External Validation of the Estimated Posttransplant Survival Score for Allocation of Deceased Donor Kidneys in the United States

Clayton, Philip; McDonald, S.; Snyder, Jon J.; Salkowski, Nicholas; Chadban, Stephen J

doi:10.1111/ajt.12761

Cited by 78 publications

(60 citation statements)

References 4 publications

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“…ability to provide the correct estimates for the probability of an adverse outcome). Existing models include the Estimated Post-Transplant Survival (EPTS) score 35 which accounts for only age and limited comorbidities (diabetes, length of time on dialysis, and prior transplant), and various models including measures of frailty 9,11,32 . In the most discriminating of these models 9 (based on days of hospitalizations in the first year of waitlist), recipients in the highest category of risk have 3-year death or graft loss rates of 30%: whether such a failure rate warrants proceeding with kidney transplant is a decision to be made at the individual level by shared decision making.…”

Section: Frailty and Transplant Candidacymentioning

confidence: 99%

Implications of Frailty for Peritransplant Outcomes in Kidney Transplant Recipients

et al. 2019

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Purpose of Review Research over the past few decades points to the importance of frailty, or the lack of physiologic reserve, in the natural history of chronic diseases and in modifying the impact of potential interventions. End-stage kidney disease (ESKD) and the intervention of kidney transplantation are no exception. We review the recent epidemiologic and cohort-based evidence on the association between frailty and kidney transplant outcomes and provide a framework of questions with which to approach future research endeavors and clinical practice. Recent Findings Frailty in kidney transplant candidates can be measured in numerous ways, including descriptive phenotype, description scores, functional testing, and surrogate measures. Regardless of the metric, the presence of frailty is strongly associated with inferior pre- and posttransplant outcomes compared to the absence of frailty. However, some frail patients with ESKD can benefit from transplant over chronic dialysis. Evidence-based approaches for identifying frail ESKD patients who can benefit from transplant over dialysis, with acceptable posttransplant outcomes, are lacking. Interventional trials to improve frailty and physical function before transplant (prehabilitation) and after transplant (rehabilitation) are also lacking. Conclusion Frailty is increasingly recognized as highly relevant to peritransplant outcomes, but more work is needed to: 1) tailor management to the unique needs of frail patients, both pre- and posttransplant; 2) define phenotypes of frail patients who are expected to benefit from transplant over dialysis; and 3) develop interventions to reverse frailty, both pre- and post-transplant.

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Section: Frailty and Transplant Candidacymentioning

confidence: 99%

Implications of Frailty for Peritransplant Outcomes in Kidney Transplant Recipients

et al. 2019

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“…56 Most importantly, our prediction model has yet to be externally validated in other cohorts. To the best of our knowledge, only few previously developed prediction scores were externally validated, 35,38,39,57 and only 2 these scores was validated in a different center. 39,57 Moreover, neither of these externally validated scores were developed and validated in patients in the United States at the 21 st century.…”

Section: Discussionmentioning

confidence: 99%

“…To the best of our knowledge, only few previously developed prediction scores were externally validated, 35,38,39,57 and only 2 these scores was validated in a different center. 39,57 Moreover, neither of these externally validated scores were developed and validated in patients in the United States at the 21 st century. Externally validating our score in other cohorts at both the multi-center and individual center level is necessary to ensure the applicability, reliability, and utility of our prediction model for use in potential kidney transplant recipient patients.…”

Section: Discussionmentioning

confidence: 99%

Predictive Score for Posttransplantation Outcomes

et al. 2017

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Background Most current scoring tools to predict allograft and patient survival upon kidney transplantion(Tx) are based on variables collected posttransplantation. We developed a novel score to predict posttransplant outcomes using pretransplant information including routine laboratory data available prior to or at the time of transplantation. Methods Linking the 5-year patient data of a large dialysis organization to the SRTR, we identified 15,125 hemodialysis patients who underwent first deceased Tx. Prediction models were developed using Cox models for (a)mortality, (b)allograft loss(death censored) and (c)combined death or transplant failure. The cohort was randomly divided into a two-thirds set(Nd=10,083) for model development and a one-third set(Nv=5,042) for validation. Model predictive discrimination was assessed using the index of concordance, or C statistic, which accounts for censoring in time-to-event models(a–c). We used the bootstrap method to assess model overfitting and calibration using the development dataset. Results Patients were 50±13 years old and included 39% women, 15% African-Americans and 36% diabetics. For prediction of post-transplant mortality and graft loss, 10 predictors were used (recipients’ age, cause and length of ESRD, hemoglobin, albumin, selected comorbidities, race and type of insurance as well as donor age, diabetes status, extended criteria donor kidney(ECD), and number of HLA mismatches). The new model (www.TransplantScore.com) showed the overall best discrimination(C-statistics:0.70(95%CI: 0.67–0.73) for mortality;0.63(95%CI: 0.60–0.66) for graft failure;0.63(95%CI: 0.61–0.66) for combined outcome). Conclusions The new prediction tool, using data available prior to the time of transplantation, predicts relevant clinical outcomes and may perform better to predict patients’ graft survival than currently used tools.

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“…Secondary outcomes included mean recipient age and mean expected posttransplant survival (EPTS) score, by KDPI groupings. EPTS was calculated according to the following formula and scaled to a reference population of all patients active on the Australian deceased donor transplant waiting list at December 31, 2014.…”

Section: Methodsmentioning

confidence: 99%

Insights into the labeling effect of Kidney Donor Performance Index reporting: The Australian experience

Sypek

Hughes

Holdsworth

et al. 2020

American Journal of Transplantation

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In 2016, Australia began reporting the Kidney Donor Performance Index (KDPI) with all deceased donor kidney transplant offers despite this not being used in organ allocation rules, offering a unique opportunity to explore the “labeling effect” of KDPI reporting. We reviewed all kidneys retrieved for transplant in Australia from 2015 to 2018 and analyzed the association of KDPI reporting with organ nonutilization, number of offer declines, and donor/recipient age and longevity matching. Analyses were stratified by organ failure risk: higher risk (KDPI > 80%), standard risk (KDPI 20% to 79%), and lower risk (KDPI 0% to 20%). There was no significant difference in organ nonutilization post KDPI reporting either overall or for higher‐risk kidneys. KDPI reporting was associated with an increase in offer declines for both higher‐risk (incidence risk ratio 1.45, P = .007) and standard‐risk (incidence risk ratio 1.22, P = .021) kidneys but not for lower‐risk organs. There was a significant increase in recipient age and expected posttransplant survival score for higher‐risk kidneys but no differences among other groups. We conclude that although KDPI reporting in Australia has been associated with an increased number of offer declines for higher‐risk kidneys, this has not resulted in increased nonutilization and may have contributed to more appropriate use of these organs.

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External Validation of the Estimated Posttransplant Survival Score for Allocation of Deceased Donor Kidneys in the United States

Cited by 78 publications

References 4 publications

Implications of Frailty for Peritransplant Outcomes in Kidney Transplant Recipients

Implications of Frailty for Peritransplant Outcomes in Kidney Transplant Recipients

Predictive Score for Posttransplantation Outcomes

Insights into the labeling effect of Kidney Donor Performance Index reporting: The Australian experience

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