Reevaluation of the Kidney Donor Risk Index

Zhong, Yingchao; Schaubel, Douglas E.; Kalbfleisch, John D.; Ashby, Valarie B.; Rao, Panduranga S.; Sung, Randall S.

doi:10.1097/tp.0000000000002498

Cited by 28 publications

(29 citation statements)

References 12 publications

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“…We acknowledge that the KDPI is calculated on the basis of allograft failure events, not allograft function, but allograft function is very meaningful to patients and transplant professionals. 42,43 To date, there is insufficient follow-up time for HCV-viremic kidney transplants into seronegative recipients to have sufficient power to examine this outcome.…”

Section: Discussionmentioning

confidence: 99%

National Trends in Utilization and 1-Year Outcomes with Transplantation of HCV-Viremic Kidneys

Potluri¹,

Goldberg²,

Mohan

et al. 2019

JASN

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BackgroundRecent pilot trials have demonstrated the safety of transplanting HCV-viremic kidneys into HCV-seronegative recipients. However, it remains unclear if allograft function is impacted by donor HCV-viremia or recipient HCV-serostatus.MethodsWe used national United States registry data to examine trends in HCV-viremic kidney use between 4/1/2015 and 3/31/2019. We applied advanced matching methods to compare eGFR for similar kidneys transplanted into highly similar recipients of kidney transplants.ResultsOver time, HCV-seronegative recipients received a rising proportion of HCV-viremic kidneys. During the first quarter of 2019, 200 HCV-viremic kidneys were transplanted into HCV-seronegative recipients, versus 69 into HCV-seropositive recipients, while 105 HCV-viremic kidneys were discarded. The probability of HCV-viremic kidney discard has declined over time. Kidney transplant candidates willing to accept a HCV-seropositive kidney increased from 2936 to 16,809 from during this time period. When transplanted into HCV-seronegative recipients, HCV-viremic kidneys matched to HCV-non-viremic kidneys on predictors of organ quality, except HCV, had similar 1-year eGFR (66.3 versus 67.1 ml/min per 1.73 m2, P=0.86). This was despite the much worse kidney donor profile index scores assigned to the HCV-viremic kidneys. Recipient HCV-serostatus was not associated with a clinically meaningful difference in 1-year eGFR (66.5 versus 71.1 ml/min per 1.73 m2, P=0.056) after transplantation of HCV-viremic kidneys.ConclusionsBy 2019, HCV-seronegative patients received the majority of kidneys transplanted from HCV-viremic donors. Widely used organ quality scores underestimated the quality of HCV-viremic kidneys based on 1-year allograft function. Recipient HCV-serostatus was also not associated with worse short-term allograft function using HCV-viremic kidneys.

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Section: Discussionmentioning

confidence: 99%

National Trends in Utilization and 1-Year Outcomes with Transplantation of HCV-Viremic Kidneys

Potluri¹,

Goldberg²,

Mohan

et al. 2019

JASN

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“…Recently, Zhong et al . reevaluated KDRI and reported it to have a concordance of about 0.65 . However, several studies confirmed KDRI as a statistically significant predictor of post‐transplant outcomes.…”

Section: Discussionmentioning

confidence: 99%

Functional status‐based risk–benefit analyses of high‐ KDPI kidney transplant versus dialysis

2019

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Yearly, over half of deceased-donor kidneys with kidney donor profile index (KDPI) > 85 were discarded, yet they could improve survival outcomes for dialysis patients. The potential risk of high-KDPI kidney transplant (KT) depends on the patient's overall health summarized by functional status, which should be examined. The analyzed cohort consisted of adult deceased-donor KT candidates on dialysis listed in 2005-2014. A multivariate Cox proportional hazards model was fitted with functional status, measured using Karnofsky Performance Score (KPS), and transplant status as time-varying covariates. Derived from the Cox model, survival curves were analyzed to compare the survival outcomes between dialysis and transplant with different kidney qualities across three different KPS strata: 10-40, 50-70, and 80-100. With KDPI 0-99 KT, KPS 10-40 patients will survive ≥4.38 years median compared with 3.21 years median if they remained on dialysis. For KPS 50+ patients, the median survival years increase from 5.82 to 6.60 years on dialysis to ≥7.83 years after KDPI < 100 KT. The risk-adjusted analyses suggested that patients are expected to benefit more from KDPI 81-99 KT than from remaining on dialysis.

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“…Prognostic scores currently implemented in transplant medicine mostly predict graft loss, renal function, and the likelihood of transplantation [25][26][27].…”

Section: Prediction Modelmentioning

confidence: 99%

A machine learning prediction model for waiting time to kidney transplant

Silva

Ferreira

Perosa³

et al. 2021

PLoS ONE

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Background Predicting waiting time for a deceased donor kidney transplant can help patients and clinicians to discuss management and contribute to a more efficient use of resources. This study aimed at developing a predictor model to estimate time on a kidney transplant waiting list using a machine learning approach. Methods A retrospective cohort study including data of patients registered, between January 1, 2000 and December 31, 2017, in the waiting list of São Paulo State Organ Allocation System (SP-OAS) /Brazil. Data were randomly divided into two groups: 75% for training and 25% for testing. A Cox regression model was fitted with deceased donor transplant as the outcome. Sensitivity analyses were performed using different Cox models. Cox hazard ratios were used to develop the risk-prediction equations. Results Of 54,055 records retrieved, 48,153 registries were included in the final analysis. During the study period, approximately 1/3 of the patients were transplanted with a deceased donor. The major characteristics associated with changes in the likelihood of transplantation were age, subregion, cPRA, and frequency of HLA-DR, -B and -A. The model developed was able to predict waiting time with good agreement in internal validation (c-index = 0.70). Conclusion The kidney transplant waiting time calculator developed shows good predictive performance and provides information that may be valuable in assisting candidates and their providers. Moreover, it can significantly improve the use of economic resources and the management of patient care before transplant.

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Reevaluation of the Kidney Donor Risk Index

Cited by 28 publications

References 12 publications

National Trends in Utilization and 1-Year Outcomes with Transplantation of HCV-Viremic Kidneys

National Trends in Utilization and 1-Year Outcomes with Transplantation of HCV-Viremic Kidneys

Functional status‐based risk–benefit analyses of high‐ KDPI kidney transplant versus dialysis

A machine learning prediction model for waiting time to kidney transplant

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