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

Riley, Stephanie; Zhang, Qing; Tse, Wai-Yee; Connor, Andrew; Wei, Yinghui

doi:10.3389/ti.2022.10397

Cited by 6 publications

(3 citation statements)

References 47 publications

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“…The current prediction models of graft outcome at the time of transplantation, however, have been criticized for being inaccurate and biased to disregard competing risks and the effect of allocation policy between donor and recipients. 6,13 KDPI utilization, while designed to counteract the nonuse of higherrisk transplants, has caused concern on the labeling effect of "high-KDPI" kidneys which are often at risk of nonuse. 10 High KDPI, long cold ischemia time, high creatinine level, and unfavorable biopsy results can all lead to organ nonutilization.…”

Section: Discussionmentioning

confidence: 99%

Preoperative Risk Assessment of Early Kidney Graft Loss

Eerola,

Sallinen,

Lyden

et al. 2024

Transplantation Direct

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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.

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Section: Discussionmentioning

confidence: 99%

Preoperative Risk Assessment of Early Kidney Graft Loss

Eerola,

Sallinen,

Lyden

et al. 2024

Transplantation Direct

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“…Analogously, deceased donor kidneys vary substantially along a quality spectrum that portends highly differential expected graft longevity depending on donor age, medical history, etc . The decision to accept any particular kidney for any specific patient involves consideration of two key risks—graft failure and disease transmission (15)—along with tremendous uncertainty in how things will turn out for any given case (16,17), and it may very well be the most complex decision in all of medicine (18).…”

Section: Three Fundamental Realitiesmentioning

confidence: 99%

Oversimplification and Misplaced Blame Will Not Solve the Complex Kidney Underutilization Problem

2022

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“…We sought to validate the predictive performance of the KDRI in the UK kidney transplantation population. In our systematic review [6], we found that the KDRI has been validated in different populations across the globe [7][8][9][10][11][12][13][14][15]. In the UK, Watson et al [14] assessed its performance in transplants performed between 2000 and 2007.…”

Section: Introductionmentioning

confidence: 98%

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

Riley,

Tam,

Tse

et al. 2023

Diagn Progn Res

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Background Transplantation represents the optimal treatment for many patients with end-stage kidney disease. When a donor kidney is available to a waitlisted patient, clinicians responsible for the care of the potential recipient must make the decision to accept or decline the offer based upon complex and variable information about the donor, the recipient and the transplant process. A clinical prediction model may be able to support clinicians in their decision-making. The Kidney Donor Risk Index (KDRI) was developed in the United States to predict graft failure following kidney transplantation. The survival process following transplantation consists of semi-competing events where death precludes graft failure, but not vice-versa. Methods We externally validated the KDRI in the UK kidney transplant population and assessed whether validation under a semi-competing risks framework impacted predictive performance. Additionally, we explored whether the KDRI requires updating. We included 20,035 adult recipients of first, deceased donor, single, kidney-only transplants between January 1, 2004, and December 31, 2018, collected by the UK Transplant Registry and held by NHS Blood and Transplant. The outcomes of interest were 1- and 5-year graft failure following transplantation. In light of the semi-competing events, recipient death was handled in two ways: censoring patients at the time of death and modelling death as a competing event. Cox proportional hazard models were used to validate the KDRI when censoring graft failure by death, and cause-specific Cox models were used to account for death as a competing event. Results The KDRI underestimated event probabilities for those at higher risk of graft failure. For 5-year graft failure, discrimination was poorer in the semi-competing risks model (0.625, 95% CI 0.611 to 0.640;0.611, 95% CI 0.597 to 0.625), but predictions were more accurate (Brier score 0.117, 95% CI 0.112 to 0.121; 0.114, 95% CI 0.109 to 0.118). Calibration plots were similar regardless of whether the death was modelled as a competing event or not. Updating the KDRI worsened calibration, but marginally improved discrimination. Conclusions Predictive performance for 1-year graft failure was similar between death-censored and competing event graft failure, but differences appeared when predicting 5-year graft failure. The updated index did not have superior performance and we conclude that updating the KDRI in the present form is not required.

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

Cited by 6 publications

References 47 publications

Preoperative Risk Assessment of Early Kidney Graft Loss

Preoperative Risk Assessment of Early Kidney Graft Loss

Oversimplification and Misplaced Blame Will Not Solve the Complex Kidney Underutilization Problem

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

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