Does the Kidney Donor Profile Index (KDPI) predict graft and patient survival in a Spanish population?

Calvillo-Arbizu, Jorge; Pérez-Valdivia, M.Á.; Gentil-Govantes, M.A.; Castro-de-la-Nuez, Pablo; Mazuecos-Blanca, Auxiliadora; Rodríguez‐Benot, Alberto; Gracia-Guindo, María C.; Utiel, Francisco José Borrego; Cabello-Díaz, Mercedes; Bedoya-Pérez, Rafael; Alonso-Gil, Manuel; Salgueira-Lazo, Mercedes; Roa-Romero, Laura M.

doi:10.1016/j.nefroe.2018.06.014

Cited by 7 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar results were obtained by Peters-Sengers et al, reporting 5-year mortality C-statistic for Kidney Donor Risk Index (including deaths after graft loss) of 0.68 30 . Calvillo-Arbizu et al also suggested that KDPI could constitute a potential indicator of patient survival, especially for recipients older than 60 years 31 . However, in another study with Spanish transplant patients no relationship was found between KDPI score and recipient death 17 .…”

Section: Discussionmentioning

confidence: 99%

Validation of a survival benefit estimator tool in a cohort of European kidney transplant recipients

Coca

Arias-Cabrales

Valencia

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Pre-transplant prognostic scores help to optimize donor/recipient allocation and to minimize organ discard rates. Since most of these scores come from the US, direct application in non-US populations is not advisable. The Survival Benefit Estimator (SBE), built upon the Estimated Post-Transplant Survival (EPTS) and the Kidney Donor Profile Index (KDPI), has not been externally validated. We aimed to examine SBE in a cohort of Spanish kidney transplant recipients. We designed a retrospective cohort-based study of deceased-donor kidney transplants carried out in two different Spanish hospitals. Unadjusted and adjusted Cox models were applied for patient survival. Predictive models were compared using Harrell’s C statistics. SBE, EPTS and KDPI were independently associated with patient survival (p ≤ 0.01 in all models). Model discrimination measured with Harrell’s C statistics ranged from 0.57 (KDPI) to 0.69 (SBE) and 0.71 (EPTS). After adjustment, SBE presented similar calibration and discrimination power to that of EPTS. SBE tended to underestimate actual survival, mainly among high EPTS recipients/high KDPI donors. SBE performed acceptably well at discriminating post-transplant survival in a cohort of Spanish deceased-donor kidney transplant recipients, although its use as the main allocation guide, especially for high KDPI donors or high EPTS recipients requires further testing.

show abstract

Section: Discussionmentioning

confidence: 99%

Validation of a survival benefit estimator tool in a cohort of European kidney transplant recipients

Coca

Arias-Cabrales

Valencia

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In our study, the median KDPI score was 95%, but one-year graft survival was 89.8% and 85.4% at 5 years, which is higher than reported in other series with a lower KDPI [ 31 ]. Notwithstanding, the KDPI is a tool developed by the American Transplantation Registry, so its usefulness in Spain is limited by the difficulty to extrapolate this score to other countries with different healthcare systems and transplant programs [ 6 , 9 ].…”

Section: Discussionmentioning

confidence: 99%

“…It gives a score from 0% to 100% which summarizes the risk of graft failure and it is calculated using 10 donor factors [ 5 ]. KDPI is not validated in Spain, but some publications in our country relate the KDPI to renal graft survival [ 6 - 9 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Expanded Criteria Donors Using the Kidney Donor Profile Index and the Preimplantation Renal Biopsy

Villanego¹,

Vigara²,

Cazorla³

et al. 2022

Transpl Int

View full text Add to dashboard Cite

The increasing comorbidity of kidney transplant (KT) donors make it necessary to develop scores to correctly assess the quality of kidney grafts. This study analyzes the usefulness of the preimplantation biopsy and the Kidney Donor Profile Index (KDPI) as indicators of KT survival from expanded criteria donors (ECD). Retrospective study of KT in our center between January 2010 to June 2019 who received a kidney from an ECD and underwent a preimplantation biopsy. 266 KT were included. Graft survival was categorized by KDPI quartiles: Q1 = 86%, Q2 = 95%, Q3 = 99% and Q4 = 100%. KT from KDPI Q1 presented better survival (p = 0.003) and Q4 donors had worse renal function (p = 0.018) and poorer glomerular filtration rate (3rd month; p = 0.017, 1st year; p = 0.010). KT survival was analyzed according to KDPI quartile and preimplantation biopsy score simultaneously: Q1 donors with biopsy score ≤3 had the best survival, especially comparing against Q3 with a biopsy score >3 and Q4 donors (p = 0.014). In multivariable analysis, hyaline arteriopathy, glomerulosclerosis, and KDPI Q4 were predictors for graft survival. High KDPI and a greater histological injury in the preimplantation biopsy, especially glomerular and vascular lesions, were related to a higher rate of KT loss from ECD.

show abstract

“…However, models for death-censored graft failure should have ideally considered the semi-competing events graft failure and death. Calvillo-Arbizu et al ( 20 ) noted that death with a functioning graft is a competing event for graft failure but used this as part of the exclusion criteria. Methods such as Fine and Gray ( 44 ) and multistate models ( 45 ) can be used to account for semi-competing events without discarding the data.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Does the Kidney Donor Profile Index (KDPI) predict graft and patient survival in a Spanish population?

Cited by 7 publications

References 19 publications

Validation of a survival benefit estimator tool in a cohort of European kidney transplant recipients

Validation of a survival benefit estimator tool in a cohort of European kidney transplant recipients

Evaluation of Expanded Criteria Donors Using the Kidney Donor Profile Index and the Preimplantation Renal Biopsy

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

Contact Info

Product

Resources

About