Figure 1 - available via license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Content may be subject to copyright.
Kidney Donor Profile Index (KDPI) distribution for recovered kidneys, correct versus erroneously high KDPI scores. The KDPI typically has a uniform distribution between 1% and 100% among recovered deceased kidney donors (top panel); however, from April 20 to May 19, 2016, KDPI was calculated incorrectly in DonorNet, resulting in a distribution skewed toward higher values (bottom panel).
Source publication
The Kidney Donor Profile Index (KDPI) became a driving factor in deceased donor kidney allocation on December 4, 2014 with the implementation of the kidney allocation system (KAS). On April 20, 2016, the annual recalibration of the Kidney Donor Risk Index (KDRI) into KDPI was incorrectly programmed in DonorNet, resulting in erroneously-high KDPI va...
Contexts in source publication
Context 1
... definition, correctly calculated KDPI scores are uni- formly distributed from 1% to 100% in a population of deceased kidney donors. During the 30-day period between April 20 and May 19, 2016, the KDPI displayed in DonorNet and used for allocation was biased upward between 1 and 21 points (Figure 1). A correct KDPI of 99%, for example, was incorrectly displayed as 100% (+1 point bias), and a correct KDPI of 19% was incor- rectly displayed as 40% (+21 point bias). ...
Context 2
... mean and median biases were +17.2 and +19.0, respectively. Fig- ure 1 compares the KDPI distribution among kidneys recovered December 4, 2014, through May 31, 2016, for correct versus erroneously high KDPI kidneys. ...
Context 3
... distributional changes are not surprising, given the KDPI shift shown in Figure 1. Far fewer kidneys were allocated with KDPI 0-20% or 21-34%, leading to less application of the longevity-matching and pediatric priori- ties prescribed by KAS. ...
Context 4
... with the relationship shown in Figure 2, the biased-high KDPI distribution used for allocation ( on kidney utilization as a result of the calculation error. In fact, had the estimated probability of discard for each of the 21 KDPI ranges shown in Figure 2 manifested during the 30-day period, the projected discard rate (calculated by multiplying the biased distribution percentages from Figure 1 with the expected discard probabilities from Fig- ure 2) would have been 31.4%. The observed discard rate during this period, however, was just 22.9%, which was moderately but not unprecedentedly higher than previously observed post-KAS monthly discard rates (Fig- ure 3). ...
Similar publications
In Caucasian and Asian populations, evidence suggests that 24-h blood pressures (BP) are more predictive of long-term cardiovascular events than clinic BP. However, few long-term studies have evaluated the predictive value of 24-h BP phenotypes (24-h, daytime, nighttime) among African Americans (AA). The purpose of this study is to evaluate the add...
This study aims to evaluate the effect of Bacillus Calmette-Guérin (BCG) instillation and radical cystectomy on high-risk NMIBC with squamous or glandular variants. We retrospectively reviewed the data of high-risk (T1 or CIS or HG or TaG1/G2 with multiple, recurrent, large tumor) NMIBC patients from January 2000 to December 2017. Comparative analy...
Background
University life typically occurs during a period of life transition, where the incidence of mental health and substance use problems and disorders peaks. However, relatively few students obtain effective treatment and support. e-Interventions have proven effective in improving the psychological outcomes of university students and have th...
Sensitive and robust outcome measures of retinal function are pivotal for clinical trials in age-related macular degeneration (AMD). A recent development is the implementation of artificial intelligence (AI) to infer results of psychophysical examinations based on findings derived from multimodal imaging. We conducted a review of the current litera...
This bibliometric analysis represents the fifty six years (1965-2020) research productivity of Library Resources and Technical Services (LRTS). Association for Library Collections and Technical Services (ALCTS) has been publishing a journal “Library Resources and Technical Services”. It is a peer-reviewed journal and continuously serving from 1957....
Citations
... When medical decisions can be affected by the way in which data are presented, this may suggest that other biases exist. 17,[24][25][26] Might a drive toward more evidence-based decision-making reduce human bias and improve health outcomes? This study suggests that it is important to not only ask decision-makers what data they require, but also to test how the data are presented, to evaluate not only which data to present, but also how it should be presented using a systematic and objective approach. ...
Due to the breadth of factors that might affect kidney transplant decisions to accept an organ or wait for another, presumably "better" offer, a high degree of heterogeneity in decision-making exists among transplant surgeons and hospitals. These decisions do not typically include objective predictions regarding the future availability of equivalent or better-quality organs, nor the likelihood of patient death while waiting for another organ. To investigate the impact of displaying such predictions on organ donation decision making, we conducted a statistically designed experiment involving 53 kidney transplant professionals, where kidney organ offers were presented via an online application and systematically altered to observe effects on decision making. We found that providing predictive analytics for time-to-better offer and patient mortality improves decision consensus and decision maker confidence in their decision. Providing a visual display of the patient's mortality slope under accept/reject conditions shortened the time to decide, but did not have an impact on the decision itself. Presenting risk of death in a loss frame as opposed to a gain frame improved decision consensus and decision confidence. Patient-specific predictions surrounding future organ offers and mortality may improve decision quality, confidence, and expediency while improving organ utilization and patient outcomes.
... 12 Indeed, the finding that this increase is eliminated and even reversed when adjusting for all components of KDRI including race (and thus KDPI values communicated with organ offers) suggests that a KDPI labeling effect may account in part for the increased discard rates among Black donor kidneys, as has been noted for high KDPI kidneys in general. 13 Thus, the reduction of KDPI for Black donors with use of race-free KDRI would be anticipated to reduce the discard of Black donor kidneys by 10%, potentially allowing for 70 additional transplants per year from Black donors. However, since organ quality is assessed only in part by KDPI, at any given KDPI Black race may be discounted as a risk factor in utilization relative to other risk factors in the KDPI, which would explain the lower discard rates of Black kidneys when accounting for KDRI. ...
Recent events of racial injustice prompted us to study potential impact of removing race from kidney donor risk index (KDRI) calculator.
Methods:
We used Scientific Registry for Transplant Recipients data to analyze outcomes of 66 987 deceased-donor kidney transplants performed in the United States between 2010 and 2016. Graft failure (GF) was defined as death or return to dialysis or requiring repeat transplant. We compared original KDRI and a race-free KDRI (Black donor coefficient zeroed out in the KDRI formula) with respect to recategorization of perceived GF risk (based on KDPI categories: ≤20, 21-34, 35-85, ≥86)' risk discrimination (using the C statistic) and predictive accuracy (using Brier score), and GF risk prediction (using Cox regression on time-to-GF). We used logistic regression to study the impact of donor race on discard probability.
Results:
There were 10 949 (16.3% of recipients) GF, and 1893 (17% of GFs) were among recipients of kidneys from Black donors. The use of race-free KDRI resulted in reclassification of 49% of kidneys from Black donors into lower GF risk categories. The impact on GF risk discrimination was minimal, with a relative decrease in C statistic of 0.16% and a change in GF predictive accuracy of 0.07%. For a given recipient/donor combination, transplants from Black (compared with non-Black) donors are estimated to decrease predicted graft survival at 1-y by 0.3%-3%, and 5-y by 1%-6%. Kidneys from Black donors are significantly more likely to be discarded (odds ratio adjusted for KDRI except race = 1.24). We estimate that an equal discard probability for Black and non-Black donors would yield 70 additional kidney transplants annually from Black donors.
Conclusions:
Use of race-free KDRI did not impact GF risk discrimination or predictive accuracy and may lower discard of kidneys from Black donors. We recommend use of race-free KDRI calculator acknowledging the possibility of miscalculation of GF risk in small proportion of kidneys from Black donors.
... 26 While grafts with KDPI < 75% are usually considered acceptable for transplantation in most centers, more than 30% of kidneys with KDPI > 75% are discarded because regarded as of insufficient quality. 27 Since KDPI plays an essential role in the acceptance or decline of kidney grafts, 27 the relationship between KDPI and postoperative outcomes requires scrutiny. ...
Background:
High kidney-donor profile index (KDPI) kidneys have a shorter survival than grafts with lower KDPI values. It is still unclear, however, whether their shorter longevity depends on an inferior baseline function, faster functional decline, or the combination of both.
Methods:
We analyzed the estimated glomerular filtration rate (eGFR) of 605 consecutive recipients of deceased donor kidney transplants (KT) at 1, 3, 6, 12, 18, 24, 36, 48, and 60 months. Comparisons were performed among four groups based on KDPI quartile: Group I-KDPI ≤ 25% (n = 151), Group II-KDPI 26-50% (n = 182), Group III-KDPI 51-75% (n = 176), and Group IV-KDPI 〉 75% (n = 96). Linear mixed model analysis was subsequently used to assess whether KDPI was independently associated with the decline in eGFR during the first 5-years after KT. We also analyzed the incidence of delayed graft function (DGF), rejection within the first year after KT, patient survival, graft survival, and death censored graft survival based on KDPI group.
Findings:
High-KDPI grafts had lower eGFR immediately after KT, had a higher incidence of DGF and rejection. However, there were no signifcant differences in the adjusted rate (slope) of decline in eGFR among the four KDPI groups (P = .06). Although patient survival was signigicantly lower for recipients of high-KDPI grafts, death-censored graft survival was similar among the four KDPI groups (P = .33).
Conclusions:
The shorter functional survival of high-KDPI grafts seems to be due to their lower baseline eGFR rather than a more rapid functional decline after KT.
... Research has revealed that the KDPI is highly associated with organ discard rates and that changes in the KDPI "numeric label" itself can make a difference in kidney utilization decisions. 84,85 The incorporation of GS into clinical prediction scores may help reduce discards by tempering the outsized effect this parameter has on transplant decision-making, particularly beyond 10%. 86,87 If clinicians began to rely on new-and-improved, biopsyinformed prediction models for decision-making, knowing that the biopsy findings were already included in an evidence-based way, the unjustifiably high discard rates associated with high GS values ( Figure 4) might also begin to taper. ...
Background
The role of procurement biopsies in deceased donor kidney evaluation is debated in light of uncertainty about the influence of biopsy findings on recipient outcomes. The literature is filled with conflicting and ambiguous findings typically derived from small studies focused on short-term outcomes or reliant on biopsies prepared via methods impractical in the time-sensitive context of organ procurement.
Methods
After manual data entry of DonorNet® attachments from 4,480 extended criteria donors recovered in the U.S. from 2008-2012, we applied causal inference methods in a Cox regression framework to estimate independent effects of glomerulosclerosis, interstitial fibrosis, and vascular changes on long-term kidney graft survival. Kidney discard rates from 2018-2019 were evaluated to characterize contemporary kidney utilization patterns.
Results
Effects of interstitial fibrosis and vascular changes were largely attenuated after adjusting for potentially confounding donor and recipient variables, although conclusions are less certain for severe levels due to smaller sample sizes. By contrast, significant effects of glomerulosclerosis (>10% vs. 0-5%) persisted even after adjustment (all-cause, HR 1.18; 95% CI 1.06, 1.28; death-censored, HR 1.28; 95% CI 1.08, 1.46) but plateaued beyond 10%. By contrast, kidney discard rates increased precipitously as glomerulosclerosis rose above 10%.
Conclusions
Despite being obtained under less than ideal conditions, estimated glomerulosclerosis from a procurement biopsy is independently associated with long-term graft survival, above and beyond standard clinical parameters, in extended criteria donor transplants. However, the disproportionately high likelihood of discard for kidneys with glomerulosclerosis above 10% is unjustified. The outsized effect of glomerulosclerosis on kidney utilization should be tempered and commensurate with its effect on outcomes.
... Organs with a KDPI ≥85%, also known as "High KDPI" organs, are associated with reduced 5-year survival and greater risk of graft failure compared to kidneys with KDPI <85. While these data provide some increased clarity, there is a risk of adverse selection and labeling of organs which may contribute to excess organ discard [6,7]. ...
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.
... A false high index reading in the prediction model at the time of the transplant may discourage the clinician as well as the patient from accepting a donor kidney. This stigmatising effect of erroneously labelling a donor kidney as 'marginal/low quality' has already been documented (320) . ...
Background
Compared to other available renal replacement therapies, such as dialysis (haemo-dialysis and peritoneal-dialysis), renal transplantation is known to improve survival and quality of life of patients with end-stage renal disease. However, donor kidneys are a scarce resource and health systems around the world have not been able to meet the growing demand for kidney grafts. This is evident from the rising numbers of patients awaiting donor kidneys around the world. Transplanting marginal-quality kidneys and allocating donor kidneys to recipients based on the presumed longevity of the kidney graft are two strategies that are being tried to increase the kidney donor pool and meet the demand for donor kidneys. For the strategy of longevity matching, the graft failure risk prediction models are crucial in deciding who is the most suitable recipient.
Objectives
The purpose of this PhD research is to provide information that can assist decision making concerning the most efficient use of donor kidneys. The following objectives were set to achieve this purpose:
1. Review the literature systematically to secure evidence about the cost-effectiveness of transplanting marginal-quality kidneys. This objective produced a manuscript that was published in ‘Cost Effectiveness and Resource Allocation’ journal (Q2 journal)
2. Review the literature systematically to secure evidence about which machine learning methods can be used to predict graft outcomes among kidney transplant patients, and to assess their usefulness as an aid to decision making. This objective produced a manuscript that was published in ‘International Journal of Medical Informatics’ journal (Q1 journal)
3. Determine the cost-effectiveness of transplanting marginal-quality kidneys. This objective produced a manuscript that was published in ‘Value in Health’ journal (Q1 journal)
4. Determine whether the choice of modelling technique has any impact on the cost-effectiveness of transplanting marginal-quality kidneys. This objective produced a manuscript that was published in ‘Health economics review’ journal (Q2 journal)
5. Assess the impact on costs and health outcomes that might eventuate if a ‘longevity matching’ kidney allocation system was adopted by the Australian healthcare system. This objective produced a manuscript that was published in ‘BMC Health Service Research’ journal (Q1 journal)
6. Develop and validate statistical and machine learning predictive models to predict death-censored graft failure following deceased donor kidney transplant, using time-to-event (survival) data from a large national Australian dataset. This objective produced two manuscripts. The protocol was published in ‘F1000Research’ (Q1 journal) and the predictive model was published in ‘BMC Medical Research Methodology’ (Q1 journal).
Methods
Objective 1 (Chapter 2): A systemic review and cost-utility analysis of interventions for chronic kidney disease patients undergoing kidney transplant was carried out using a search of the MEDLINE, CINAHL, EMBASE, PsycINFO and NHS-EED databases. The CHEERS checklist was used to determine the reporting quality of the economic evaluations. The quality of the data used to inform model parameters was determined using the modified hierarchies of data sources.
Objective 2 (Chapter 3): A systemic review of machine learning methods currently used to predict graft outcomes for kidney transplant patients was carried out using searches of databases, including Medline, the Cumulative Index to Nursing and Allied Health Literature, EMBASE, PsycINFO and Cochrane.
Objective 3 (Chapter 4): A decision analytic model to estimate cost-effectiveness was developed using a Markov model. Separate models were developed for four separate kidney quality bands and for patients waiting on dialysis. Models were simulated in one-year cycles for a 20-year time horizon, from a healthcare payer’s perspective. Willingness to pay was A$28,000.
Objective 4 (Chapter 5): The cost-effectiveness of transplanting marginal-quality kidneys as opposed to remaining on dialysis was assessed using the Markov models and discrete event simulations (DES). Parametric survival models were used to estimate the time-dependent transition probabilities of the Markov models and the distribution of time-to-event in the DES models. The Markov models were simulated in 12 and 6 monthly cycles, out to five and 20-year time horizons, for all the transitions through distinct health states. Willingness to pay was A$28,000.
Objective 5 (Chapter 6): A decision analytic model to estimate cost-effectiveness was developed using the Markov model. Four plausible competing allocation options were compared to the current kidney allocation practice. Models were simulated in one-year cycles for a 20-year time horizon, with transitions through distinct health states relevant to the kidney recipient. Willingness to pay was A$28,000.
Objective 6 (Chapter 7 and 8): Risk prediction models were developed with the use of survival tree, random survival forest, survival support vector machine and Cox proportional regression. Data included donor and recipient characteristics (n=98) of 7,365 deceased donor transplants between 1 January 2007 and 31 December 2017, all conducted in Australia. The models were trained using 70% of the data and validated using the remaining data (30%). The model with the best discriminatory power, assessed by the concordance index (C- index), was chosen as the best model.
Results
Objective 1: One publication had assessed the cost-effectiveness of transplanting a marginal-quality kidney, compared to waiting on dialysis. However, it did not assess the cost-effectiveness of transplanting donor organs of differing quality levels, nor did it report on the relative merits of transplanting a marginal donor kidney versus remaining on a waitlist until a superior-quality kidney should become available.
Objective 2: Only one study had modelled time-to-event (survival) information, while all the others had used classification-based machine learning methods. There are inconsistencies in the prediction accuracy of machine learning models compared to traditional predictive methods. However, there are indications that machine learning models do have the potential to improve the prediction of kidney transplant outcomes.
Objective 3: Transplanting a kidney of any quality is cost-effective compared to remaining on a waitlist. Transplanting superior quality kidneys to younger patients and lower quality kidneys to older patients is also cost-effective. Waiting on dialysis in the hope of receiving a higher quality kidney is not a cost-effective strategy for any age group.
Objective 4: Irrespective of the modelling method, the cycle length of the Markov model or the time horizon, transplanting a low-quality kidney as compared to remaining waitlisted was the dominant strategy.
Objective 5: Allocating the worst (lowest estimated graft survival) 20% of donor kidneys to the worst (lowest estimated patient survival) 20% of recipients returned the lowest costs, greatest health benefits and largest gain to net monetary benefits.
Objective 6: Two models, developed using Cox regression and random survival forest, had the highest C-index (0.67) in discriminating death-censored graft failure and combined graft failures (0.62 and 0.61, respectively). The best-fitting Cox model used seven independent variables and showed a moderate level of prediction accuracy (calibration).
Conclusions
Transplanting a marginal-quality kidney was found to be cost-effective as compared to the alternative option of a patient receiving dialysis on a long waitlist. Also, remaining on dialysis in the hope of receiving a superior-quality kidney is not a cost-effective strategy for any age group. If the Australian kidney allocation system can enable marginal-quality kidneys for older recipients, this will increase the usage of marginal-quality kidneys reducing discard rates, and promote the best value for all donated kidneys. Our new index to predict the risk of graft failure demonstrated adequate potential to make pre-transplantation predictions about which recipient(s) will gain the greatest longevity from an available donated kidney.
... A false high index reading in the prediction model at the time of the transplant may discourage the clinician as well as the patient from accepting a donor kidney. This stigmatising effect of erroneously labelling a donor kidney as 'marginal/low quality' has already been documented [39]. Prediction of graft failure is a complex phenomenon, which involves donor characteristics, features related to donor organ retrieval, recipient characteristics, features related to the transplantation, and post-transplantation factors such as the use of immunosuppression drugs. ...
Background
Kidney graft failure risk prediction models assist evidence-based medical decision-making in clinical practice. Our objective was to develop and validate statistical and machine learning predictive models to predict death-censored graft failure following deceased donor kidney transplant, using time-to-event (survival) data in a large national dataset from Australia.
Methods
Data included donor and recipient characteristics (n = 98) of 7,365 deceased donor transplants from January 1st, 2007 to December 31st, 2017 conducted in Australia. Seven variable selection methods were used to identify the most important independent variables included in the model. Predictive models were developed using: survival tree, random survival forest, survival support vector machine and Cox proportional regression. The models were trained using 70% of the data and validated using the rest of the data (30%). The model with best discriminatory power, assessed using concordance index (C-index) was chosen as the best model.
Results
Two models, developed using cox regression and random survival forest, had the highest C-index (0.67) in discriminating death-censored graft failure. The best fitting Cox model used seven independent variables and showed moderate level of prediction accuracy (calibration).
Conclusion
This index displays sufficient robustness to be used in pre-transplant decision making and may perform better than currently available tools.
... However, these 3 factors are highly associated with kidney utilization and are likely to capture the majority of donor-to-donor variation associated with utilization. [15][16][17] To depict risk-adjusted UR trends, we used a method analogous to least-squares means and described as the "mean of the predicted values" approach, which has been used in previous studies. 18 ...
Background and objectives
Kidneys from elderly deceased donors are often discarded after procurement if the expected outcomes from single kidney transplantation are considered unacceptable. An alternative is to consider them for dual kidney transplantation. We aimed to examine the utilization of kidneys from donors aged ≥60 years in the United Kingdom and compare clinical outcomes of dual versus single kidney transplant recipients.
Design, setting, participants, & measurements
Data from the United Kingdom Transplant Registry from 2005 to 2017 were analyzed. We examined utilization rates of kidneys retrieved from deceased donors aged ≥60 years, and 5-year patient and death-censored graft survival of recipients of dual and single kidney transplants. Secondary outcomes included eGFR. Multivariable analyses and propensity score analysis were used to correct for differences between the groups.
Results
During the study period, 7841 kidneys were procured from deceased donors aged ≥60 years, of which 1338 (17%) were discarded; 356 dual and 5032 single kidneys were transplanted. Donors of dual transplants were older (median, 73 versus 66 years; P <0.001) and had higher United States Kidney Donor Risk Indices (2.48 versus 1.98; P <0.001). Recipients of dual transplants were also older (64 versus 61 years; P <0.001) and had less favorable human leukocyte antigen matching ( P <0.001). After adjusting for confounders, dual and single transplants had similar 5-year graft survival (hazard ratio, 0.81; 95% CI, 0.59 to 1.12). No difference in patient survival was demonstrated. Similar findings were observed in a matched cohort with a propensity score analysis method. Median 12-month eGFR was significantly higher in the dual kidney transplant group (40 versus 36 ml/min per 1.73 m ² ; P <0.001).
Conclusions
Recipients of kidneys from donors aged ≥60 years have similar 5-year graft survival and better graft function at 12 months with dual compared with single deceased donor kidney transplants.
... Insights from behavioral science reveal that the precise way complex information is presented can affect decision making through psychologic phenomena such as priming, loss aversion, observational learning, and default effects (11)(12)(13)(14)(15). To enable scientific study of these and other phenomena such as cognitive burden ("information overload") (16,17) and labeling effects (18)(19)(20), in 2016 under its UNOS Labs initiative (21), UNOS developed a DonorNet simulator, SimUNet, that sends hypothetical kidney offers to participating clinicians and receives their acceptance and refusal decisions for analysis. SimUNet was designed to test a broad variety of potential system changes, including the addition of new data, rearranging of data, removal of data, manipulation of data, and other user-interface changes hypothesized to improve decision making. ...
Background
The use of procurement biopsies for assessing kidney quality has been implicated as a driver of the nearly 20% kidney discard rate in the United States. Yet in some contexts, biopsies may boost clinical confidence, enabling acceptance of kidneys that would otherwise be discarded. We leveraged a novel organ offer simulation platform to conduct a controlled experiment isolating biopsy effects on offer acceptance decisions.
Methods
Between November 26 and December 14, 2018, 41 kidney transplant surgeons and 27 transplant nephrologists each received the same 20 hypothetical kidney offers using a crossover design with weekend “washout” periods. Mini-study 1 included four, low serum creatinine (<1.5 mg/dl) donor offers with arguably “poor” biopsy findings that were based on real offers that were accepted with successful 3-year recipient outcome. For each of the four offers, two experimental variants—no biopsy and “good” biopsy—were also sent. Mini-study 2 included four AKI offers with no biopsy, each having an offer variant with “good” biopsy findings.
Results
Among low serum creatinine donor offers, we found approximately threefold higher odds of acceptance when arguably poor biopsy findings were hidden or replaced with good biopsy findings. Among AKI donor offers, we found nearly fourfold higher odds of acceptance with good biopsy findings compared with no biopsy. Biopsy information had profound but variable effects on decision making: more participants appeared to have been influenced by biopsies to rule out, versus rule in, transplantable kidneys.
Conclusions
The current use of biopsies in the United States appears skewed toward inducing kidney discard. Several areas for improvement, including reducing variation in offer acceptance decisions and more accurate interpretation of findings, have the potential to make better use of scarce, donated organs. Offer simulation studies are a viable research tool for understanding decision making and identifying ways to improve the transplant system.
... However, these 3 factors are highly associated with kidney utilization and are likely to capture the majority of donor-to-donor variation associated with utilization. [15][16][17] To depict risk-adjusted UR trends, we used a method analogous to least-squares means and described as the "mean of the predicted values" approach, which has been used in previous studies. 18 ...
In transplantation, meaningful international comparisons in organ utilization are needed. This collaborative study between the UK and US aimed to develop a kidney utilization metric allowing for legitimate inter‐country comparisons. Data from the UK and US transplant registries, including all deceased donor kidneys recovered from 2006‐2017, were analyzed. To identify a potentially comparable kidney utilization rate (UR), several denominators were assessed. We discovered that the proportion of transplanted kidneys from elderly donors in the UK (10.7%) was 18 times greater than in the US (0.6%). Conversely, en‐bloc paediatric kidney transplantation was more common in the US. DCD utilization has risen in both countries but is twice as prevalent in the UK (39% of transplants) vs. the US (20%). In addition, US and UK URs are not directly comparable due to fundamental system differences. However, using a suite of URs revealed practice areas likely to yield the most benefit if improved, for example efforts to increase kidney offer acceptance in the US and to reduce post‐acceptance discard in the UK. Methods employed in this study, including a novel intra‐country risk‐adjusted UR trend logistic regression analyses, can be translated to other international transplant registries in pursuit of further global learning opportunities.
