kidneys, allocation time increased from a median of 18 to 28 hours. Out-of-sequence (OOS) allocation, used by OPOs to limit nonutilization due to excess cold ischemia time, more than doubled in frequency between 2018 and 2022, with median time from first offer to initiation of OOS varying across OPOs from 0 to 47 hours. Increasing rates of organ nonutilization and the observed allocation practice differences based on organ quality demonstrate the urgent need for new approaches to achieve more efficient placement of hard-to-place kidneys. Data-driven approaches to optimize kidney allocation efforts will help ensure fairness in a system that currently allows for wide practice variation and frequent OOS allocation.

Using Unsupervised Clustering to Characterize Phenotypes Among Older Kidney Transplant Recipients: A Cohort Study

Background

Older kidney transplant recipients have inferior outcomes compared to younger recipients, and this risk may be compounded by donor characteristics.

Objective

We applied an unsupervised machine learning clustering approach to group older recipients into similar phenotypes. We evaluated the association between each cluster and graft failure, and the impact of donor quality on outcomes.

Design

This is a nationally representative retrospective cohort study.

Setting and Patients

Kidney transplant recipients aged ≥65 years identified from the Scientific Registry of Transplant Recipients (2000-2017).

Measurements and Methods

We used unsupervised clustering to generate phenotypes using 16 recipient factors. Donor quality was evaluated using 2 approaches, including the Kidney Donor Risk Index (KDRI). All-cause graft failure was analyzed using multivariable Cox regression.

Results

Overall, 16 364 patients (mean age 69 years; 38% female) were separated into 3 clusters. Cluster 1 recipients were exclusively female; cluster 2 recipients were exclusively males without diabetes; and cluster 3 recipients were males with a higher burden of comorbidities. Compared to cluster 2, the risk of graft failure was higher for cluster 3 recipients (adjusted hazard ratio [aHR] = 1.25, 95% confidence interval [CI] = 1.19-1.32). Cluster 3 recipients of a lower quality (KDRI ≥1.45) kidney had the highest risk of graft failure (aHR = 1.74, 95% CI = 1.61-1.87) relative to cluster 2 recipients of a higher quality kidney.

Limitations

This study did not include an external validation cohort. The findings should be interpreted as exploratory and should not be used to inform individual risk prediction nor be applied to recipients <65 years of age.

Conclusions

In a national cohort of older kidney transplant recipients, unsupervised clustering generated 3 clinically distinct recipient phenotypes. These phenotypes may aid in complementing allocation decisions, providing prognostic information, and optimizing post-transplant care for older recipients.

The dynamics of deceased donor kidney transplant decision making: insights from studying individual clinicians' offer decisions

Despite the high demand, >7500 recovered kidneys annually go unused, with transplant centers showing significant variation in their offer acceptance practices. However, it remains unclear how much of this variation occurs between individual clinicians within the same center and its impact on allocation efficiency and equity. This study quantified the variability in kidney offer acceptance decisions attributable to clinicians vs centers and examined the role of donor quality in acceptance decisions. We analyzed national transplant registry data (from January 2016 to December 2020) linked to on-call records from 15 transplant centers, creating a clinician-level data set with 344 678 deceased donor kidney offers. The primary outcome was the variability in offer acceptance attributable to clinicians vs centers, quantified via hierarchical, mixed-effect logistic regression models. To complement kidney donor profile index as a measure of donor quality, we incorporated expected acceptance probability, adjusting for a broader set of donor characteristics and recipient factors. Both center-level (0.35; 95% CI: 0.15-0.79) and clinician-level (0.10; 95% CI: 0.06-0.18) variances were significant, with heterogeneity in the kidney donor profile index-acceptance association among clinicians. These results underscore the need for further research into the mechanisms driving the clinician-level variation and its implications for organ allocation efficacy, equity, and patient outcomes.

Artificial Intelligence in Nephrology: Clinical Applications and Challenges

Artificial intelligence (AI) is increasingly used in many medical specialties. However, nephrology has lagged in adopting and incorporating machine learning techniques. Nephrology is well positioned to capitalize on the benefits of AI. The abundance of structured clinical data, combined with the mathematical nature of this specialty, makes it an attractive option for AI applications. AI can also play a significant role in addressing health inequities, especially in organ transplantation. It has also been used to detect rare diseases such as Fabry disease early. This review article aims to increase awareness on the basic concepts in machine learning and discuss AI applications in nephrology. It also addresses the challenges in integrating AI into clinical practice and the need for creating an AI-competent nephrology workforce. Even though AI will not replace nephrologists, those who are able to incorporate AI into their practice effectively will undoubtedly provide better care to their patients. The integration of AI technology is no longer just an option but a necessity for staying ahead in the field of nephrology. Finally, AI can contribute as a force multiplier in transitioning to a value-based care model.

Impact of kidney allocation system 250 policy on 1-year graft loss

A new deceased donor kidney allocation system (KAS250) was implemented in March 2021 that prioritizes recipients within a 250-nautical mile radius of the donor hospital. KAS250 was implemented to reduce geographic disparities in access to kidney transplantation. Studies have shown an increase in cold ischemia time (CIT) after KAS250 implementation but the impact on graft outcomes is unknown. Utilizing data from the Scientific Registry of Transplant Recipients, we estimated cause-specific hazards of 1-year death-censored graft loss (DCGL) and all-cause graft loss (ACGL) due to KAS250 for the post-KAS250 period (April 2021 to December 2022; N = 28 584) compared to the pre-KAS250 period (January 2017 to December 2018; N = 23 798). We found that the post-KAS250 period had higher DCGL (hazard ratio 1.14; 95% CI 1.02-1.26; P = 0.0187) and ACGL (hazard ratio 1.22; 95% CI 1.13-1.31, P < .0001). Mediation analysis showed that CIT indirectly mediated 45.54% and 15.73% of KAS250 policy's effect on DCGL and ACGL, respectively. In conclusion, short-term graft outcomes in the post-KAS250 era are inferior to those in the pre-KAS250 era, with the worsening CIT being a significant contributor. Therefore, further adjustments to both the policy and transplant practices should be considered to further optimize equity and outcomes.

Educational interventions improve disparities in patient access to kidney transplantation: a network meta-analysis of randomized controlled trials

BACKGROUND

Transplantation significantly improves the quality of life for patients with chronic kidney disease. Despite various educational strategies being assessed, the optimal approach to overcome barriers to kidney transplantation remains unclear.

MATERIALS AND METHODS

The authors conducted a systematic review and network meta-analysis (NMA) of randomized controlled trials (RCTs) comparing educational interventions to improve kidney transplantation access. The authors searched Medline, Embase, Cochrane Central, and Clinicaltrials.gov up until June 2024. Outcomes included rate of transplantation, living donor inquiries, waitlisting, evaluation, and knowledge level. Frequentist random-effects models and p-scores were used to rank strategies. The protocol was registered in PROSPERO.

RESULTS

The authors included 24 RCTs with a total of 116 054 patients. Of these, 57 996 (49.97%) received educational interventions and 58 058 (50.03%) received standard-care. Educator-guided and home-based strategies were associated with a higher rate of transplantation to multilevel interventions (RR 1.63; 95% CI: 1.07-2.48; P=0.023 | RR 1.85; 95% CI: 1.11-3.08; P=0.019) and standard-care (RR 1.56; 95% CI: 1.00-2.45; P=0.049 | RR 1.78; 95% CI: 1.17-2.70; P=0.007). According to the P-scores ranking, home-based interventions were the most likely strategy to improve transplantation access.

CONCLUSION

In this NMA of 24 RCTs, home-based and educator-guided interventions were the most beneficial for improving access to kidney transplantation. Future studies should focus on their applicability for minority populations with challenges in health literacy and transplant access.

Push toward pre-emptive kidney transplantation - for sure?

Pre-emptive kidney transplantation (PKT) has long been considered the optimal treatment for patients with end-stage chronic kidney disease (CKD) seeking the most favourable long-term outcomes. However, the significant growth in transplant procedures over recent decades has led to a notable increase in wait-listed patients and a disproportionate demand for donor organs. This situation necessitates a re-evaluation of transplantation timing and the establishment of rational indications from both societal and clinical perspectives. An increasing number of retrospective analyses have challenged the universal benefit of PKT, suggesting that premature indications for living or deceased donor PKT may not always yield superior hard outcomes compared with non-PKT approaches. Conventional predictive models have shown limitations in accurately assessing risks for certain subpopulations, potentially leading to significant disparities among wait-listed patients. To address these challenges, we propose the following considerations. Prediction models should not only optimize the distribution of our limited donor resources, but should also illuminate foreseeable risks associated with a potentially 'unsuccessful' PKT. Therefore, this article seeks to underscore the necessity for further discourse on the smouldering concept of when and for whom living or deceased donor PKT should be considered. Is it universally beneficial, or should the clinical paradigm be re-evaluated? In the endeavour to attain superior post-PKT survival outcomes compared with non-PKT or conservative treatment, it seems critical to acknowledge that other treatments may provide more favourable results for certain individuals. This introduces the intricate task of effectively navigating the complexities associated with 'too early' or 'unsuccessful' PKT.

Adjusted Donor Age: A Clinical Score to Support Organ Acceptance Decisions in Deceased-Donor Kidney Transplantation

As transplant programmes have evolved to allow a wider donor pool, organ acceptance decisions have become increasingly complex and lack transparency and equality. Clinical scoring tools exist but there is limited consensus on their use. From a prospective observation of consecutive deceased-donor kidney offers in a large urban transplant centre, a simple score was developed based on donor age and other risk characteristics, excluding ischemia time and graft histology. The score was validated in subsequent cohorts of consecutive offers in the United Kingdom and Germany. In the development cohort of 389 kidney offers, 110 (28%) were transplanted and 175 (45%) declined. Nine risk factors were incorporated into a score based on age, but adjusted for the number of risk factors present, making an "adjusted donor age," with offers separated into equal quintiles by decade. The score was validated in a UK cohort of 380 subsequent offers, and a German cohort of 431 offers. In both cohorts adjusted donor age discriminated between favourable and poor post-transplant outcomes (C-statistic 0.77 in the United Kingdom, 95% CI 0.65-0.88, and 0.71 in Germany, 95% CI 0.64-0.77). Adjusted donor age is a simple score quantifying deceased donor kidney quality, which is consistent with current practice and predicts post-transplant outcome.

Present and Future Applications of Artificial Intelligence in Kidney Transplantation

Artificial intelligence (AI) has a wide and increasing range of applications across various sectors. In medicine, AI has already made an impact in numerous fields, rapidly transforming healthcare delivery through its growing applications in diagnosis, treatment and overall patient care. Equally, AI is swiftly and essentially transforming the landscape of kidney transplantation (KT), offering innovative solutions for longstanding problems that have eluded resolution through traditional approaches outside its spectrum. The purpose of this review is to explore the present and future applications of artificial intelligence in KT, with a focus on pre-transplant evaluation, surgical assistance, outcomes and post-transplant care. We discuss its great potential and the inevitable limitations that accompany these technologies. We conclude that by fostering collaboration between AI technologies and medical practitioners, we can pave the way for a future where advanced, personalised care becomes the standard in KT and beyond.

Association of Race and Ethnicity With High Longevity Deceased Donor Kidney Transplantation Under the US Kidney Allocation System

RATIONALE & OBJECTIVE

The US Kidney Allocation System (KAS) prioritizes candidates with a≤20% estimated posttransplant survival (EPTS) to receive high-longevity kidneys defined by a≤20% Kidney Donor Profile Index (KDPI). Use of EPTS in the KAS deprioritizes candidates with older age, diabetes, and longer dialysis durations. We assessed whether this use also disadvantages race and ethnicity minority candidates, who are younger but more likely to have diabetes and longer durations of kidney failure requiring dialysis.

STUDY DESIGN

Observational cohort study.

SETTING & PARTICIPANTS

Adult candidates for and recipients of kidney transplantation represented in the Scientific Registry of Transplant Recipients from January 2015 through December 2020.

EXPOSURE

Race and ethnicity.

OUTCOME

Age-adjusted assignment to≤20% EPTS, transplantation of a≤20% KDPI kidney, and posttransplant survival in longevity-matched recipients by race and ethnicity.

ANALYTIC APPROACH

Multivariable logistic regression, Fine-Gray competing risks survival analysis, and Kaplan-Meier and Cox proportional hazards methods.

RESULTS

The cohort included 199,444 candidates (7% Asian, 29% Black, 19% Hispanic or Latino, and 43% White) listed for deceased donor kidney transplantation. Non-White candidates had significantly higher rates of diabetes, longer dialysis duration, and were younger than White candidates. Adjusted for age, Asian, Black, and Hispanic or Latino candidates had significantly lower odds of having a ETPS score of≤20% (odds ratio, 0.86 [95% CI, 0.81-0.91], 0.52 [95% CI, 0.50-0.54], and 0.49 [95% CI, 0.47-0.51]), and were less likely to receive a≤20% KDPI kidney (sub-hazard ratio, 0.70 [0.66-0.75], 0.89 [0.87-0.92], and 0.73 [0.71-0.76]) compared with White candidates. Among recipients with≤20% EPTS scores transplanted with a≤20% KDPI deceased donor kidney, Asian and Hispanic recipients had lower posttransplant mortality (HR, 0.45 [0.27-0.77] and 0.63 [0.47-0.86], respectively) and Black recipients had higher but not statistically significant posttransplant mortality (HR, 1.22 [0.99-1.52]) compared with White recipients.

LIMITATIONS

Provider reported race and ethnicity data and 5-year post transplant follow-up period.

CONCLUSIONS

The US kidney allocation system is less likely to identify race and ethnicity minority candidates as having a≤20% EPTS score, which triggers allocation of high-longevity deceased donor kidneys. These findings should inform the Organ Procurement and Transplant Network about how to remedy the race and ethnicity disparities introduced through KAS's current approach of allocating allografts with longer predicted longevity to recipients with longer estimated posttransplant survival.

PLAIN-LANGUAGE SUMMARY

The US Kidney Allocation System prioritizes giving high-longevity, high-quality kidneys to patients on the waiting list who have a high estimated posttransplant survival (EPTS) score. EPTS is calculated based on the patient's age, whether the patient has diabetes, whether the patient has a history of organ transplantation, and the number of years spent on dialysis. Our analyses show that Asian, Black or African American, and Hispanic or Latino patients were less likely to receive high-longevity kidneys compared with White patients, despite having similar or better posttransplant survival outcomes.

Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor-Recipient Matching

(1) Background: Globally, the kidney donor shortage has made the allocation process critical for patients awaiting a kidney transplant. Adopting Machine Learning (ML) models for donor-recipient matching can potentially improve kidney allocation processes when compared with traditional points-based systems. (2) Methods: This study developed an ML-based approach for donor-recipient matching. A comprehensive evaluation was conducted using ten widely used classifiers (logistic regression, decision tree, random forest, support vector machine, gradient boosting, boost, CatBoost, LightGBM, naive Bayes, and neural networks) across three experimental scenarios to ensure a robust approach. The first scenario used the original dataset, the second used a merged version of the dataset, and the last scenario used a hierarchical architecture model. Additionally, a custom ranking algorithm was designed to identify the most suitable recipients. Finally, the ML-based donor-recipient matching model was integrated into a web-based platform called Nephron. (3) Results: The gradient boost model was the top performer, achieving a remarkable and consistent accuracy rate of 98% across the three experimental scenarios. Furthermore, the custom ranking algorithm outperformed the conventional cosine and Jaccard similarity methods in identifying the most suitable recipients. Importantly, the platform not only facilitated efficient patient selection and prioritisation for kidney allocation but can be flexibly adapted for other solid organ allocation systems built on similar criteria. (4) Conclusions: This study proposes an ML-based approach to optimize donor-recipient matching within the kidney allocation process. Successful implementation of this methodology demonstrates significant potential to enhance both efficiency and fairness in kidney transplantation.

Treatment of chronic kidney disease in older populations

As the world population ages, an expected increase in the prevalence of chronic kidney disease (CKD) among older individuals will pose a considerable challenge for health care systems in terms of resource allocation for disease management. Treatment strategies for older patients with CKD should ideally align with those applied to the general population, focusing on minimizing cardiovascular events and reducing the risk of progression to kidney failure. Emerging therapies, such as SGLT-2 inhibitors and GLP-1 receptor agonists, hold promise for the effective management of CKD in older individuals. In addition, non-pharmacological interventions such as nutritional and exercise therapies have a crucial role. These interventions enhance the effects of pharmacotherapy and, importantly, contribute to the maintenance of cognitive function and overall quality of life. Various factors beyond age and cognitive function must be taken into account when considering kidney replacement therapy for patients with kidney failure. Importantly, all treatment options, including dialysis, transplantation and conservative management approaches, should be tailored to the individual through patient-centred decision-making. The dynamic integration of digital technologies into medical practice has the potential to transform the management of CKD in the aging population.

Differential effects of donor factors on post-transplant survival in lung transplantation

Background

Predicting post-transplant (PT) survival in lung allocation remains an elusive goal. We analyzed the impact of donor factors on PT survival and how these relationships vary among transplant recipients.

Methods

We studied primary bilateral lung transplant recipients (n = 7,609) from the US Scientific Registry of Transplant Recipients (19 February 2015-1 February 2020). Main and interaction effects were evaluated and adjusted across candidate age, sex, and diagnosis. Models predicting PT survival were compared to the PT Composite Allocation Score model (PT-CAS): (1) Cox regression donor multivariable model (COX), (2) COX + PT-CAS, (3) random forest model (RF), and (4) RF + PT-CAS. Model discrimination and calibration measures were compared.

Results

Interactions between donor and recipient factors emerged by age: lower survival for donation after circulatory death organs for recipients aged 55 to 69 years, donor smoking for recipients aged 30 to 54 and 70+, Hispanic donor for recipients <30, non-Hispanic Black donor for recipients aged 30+; sex: cytomegalovirus mismatch for males; diagnosis: higher donor recipient weight ratio for diagnosis group C (e.g., cystic fibrosis), donor diabetes for diagnosis group D (e.g., idiopathic pulmonary fibrosis). COX and RF models performed similarly to PT-CAS; however, the combined COX + PT-CAS model had improved discrimination (1-year area under the receiver operator characteristic curve [AUC] PT-CAS 0.609 vs 1-year AUC COX + PT-CAS 0.626) and improved calibration across a broader range of predicted risk.

Conclusions

The influence of donor factors on recipient PT survival differed by age, sex, and diagnosis. The addition of donor factors to existing models predicting PT survival led to only modest improvement in prediction accuracy. Future efforts may focus on optimizing matching strategies to improve donor utilization.

Preoperative Risk Assessment of Early Kidney Graft Loss

Background

A large proportion of potential organ donors are not utilized for kidney transplantation out of risk of early allograft loss because of donor-related characteristics. These can be summarized using kidney donor profile index (KDPI). Because KDPI affects the choice of the recipient, the predictive ability of KDPI is tied to recipient attributes. These have been questioned to explain most of the predictive ability of KDPI. This study aims to quantify the effect of the donor on early graft loss (EGL) by accounting for nonrandom allocation.

Methods

This study included patients undergoing kidney transplantation from deceased donors between 2014 and 2020 from the Scientific Registry of Transplantation Recipients. EGL, defined as a return to dialysis or retransplantation during the first posttransplant year, was the primary endpoint. Nonrandom allocation and donor-recipient matching by KDPI necessitated the use of inverse probability treatment weighting, which served to assess the effect of KDPI and mitigate selection bias in a weighted Cox regression model.

Results

The study comprised 89 290 transplantations in 88 720 individual patients. Inverse probability treatment weighting resulted in a good balance of recipient covariates across values of continuous KDPI. Weighted analysis showed KDPI to be a significant predictor for short-term outcomes. A comparable (in terms of age, time on dialysis, previous transplants, gender, diabetes status, computed panel-reactive antibodies, and HLA mismatches) average recipient, receiving a kidney from a donor with KDPI 40-60 had a 3.5% risk of EGL increased to a risk of 7.5% if received a kidney from a KDPI >95 donor (hazard ratio, 2.3; 95% confidence interval, 1.9-2.7). However, for all-cause survival KDPI was less influential.

Conclusions

The predictive ability of KDPI does not stem from recipient confounding alone. In this large sample-sized study, modeling methods accounting for nonindependence of recipient selection verify graft quality to effectively predict short-term transplantation outcomes.

Expanding Access to High KDPI Kidney Transplant for Recipients Aged 60 y and Older: Cost Utility and Survival

Background

Modern organ allocation systems are tasked with equitably maximizing the utility of transplanted organs. Increasing the use of deceased donor organs at risk of discard may be a cost-effective strategy to improve overall transplant benefit. We determined the survival implications and cost utility of increasing the use of marginal kidneys in an older adult Canadian population of patients with end-stage kidney disease.

Methods

We constructed a cost-utility model with microsimulation from the perspective of the Canadian single-payer health system for incident transplant waitlisted patients aged 60 y and older. A kidney donor profile index score of ≥86 was considered a marginal kidney. Donor- and recipient-level characteristics encompassed in the kidney donor profile index and estimated posttransplant survival scores were used to derive survival posttransplant. Patients were followed up for 10 y from the date of waitlist initiation. Our analysis compared the routine use of marginal kidneys (marginal kidney scenario) with the current practice of limited use (status quo scenario).

Results

The 10-y mean cost and quality-adjusted life-years per patient in the marginal kidney scenario were estimated at $379 485.33 (SD: $156 872.49) and 4.77 (SD: 1.87). In the status quo scenario, the mean cost and quality-adjusted life-years per patient were $402 937.68 (SD: $168 508.85) and 4.37 (SD: 1.87); thus, the intervention was considered dominant. At 10 y, 62.8% and 57.0% of the respective cohorts in the marginal kidney and status quo scenarios remained alive.

Conclusions

Increasing the use of marginal kidneys in patients with end-stage kidney disease aged 60 y and older may offer cost savings, improved quality of life, and greater patient survival in comparison with usual care.

Organ Utilization Rates from Non-Ideal Donors for Solid Organ Transplant in the United States

Background: Non-ideal donors provide acceptable allografts and may expand the donor pool. This study evaluates donor utilization across solid organs over 15-years in the United States. Methods: We analyzed the OPTN STAR database to identify potential donors across three donor eras: 2005-2009, 2010-2014, and 2015-2019. Donors were analyzed by a composite Donor Utilization Score (DUS), comprised of donor age and comorbidities. Outcomes of interest were overall and organ-specific donor utilization. Descriptive analyses and multivariable logistic regression modeling were performed. p-values < 0.01 considered significant. Results: Of 132,465 donors, 32,710 (24.7%) were identified as non-ideal donors (NID), based on a DUS ≥ 3. Compared to ideal donors (ID), NID were older (median 56 years, IQR 51-64 years vs. 35 years, 22-48 years, p < 0.001) and more frequently female (44.3% vs. 39.1%, p < 0.001), Black (22.1% vs. 14.6%, p < 0.001) and obese (60.7% vs. 19.6%, p < 0.001). The likelihood of overall DBD utilization from NID increased from Era 1 to Era 2 (OR 1.227, 95% CI 1.123-1.341, p < 0.001) and Era 3 (OR 1.504, 1.376-1.643, p < 0.001), while DCD donor utilization in NID was not statistically different across Eras. Compared to Era 1, the likelihood of DBD utilization from NID for kidney transplantation was lower in Era 2 (OR 0.882, 0.822-0.946) and Era 3 (OR 0.938, 0.876-1.004, p = 0.002). The likelihood of NID utilization increased in Era 3 compared to Era 1 for livers (OR 1.511, 1.411-1.618, p < 0.001), hearts (OR 1.623, 1.415-1.862, p < 0.001), and lungs (OR 2.251, 2.011-2.520, p < 0.001). Conclusions: Using a universal definition of NID across organs, NID donor utilization is increasing; however, use of DUS may improve resource utilization in identifying donors at highest likelihood for multi-organ donation.

Interaction between cold ischemia time and Kidney Donor Profile Index on postrenal transplant outcomes

We analyzed whether there is an interaction between the Kidney Donor Profile Index (KDPI) and cold ischemia time (CIT) in recipients of deceased donor kidney transplant (KTs). Adults who underwent KTs in the United States between 2014 and 2020 were included and divided into 3 KDPI groups (≤20%, 21%-85%, >85%) and 4 CIT strata (<12, 12-17.9, 18-23.9, ≥24 hours). Multivariate analyses were used to test the interaction between KDPI and CIT for the following outcomes: primary graft nonfunction (PGNF), delayed graft function (DGF), estimated glomerular filtration rate (eGFR) at 6 and 12 months, patient survival, graft survival, and death-censored graft survival (DCGS). A total of 69,490 recipients were analyzed: 18,241 (26.3%) received a graft with KDPI ≤20%, 46,953 (67.6%) with KDPI 21%-85%, and 4,296 (6.2%) with KDPI >85%. Increasing KDPI and CIT were associated with worse post-KT outcomes. Contrary to our hypothesis, howerver, the interaction between KDPI and CIT was statistically significant only for PGNF and DGF and eGFR at 6 months. Paradoxically, the negative coefficient of the interaction suggested that increasing duration of CIT was more detrimental for low and intermediate-KDPI organs relative to high-KDPI grafts. Conversely, for mortality, graft survival, and DCGS, we found that the interaction between CIT and KDPI was not statistically significant. We conclude that, high KDPI and prolonged CIT are independent risk factors for inferior outcomes after KT. Their interaction, however, is statistically significant only for the short-term outcomes and more pronounced on low and intermediate-KDPI grafts than high-KDPI kidneys.

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.

Trends in Survival for Adult Organ Transplantation

Objective

Intent-to-treat analysis follows patients from listing to death, regardless of their transplant status, and aims to provide a more holistic scope of the progress made in adult solid-organ transplantation.

Background

Many studies have shown progress in waitlist and post-transplant survival for adult kidney, liver, heart, and lung transplants, but there is a need to provide a more comprehensive perspective of transplant outcomes for patients and their families.

Methods

Univariable and multivariable Cox regression analyses were used to analyze factors contributing to intent-to-treat survival in 813,862 adults listed for kidney, liver, heart, and lung transplants. The Kaplan-Meier method was used to examine changes in waitlist, post-transplant, and intent-to-treat survival. Transplantation rates were compared using χ2 tests.

Results

Intent-to-treat survival has steadily increased for liver, heart, and lung transplants. The percentage of patients transplanted within 1 year significantly increased for heart (57.4% from 52.9%) and lung (73.5% from 33.2%). However, the percentage of patients transplanted within 1 year significantly decreased from 35.8% to 21.2% for kidney transplant. Notably, intent-to-treat survival has decreased for kidneys despite increases in waitlist and post-transplant survival, likely because of the decreased transplant rate.

Conclusion

Intent-to-treat survival steadily improved for liver, heart, and lung transplant over the 30-year study period. Continued advancements in allocation policy, immunosuppression, and improved care of patients on the waitlist may contribute to further progress in outcomes of all organs, but the increasing discrepancy in supply and demand of donor kidneys is alarming and has impeded the progress of kidney intent-to-treat survival.

Physician Preferences when Selecting Candidates for Lower-Quality Kidney Offers

BACKGROUND

In the United States, more than 50% of kidneys in the lowest 15% quality range (those with Kidney Donor Profile Index >85) are discarded. Studies suggest that using more of these kidneys could benefit patients waiting for a transplant. This study assesses the trade-offs physicians make when selecting recipients for lower-quality kidneys.

METHODS

A discrete choice experiment (DCE) was administered to surgeons and nephrologists in the United States who are involved in kidney acceptance decisions. The DCE presented kidneys that varied in terms of Kidney Donor Profile Index, expected cold ischemia time, donor age, pump parameters, serum creatinine levels, glomerulosclerosis, donor diabetes status, and whether donation was made after circulatory death. Candidate characteristics included recipients' age, diabetes history, time on dialysis, ejection fraction, HLA mismatch, calculated panel reactive antibody, and Karnofsky performance score. Regression analysis was used to estimate acceptability weights associated with kidney and recipient characteristics.

RESULTS

A total of 108 physicians completed the DCE. The likelihood of acceptance was significantly lower with deterioration of kidney quality, expected cold ischemia time at transplantation, and missing biopsy and pump information. Acceptance was prioritized for patients who were higher on the waiting list, younger recipients, those who have spent less time on dialysis, and those without a history of diabetes. Performance status (Karnofsky score) and calculated panel reactive antibody also had a statistically significant but smaller association. Finally, ejection fraction had a marginally significant association, and HLA match had no significant association with the acceptance of marginal kidneys. A group of respondents were found to be primarily concerned about cold ischemia time.

CONCLUSIONS

In this DCE, physicians considered the recipient characteristics that inform expected post-transplant survival score when they decided whether to accept a marginal kidney for a given recipient.

Considerable Variability Among Transplant Nephrologists in Judging Deceased Donor Kidney Offers

Introduction

Transplant clinicians may disagree on whether or not to accept a deceased donor kidney offer. We investigated the interobserver variability between transplant nephrologists regarding organ acceptance and whether the use of a prediction model impacted their decisions.

Methods

We developed an observational online survey with 6 real-life cases of deceased donor kidneys offered to a waitlisted recipient. Per case, nephrologists were asked to estimate the risk of adverse outcome and whether they would accept the offer for this patient, or for a patient of their own choice, and how certain they felt. These questions were repeated after revealing the risk of adverse outcome, calculated by a validated prediction model.

Results

Sixty Dutch nephrologists completed the survey. The intraclass correlation coefficient of their estimated risk of adverse outcome was poor (0.20, 95% confidence interval [CI] 0.08-0.62). Interobserver agreement of the decision on whether or not to accept the kidney offer was also poor (Fleiss kappa 0.13, 95% CI 0.129-0.130). The acceptance rate before and after providing the outcome of the prediction model was significantly influenced in 2 of 6 cases. Acceptance rates varied considerably among transplant centers.

Conclusion

In this study, the estimated risk of adverse outcome and subsequent decision to accept a suboptimal donor kidney varied greatly among transplant nephrologists. The use of a prediction model could influence this decision and may enhance nephrologists' certainty about their decision.

Exploring the variable importance in random forests under correlations: a general concept applied to donor organ quality in post-transplant survival

Random Forests are a powerful and frequently applied Machine Learning tool. The permutation variable importance (VIMP) has been proposed to improve the explainability of such a pure prediction model. It describes the expected increase in prediction error after randomly permuting a variable and disturbing its association with the outcome. However, VIMPs measure a variable's marginal influence only, that can make its interpretation difficult or even misleading. In the present work we address the general need for improving the explainability of prediction models by exploring VIMPs in the presence of correlated variables. In particular, we propose to use a variable's residual information for investigating if its permutation importance partially or totally originates from correlated predictors. Hypotheses tests are derived by a resampling algorithm that can further support results by providing test decisions and p-values. In simulation studies we show that the proposed test controls type I error rates. When applying the methods to a Random Forest analysis of post-transplant survival after kidney transplantation, the importance of kidney donor quality for predicting post-transplant survival is shown to be high. However, the transplant allocation policy introduces correlations with other well-known predictors, which raises the concern that the importance of kidney donor quality may simply originate from these predictors. By using the proposed method, this concern is addressed and it is demonstrated that kidney donor quality plays an important role in post-transplant survival, regardless of correlations with other predictors.

Impact of suboptimal donor to suboptimal recipient kidney transplant on delayed graft function and outcome

Introduction

The demographics of donor and recipient candidates for kidney transplantation (KT) have substantially changed. Recipients tend to be older and polymorbid and KT to suboptimal recipients is associated with delayed graft function (DGF), prolonged hospitalization, inferior long-term allograft function, and poorer patient survival. In parallel, donors are also older, suffer from several comorbidities, and donations coming from circulatory death (DCD) predominate, which in turn leads to early and late complications. However, it is unclear how donor and recipient risk factors interact.

Methods

In this retrospective cohort study, we assess the impact of a KT from suboptimal donors to suboptimal recipients. We focused on: 1) DGF; 2) hospital stay and number of dialysis days after KT and 3) allograft function at 12 months.

Results and discussion

Among the 369 KT included, the overall DGF rate was 25% (n = 92) and median time from reperfusion to DGF resolution was 7.8 days (IQR: 3.0-13.8 days). Overall, patients received four dialysis sessions (IQR: 2-8). The combination of pre-KT anuria (<200 ml/24 h, 32%) and DCD procurement (14%) was significantly associated with DGF, length of hospital stay, and severe perioperative complications, predominantly in recipients 50 years and older.

All Expanded Criteria Donor Kidneys are Equal But are Some More Equal Than Others? A Population-Cohort Analysis of UK Transplant Registry Data

Survival outcomes for kidney transplant candidates based on expanded criteria donor (ECD) kidney type is unknown. A retrospective cohort study was undertaken of prospectively collected registry data of all waitlisted kidney failure patients receiving dialysis in the United Kingdom. All patients listed for their first kidney-alone transplant between 2000-2019 were included. Treatment types included; living donor; standard criteria donor (SCD); ECD60 (deceased donor aged ≥60 years); ECD50-59 (deceased donor aged 50-59 years with two from the following three; hypertension; raised creatinine and/or death from stroke) or remains on dialysis. The primary outcome was all-cause mortality, with time-to-death from listing analyzed using time-dependent non-proportional Cox regression models. The study cohort comprised 47,917 waitlisted kidney failure patients, of whom 34,558 (72.1%) received kidney transplantation. ECD kidneys (n = 7,356) were stratified as ECD60 (n = 7,009) or ECD50-59 (n = 347). Compared to SCD, both ECD60 (Hazard Ratio 1.126, 95% CI 1.093-1.161) and ECD50-59 (Hazard Ratio 1.228, 95% CI 1.113-1.356) kidney recipients have higher all-cause mortality. However, compared to dialysis, both ECD60 (Hazard Ratio 0.194, 95% CI 0.187-0.201) and ECD50-59 (Hazard Ratio 0.218, 95% CI 0.197-0.241) kidney recipients have lower all-cause mortality. ECD kidneys, regardless of definition, provide equivalent and superior survival benefits in comparison to remaining waitlisted.

Predicting 5-year survival after kidney transplantation in Colombia using the survival benefit estimator tool

INTRODUCTION

A complex relationship between donor and recipient characteristics influences kidney transplant (KT) success. A tool developed by Bae S. et al. (Survival Benefit Estimator, SBE) helps estimate post-KT survival. We aim to evaluate the predictive performance of the SBE tool in terms of 5-year patient survival after a kidney transplant.

METHODS

A retrospective cohort study of all deceased-donor KT recipients between January 2009 to December 2021. A descriptive analysis of clinical and sociodemographic characteristics was performed. The SBE online tool was used to calculate the predicted patient survival (PPS) and the survival benefit at five years post-KT. Comparisons between predictive vs. actual patient survival were made using quintile subgroups. Three Cox regression models were built using PPS, EPTS, and KDPI.

RESULTS

A total of 1145 recipients were evaluated. Mortality occurred in 157 patients. Patient survival was 86.2%. Predictive survival for patients if they remained on the waiting list was 70.6%. The PPS was 89.3%, which results in a survival benefit (SB) of 18.7% for our population. Actual survival rates were lower than the predicted ones across all the quintiles. In unadjusted analysis, PPS was a significant protective factor for mortality (HR 0.66), whereas EPTS (HR 8.9) and KDPI (HR 3.25) scores were significant risk factors. The discrimination of KDPI, PPS, and EPTS scores models were 0.59, 0.65, and 0.66, respectively.

CONCLUSION

SBE score overestimated actual survival rates in our sample. The discrimination power of the score was moderate, although the utility of this tool may be limited in this specific population.

Outcomes of Declined Deceased Donor Kidney Offers That Are Subsequently Implanted: A UK Registry Study

BACKGROUND

Deceased donor kidneys are often declined for ≥1 patients but then implanted into another. Studies are needed to guide transplant clinicians and patients, especially given the increasing age and comorbidity of donors. This study compares outcomes of recipients of transplanted kidneys that were initially declined with outcomes of patients who remained on the waiting list.

METHODS

This UK Transplant Registry study examined named-patient, adult donation after brain death donor single kidney-only offers that were declined for donor- or organ-related reasons (DORRs), in which the kidney was subsequently transplanted from January 1, 2010, to December 31, 2018. Outcomes included graft function and survival of kidneys transplanted following DORR decline, survival and transplant status of patients who had a kidney declined, and intercenter decline rates.

RESULTS

A total of 4722 kidneys declined for DORRs, which eventually resulted in single kidney-only transplants, were examined. One year after the offer decline, 35% of patients for whom the organ was declined remained on the list, 55% received a deceased donor transplant at a median of 174 d after the initial offer decline, and 4% had been removed or died. For patients transplanted following offer decline, there was no significant difference in 5-y graft survival when comparing the outcomes to those recipients who received the declined kidney. There was significant variation in DORR decline rates between UK transplant units (17%-54%).

CONCLUSIONS

This study shows reasonable outcomes of kidneys previously declined for DORRs and supports the utilization of those considered to be of higher risk for carefully selected recipients.

Kidney organ allocation: reducing discards

PURPOSE OF REVIEW

The donation and kidney transplant system in the United States is challenged with reducing the number of kidneys that are procured for transplant but ultimately discarded. That number can reach 20% of donated kidneys each year.

RECENT FINDINGS

The reasons for these discards, in the face of overwhelming demand, are multiple.

SUMMARY

The authors review the data supporting a number of potential causes for high discard rates as well as provide potential solutions to the problem.

Maximizing opportunities for kidney transplantation in older adults

PURPOSE OF REVIEW

To summarize the current state of evidence related to the outcomes of older adults who need and receive kidney transplants, and strategies to facilitate appropriate transplant access in this at-risk group.

RECENT FINDINGS

Older adults are a rapidly growing subgroup of the kidney transplant waitlist. Compared to younger adults, older kidney transplant recipients have increased mortality after kidney transplant and lower death-censored graft survival. In determining suitability for transplantation in older patients, clinicians must balance procedural and immunosuppression-related risk with incremental survival when compared with dialysis. To appropriately increase access to transplantation in this population, clinicians and policy makers consider candidates' chronological age and frailty, as well as the quality of and waiting time for a donated allograft. Given risk of deterioration prior to transplant, candidates should be rapidly evaluated, listed, and transplanted using living donor and or less than ideal deceased donor organs when available.

SUMMARY

Access to transplantation for older adults can be increased through targeted interventions to address frailty and reduce waiting times through optimized organ use. Focused study and educational interventions for patients and providers are needed to improve the outcomes of this vulnerable group.

Does anybody really know what (the kidney median waiting) time is?

The median waiting time (MWT) to deceased donor kidney transplant is of interest to patients, clinicians, and the media but remains elusive due to both methodological and philosophical challenges. We used Organ Procurement and Transplantation Network data from January 2003 to March 2022 to estimate MWTs using various methods and timescales, applied overall, by era, and by candidate demographics. After rising for a decade, the overall MWT fell to 5.19 years between 2015 and 2018 and declined again to 4.05 years (April 2021 to March 2022), based on the Kaplan-Meier method applied to period-prevalent cohorts. MWTs differed markedly by blood type, donor service area, and pediatric vs adult status, but to a lesser degree by race/ethnicity. Choice of methodology affected the magnitude of these differences. Instead of waiting years for an answer, reliable kidney MWT estimates can be obtained shortly after a policy is implemented using the period-prevalent Kaplan-Meier approach, a theoretical but useful construct for which we found no evidence of bias compared with using incident cohorts. We recommend this method be used complementary to the competing risks approach, under which MWT is often inestimable, to fill the present information void concerning the seemingly simple question of how long it takes to get a kidney transplant in the United States.

Kidney Transplantation Outcome Predictions (KTOP): A Risk Prediction Tool for Kidney Transplants from Brain-dead Deceased Donors Based on a Large European Cohort

BACKGROUND

European kidney donation shortages mandate efficient organ allocation by optimizing the prediction of success for individual recipients.

OBJECTIVE

To develop the first European online risk tool for kidney transplant outcomes on the basis of recipient-only and recipient plus donor characteristics.

DESIGN, SETTING, AND PARTICIPANTS

We used individual recipient and donor risk factors and three outcomes (death, death with functioning graft [DWFG], and graft loss) for 32 958 transplants within the Eurotransplant kidney allocation system and the Eurotransplant senior program between January 2006 and May 2018 in eight European countries to develop and validate a risk tool.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Cox proportional-hazards models were used to analyze the association of risk factors with overall patient mortality, and proportional subdistribution hazard regression models for their association with graft loss and DWFG. Prediction models were developed with recipient-only and recipient-donor risk factors. Sensitivity analyses based on time-specific area under the receiver operating characteristic curve (AUC) with leave-one-country-out validation were performed and calibration plots were generated.

RESULTS AND LIMITATIONS

The 10-yr cumulative incidence rate was 37% for mortality, 12% for DWFG, and 41% for graft loss. In recipient-donor models the leading risk factors for mortality were recipient diabetes (hazard ratio [HR] 10.73), retransplantation (HR 3.08 per transplant), and recipient age (HR 1.08). Effects were similar for DWFG. For graft loss, diabetes (subdistributional HR [SHR] 1.32), increased donor age (SHR 1.02), and prolonged cold ischemia time (SHR 1.02) had increased SHRs. All p values were <0.001.

CONCLUSIONS

Previously identified risk factors for outcomes following kidney transplants allow for outcome prediction with 10-yr AUC values of up to 0.81.

PATIENT SUMMARY

Using European data, we estimated individual risks to predict the success of kidney transplants and support physicians in decision-making. An online tool is now available (https://riskcalc.org/ktop/) for predicting kidney transplant outcomes both before and after a donor has been identified.

The strategy of diminishing age gap effect on different donor-recipient combinations in living donor kidney transplantation

BACKGROUND

The disparity between kidney donation and the number of uremic patients on the waiting list has increased the demand for older live-donor kidneys (OLK). However, the donor-recipient age gap may have an impact on the recipient's outcome.

METHODS

Patients who underwent living donor kidney transplantation at our institute between 2005 and 2019 were enrolled and categorized into four donor-recipient groups according to age (≥50 years and <50 years). The Estimated Post-Transplant Survival (EPTS) score was used to quantify the recipient's condition. Adjusted models analyzed recipient outcomes and related risks among the four groups.

RESULTS

Of the 154 pairs of live donors and recipients, OLK did not influence overall or death-censored graft survival. The four donor-recipient combinations had similar recipient outcomes, except it slightly worsened in the "old donor to young recipient" group. The EPTS score (adjusted HR, 1.02; 95% CI, 1.01-1.04; p = 0.014) and rejection (adjusted HR, 4.26; 95% CI, 1.36-13.37; p = 0.013) were significant risk factors for overall and death-censored graft survival, respectively. Recipients with pretransplant diabetes or prior solid organ transplantation could have amplified risk effects. The main causes of graft loss were death in older recipients and chronic rejection in younger recipients.

CONCLUSION

OLK is safe for young recipients. Nevertheless, adequate immunosuppression should be maintained to prevent rejection and subsequent graft loss, especially for those receiving second kidney transplantation. In contrast, older recipients should avoid overt immunosuppression and control their comorbidities, such as diabetes-related complications to improve their long-term outcomes.

Pretransplant kidney transcriptome captures intrinsic donor organ quality and predicts 24-month outcomes

With the development of novel prognostic tools derived from omics technologies, transplant medicine is entering the era of precision medicine. Currently, there are no established predictive biomarkers for posttransplant kidney function. A total of 270 deceased donor pretransplant kidney biopsies were collected and posttransplant function was prospectively monitored. This study first assessed the utility of pretransplant gene expression profiles in predicting 24-month outcomes in a training set (n = 174). Nearly 600 differentially expressed genes were associated with 24-month graft function. Grafts that progressed to low function at 24 months exhibited upregulated immune responses and downregulated metabolic processes at pretransplantation. Using penalized logistic regression modeling, a 55 gene model area under the receiver operating curve (AUROC) for 24-month graft function was 0.994. Gene expression for a subset of candidate genes was then measured in an independent set of pretransplant biopsies (n = 96) using quantitative polymerase chain reaction. The AUROC when using 13 genes with three donor characteristics (age, race, body mass index) was 0.821. Subsequently, a risk score was calculated using this combination for each patient in the validation cohort, demonstrating the translational feasibility of using gene markers as prognostic tools. These findings support the potential of pretransplant transcriptomic biomarkers as novel instruments for improving posttransplant outcome predictions and associated management.

Patient Preferences for Waiting Time and Kidney Quality

BACKGROUND AND OBJECTIVES

Approximately 20% of deceased donor kidneys are discarded each year in the United States. Some of these kidneys could benefit patients who are waitlisted. Understanding patient preferences regarding accepting marginal-quality kidneys could help more of the currently discarded kidneys be transplanted.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

This study uses a discrete choice experiment that presents a deceased donor kidney to patients who are waiting for, or have received, a kidney transplant. The choices involve trade-offs between accepting a kidney today or a future kidney. The options were designed experimentally to quantify the relative importance of kidney quality (expected graft survival and level of kidney function) and waiting time. Choices were analyzed using a random-parameters logit model and latent-class analysis.

RESULTS

In total, 605 participants completed the discrete choice experiment. Respondents made trade-offs between kidney quality and waiting time. The average respondent would accept a kidney today, with 6.5 years of expected graft survival (95% confidence interval, 5.9 to 7.0), to avoid waiting 2 additional years for a kidney, with 11 years of expected graft survival. Three patient-preference classes were identified. Class 1 was averse to additional waiting time, but still responsive to improvements in kidney quality. Class 2 was less willing to accept increases in waiting time for improvements in kidney quality. Class 3 was willing to accept increases in waiting time even for small improvements in kidney quality. Relative to class 1, respondents in class 3 were likely to be age ≤61 years and to be waitlisted before starting dialysis, and respondents in class 2 were more likely to be older, Black, not have a college degree, and have lower Karnofsky performance status.

CONCLUSIONS

Participants preferred accepting a lower-quality kidney in return for shorter waiting time, particularly those who were older and had lower functional status.

An enhanced level of VCAM in transplant preservation fluid is an independent predictor of early kidney allograft dysfunction

Background

We aimed to evaluate whether donor-related inflammatory markers found in kidney transplant preservation fluid can associate with early development of kidney allograft dysfunction.

Methods

Our prospective study enrolled 74 consecutive donated organs who underwent kidney transplantation in our center between September 2020 and June 2021. Kidneys from 27 standard criteria donors were allocated to static cold storage and kidneys from 47 extended criteria donors to hypothermic machine perfusion. ELISA assessment of inflammatory biomarkers (IL-6, IL6-R, ICAM, VCAM, TNFα, IFN-g, CXCL1 and Fractalkine) was analyzed in view of a primary endpoint defined as the occurrence of delayed graft function or slow graft function during the first week following transplantation.

Results

Soluble VCAM levels measured in transplant conservation fluid were significantly associated with recipient serum creatinine on day 7. Multivariate stepwise logistic regression analysis identified VCAM as an independent non-invasive predictor of early graft dysfunction, both at 1 week (OR: 3.57, p = .04, 95% CI: 1.06-12.03) and 3 Months (OR: 4.039, p = .034, 95% CI: 1.11-14.73) after transplant surgery.

Conclusions

This prospective pilot study suggests that pre-transplant evaluation of VCAM levels could constitute a valuable indicator of transplant health and identify the VCAM-CD49d pathway as a target to limit donor-related vascular injury of marginal transplants.

Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation

Artificial intelligence (AI) refers to computer algorithms used to complete tasks that usually require human intelligence. Typical examples include complex decision-making and- image or speech analysis. AI application in healthcare is rapidly evolving and it undoubtedly holds an enormous potential for the field of solid organ transplantation. In this review, we provide an overview of AI-based approaches in solid organ transplantation. Particularly, we identified four key areas of transplantation which could be facilitated by AI: organ allocation and donor-recipient pairing, transplant oncology, real-time immunosuppression regimes, and precision transplant pathology. The potential implementations are vast—from improved allocation algorithms, smart donor-recipient matching and dynamic adaptation of immunosuppression to automated analysis of transplant pathology. We are convinced that we are at the beginning of a new digital era in transplantation, and that AI has the potential to improve graft and patient survival. This manuscript provides a glimpse into how AI innovations could shape an exciting future for the transplantation community.

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.

Survival Benefit in Older Patients Transplanted With Viremic Hepatitis C Positive Kidneys When Compared With High KDPI Kidneys

BACKGROUND

Because of the continued demand in kidney transplantation, organs from donors with risk criteria for blood-borne viruses, high Kidney Donor Profile Index (KDPI) kidneys, and hepatitis C virus (HCV)-positive kidneys are being considered. There continues to be reluctance on the part of the providers and the candidates to accept HCV-positive kidneys.

METHODS

We conducted a retrospective analysis of the Organ Procurement and Transplantation Network database of all adult (≥18 y old) recipients undergoing kidney transplant from May 10, 2013, to June 30, 2021. We compared patient and graft survival in candidates who received HCV-positive kidneys versus non-hepatitis C (Hep C) high KDPI kidneys by estimated posttransplant survival (EPTS) groups.

RESULTS

HCV-viremic kidneys were transplanted in 5.6% of patients in the EPTS >61% group compared with 5.1% of patients in the 21%-60% EPTS group and 1.9% of 0%-20% EPTS group (P < 0.001). Of all transplants performed in the EPTS 61%-100% group, 11.9% were KDPI >85% compared with 5.2% in the EPTS 21%-60%, and 0.5% in the EPTS 0%-20%. Patient survival was significantly longer at 1, 3, and 5 y in the EPTS >61% group who received Hep C-viremic or -nonviremic allografts compared with non-Hep C kidneys with KDPI >85%. When it comes to listing, only 25% of candidates in the EPTS >61% group were listed for Hep C nucleic acid testing-positive kidneys in 2021.

CONCLUSIONS

Our findings could be used for counseling candidates on the types of kidneys they should consider for transplantation. Also, listing practices for viremic Hep C kidneys need continued re-evaluation.

The Independent Effects of Procurement Biopsy Findings on Ten-Year Outcomes of Extended Criteria Donor Kidney Transplants

Introduction

Methods

Results

Conclusion

Beyond Graft Survivl: A National Cohort Study Quantifying the Impact of Increasing Kidney Donor Profile Index on Recipient Outcomes 1 Year Post-transplantation

Background.

The reporting of a locally validated kidney donor profile index (KDPI) began in Australia in 2016. Across diverse populations, KDPI has demonstrated utility in predicting allograft survival and function. A metric that incorporates both elements may provide a more comprehensive picture of suboptimal recipient outcomes.

Methods.

A retrospective cohort study of adult kidney transplant recipients in Australia (January 2009 to December 2014) was conducted. Conventional recipient outcomes and a composite measure of suboptimal outcome (1-y allograft failure or estimated glomerular filtration rate [eGFR] <30 mL/min) were evaluated across KDPI intervals (KDPI quintiles and 5-point increments in the KDPI 81–100 cohort). The impact of increasing KDPI on allograft function (1-y eGFR) and a suboptimal outcome was explored using multivariable regression models, adjusting for potential confounding factors.

Results.

In 2923 donor kidneys eligible for analysis, median KDPI was 54 (interquartile range [IQR], 31–77), and Kidney Donor Risk Index was 1.39 (IQR, 1.03–1.67). The median 1-y eGFR was 52.74 mL/min (IQR, 40.79–66.41 mL/min). Compared with the first quintile reference group, progressive reductions in eGFR were observed with increasing KDPI and were maximal in the fifth quintile (adjusted β-coefficient: −27.43 mL/min; 95% confidence interval, –29.44 to –25.42; P < 0.001). A suboptimal outcome was observed in 359 recipients (12.3%). The adjusted odds for this outcome increased across quintiles from a baseline of odds ratio of 1.00 (first quintile) to odds ratio of 11.68 (95% confidence interval, 6.33-21.54, P < 0.001) in the fifth quintile cohort.

Conclusions.

Increases in donor KDPI were associated with higher probabilities of a suboptimal outcome and poorer baseline allograft function, particularly in the KDPI > 80 cohort. These findings may inform pretransplant discussions with potential recipients of high-KDPI allografts.

Kidney transplant candidates' and recipients' perspectives on the decision-making process to accept or refuse a deceased donor kidney offer: Trust and graft survival matter

BACKGROUND

The decision to accept a kidney from a deceased donor can be a difficult one. This study aims to capture the perspectives of transplant candidates (TCs) and kidney transplant recipients (KTRs) on the decision-making process when a deceased kidney is offered.

METHODS

We conducted six focus groups with KTRs and TCs. The content of the focus groups was analyzed using the qualitative thematic method.

RESULTS

KTRs reported that the experience of being offered a kidney could be difficult because of the circumstances of the offer and unpreparedness to participate in the discussion. Both KTRs and TCs trusted the medical expertise. Age and having experience with dialysis could influence the decision to accept an offer. In order to engage in the discussion, patients wanted to obtain estimates of expected graft survival. Patients did not express interest for a web-based calculator for patient use, but expected transplant physicians to summarize and explain the information that would impact graft survival time.

CONCLUSION

TCs and KTRs wanted to be involved in the decision to accept a deceased donor kidney. Tools that can help physicians communicate the risks and benefits of accepting an offer could improve patient participation in the decision-making process.

Prescriptive Analytics Determining Which Patients Undergoing Simultaneous Liver-Kidney Transplant May Benefit From High-Risk Organs

OBJECTIVES

Simultaneous liver-kidney transplant is a treatment option for patients with end-stage liver disease and concomitant irreversible kidney injury. We developed a decision toolto aid transplant programs to advise their candidates for simultaneous liver-kidney transplant on accepting high-risk grafts versus waiting for lower-risk grafts.

MATERIALS AND METHODS

To find the critical decision factors, we used the prescriptive analytic technique of microsimulation.All probabilities used in the simulation model were calculated from Organ Procurement and Transplantation Network data collected from February 27, 2002 to June 30, 2018.

RESULTS

The simulated patient population results revealed, on average, that high-risk candidates for simultaneous liver-kidney transplant who accept highrisk organs have 254.8 ± 225.4 weeks of life compared with 285.6 ± 232.4 weeks if they waited for better organs. However, critical decision factors included the specific organ offer rates within individual transplant programs and the rank of the candidate in each program's waitlist. Thus, for programs with lower organ offer rates or for candidates with a rare blood type, a high-risk simultaneous liver-kidney transplant candidate might accept a high-risk organ for longer survival.

CONCLUSIONS

Our model can be utilized to determine when acceptance of high-risk organs for patients being considered for simultaneous liver-kidney transplant would lead to survival benefit, based on probabilities specific for their program.

Long-Term Results of Kidney Transplantation in the Elderly: Comparison between Different Donor Settings

With scarce organ supply, a selection of suitable elderly candidates for transplant is needed, as well as auditing the long-term outcomes after transplant. We conducted an observational cohort study among our patient cohort >60 years old with a long follow up. (1). Patients and Methods: We used our database to study the results after transplant for 593 patients >60 years old who underwent a transplant between 2000–2017. The outcome was compared between live donor (LD; n = 257) recipients, an old-to-old (OTO, n = 215) group using an extended criteria donor (ECD) kidney, and a young-to-old (YTO, n = 123) group using a standard-criteria donor. The Kaplan−Meir method was used to calculate the patient and graft survival and Cox regression analysis in order to find risk factors associated with death. (2). Results: The 5- and 10-year patient survival was significantly better in the LD group (92.7% and 66.9%) compared with the OTO group (73.3% and 42.8%) and YTO group (70.9% and 40.6%) (p < 0.0001). The 5- and 10-year graft survival rates were 90.3% and 68.5% (LD), 61.7% and 30.9% (OTO), and 64.1% and 39.9%, respectively (YTO group; p < 0.0001 between the LD and the two DD groups). There was no difference in outcome between patients in their 60’s and their 70’s. Factors associated with mortality included: age (HR-1.060), DM (HR-1.773), IHD (HR-1.510), and LD/DD (HR-2.865). (3). Conclusions: Our 17-years of experience seems to justify the rational of an old-to-old allocation policy in the elderly population. Live-donor transplant should be encouraged whenever possible. Each individual decision of elderly candidates for transplant should be based on the patient’s comorbidity and predicted life expectancy.

Kidney Allocation Policy: Past, Present, and Future

Despite an increase in the number of kidney transplants performed annually, there remain more than 90,000 individuals awaiting transplantation in the United States. As kidney transplantation has evolved, so has kidney allocation policies. The Kidney Allocation System, which was introduced in 2014, made significant strides to improve utility and equity, but regional and geographic disparities remain. Further modifications eliminating donor service areas have been introduced. Moving forward, systems involving continuous distribution and artificial intelligence may provide further advancement toward an ideal allocation system.

Technology-Enabled Care and Artificial Intelligence in Kidney Transplantation

Purpose of Review

Artificial intelligence (AI), machine learning, and technology-enabled remote patient care have evolved rapidly and have now been incorporated into many aspects of medical care. Transplantation is fortunate to have large data sets upon which machine learning algorithms can be constructed. AI are now available to improve pretransplant management, donor selection, and post-operative management of transplant patients.

Recent Findings

Changes in patient and donor characteristics warrant new approaches to listing and organ acceptance practices. Machine learning has been employed to optimize donor selection to identify patients likely to benefit from transplantation of higher risk organs, increasing organ discard and reducing waitlist mortality. These models have greater precisions and predictive ability than currently employed metrics including the Kidney Donor Profile Index and the expected posttransplant survival models. After transplant, AI tools have been developed to optimize immunosuppression management, track patients adherence, and assess graft survival.

Summary

AI and technology-enabled management tools are now available throughout the transplant journey. Unfortunately, those are frequently not available at the point of decision (patient listing, organ acceptance, posttransplant clinic), limiting utilization. Incorporation of these tools into the EMR, the Donor Net® organ offer system, and mobile devices is vital to ensure widespread adoption.

Development of predictive score for posttransplant survival based on pre-transplant recipient characteristics

Background

The new kidney allocation system in the United States has introduced longevity matching, which gives priority to allocating the best quality organs to wait-listed candidates with the longest predicted survival for the efficient utilization of organs that are of limited availability. The estimated post-transplant survival (EPTS) score was developed in the United States to risk-stratify all wait-listed patients. However, prognostic indices used in Western countries were derived from data that may be different for Korea and do not necessarily reflect prognostic values for Korean deceased donor kidney transplantation. Prognostic indices for Korean wait-listed candidates therefore need to be developed from Korean data.

Methods

We analyzed 6,731 adult solitary kidney transplant patients for candidate risk prediction using the national data from the Korean Network for Organ Sharing (KONOS) and National Health Insurance Data Sharing Service (NHISS). Cox regression analysis was used to model the risk of patient death.

Results

The Korean EPTS (K-EPTS) score was developed based on four recipient parameters (age, diabetes mellitus, hepatitis C virus, and duration of dialysis) that showed a significant association with post-transplant survival. K-EPTS scores showed good discrimination (C-statistics 0.690; 95% confidence interval, 0.666–0.715). Moreover, the ability of the K-EPTS score to discriminate patient survival was better than that of the EPTS according to the criteria of the United Network for Organ Sharing (US-EPTS) score (P<0.001).

Conclusions

The K-EPTS score, which was developed based on Korean national data, is expected to contribute to the assessment of recipient prognosis and efficient utilization of deceased donor kidneys.

Donation after circulatory death is associated with increased fibrosis on 1-year post-transplant kidney allograft surveillance biopsy

AIM

The use of kidneys donated after circulatory death (DCD) provides an invaluable expansion of the organ supply for transplantation. Here, we investigated the effect of DCD on fibrotic changes on 1 1-year post 1-transplant surveillance kidney allograft biopsy.

METHODS

Recipients of a deceased donor kidney transplant between 2013 and 2017 at a single institution, who survived 1 year and underwent surveillance biopsy, were included in the analysis (n = 333: 87 DCD kidneys, 246 kidneys donated after brain death [DBD]). Banff scores for interstitial fibrosis and tubular atrophy were summed as IFTA and compared between the groups.

RESULTS

DCD and DBD groups were comparable for baseline characteristics. Delayed graft function was 39% in DCD versus 19% in DBD, P = .0002. Patient and graft survival were comparable for DCD and DBD cohorts. IFTA scores were higher in DCD compared to DBD (2.43±..13 vs. 2.01±..08, P = .0054). On multivariate analysis, the odds of IFTA > 2 in the DCD group was 2.5× higher (95%CI: 1.354.63) than in the DBD group. Within the DCD group, kidneys with IFTA > 2 had inferior 5-year graft survival (P = .037).

CONCLUSION

Compared to DBD kidneys, DCD kidneys developed a greater degree of fibrotic changes on 1-year post-transplant surveillance biopsy, which affected graft longevity within the DCD cohort.

In Search of a Better Outcome: Opting Into the Live Donor Paired Kidney Exchange Program

Background

Live donor (LD) kidney transplantation is the best option for patients with end-stage kidney disease (ESKD). However, this may not be the best option if a patient's donor is older and considerably smaller in weight. Patient (A) with a less than ideal donor (Donor A) might enter into a live donor paired exchange (LDPE) program with the hopes of swapping for a better-quality organ. A second patient (B) who is in the LDPE may or may not benefit from this exchange with Donor A.

Methods

This medical decision analysis examines the conditions that favor Patient A entering into the LDPE compared to directly accepting a kidney from their intended donor, as well as the circumstances where Patient B also benefits by accepting a lower-quality organ.

Results

Under select circumstances, a paired exchange could benefit both Patients A and B. For example, a 30-year-old Patient A with a lower-quality donor might gain _1.201.52_1.84 quality adjusted life years (QALYs) by entering into a LDPE for a better-quality kidney, whereas a 60-year-old Patient B might gain _0.931.03_1.13 QALYs by accepting Donor A's kidney rather than waiting longer in the LDPE. The net benefit (or loss) of entering the LDPE differs by recipient age, donor organ quality, likelihood of Patient B being transplanted in LDPE, and likelihood of Patient A finding an ideal donor in the LDPE.

Conclusion

This study shows there are ways to increase live donor utilization and effectiveness that require further research and potentially changes to the LDPE process.