A Simple Tool to Predict Outcomes After Kidney Transplant

Kasiske, Bertram L.; Israni, Ajay K.; Snyder, Jon J.; Skeans, M. A.; Peng, Yi; Weinhandl, Eric D.

doi:10.1053/j.ajkd.2010.06.020

Cited by 78 publications

(98 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Existing prediction models look at dialysis survival until transplantation [1], patient survival on the transplant waiting list [2-5], patient survival after transplantation [6,7], or focus on a specific patient group in which differences in treatment modality are less likely [8]. However, none of the available predication models focus on survival for the complete group of incident RRT patients, taking into account survival after dialysis combined with survival after a possible transplantation.…”

Section: Introductionmentioning

confidence: 99%

Survival prognosis after the start of a renal replacement therapy in the Netherlands: a retrospective cohort study

Hemke

Heemskerk²,

Diepen

et al. 2013

BMC Nephrol

View full text Add to dashboard Cite

BackgroundThere is no single model available to predict the long term survival for patients starting renal replacement therapy (RRT). The available models either predict survival on dialysis until transplantation, survival on the transplant waiting list, or survival after transplantation. The aim of this study was to develop a model that includes dialysis survival and survival after an eventual transplantation.MethodsFrom the Dutch renal replacement registry, patients of 16 years of age or older were included if they started RRT between 1995 and 2005, still underwent RRT at baseline (90 days after the start of RRT) and were not registered at a non-renal organ transplant waiting list (N = 13868). A prediction model of 10-year patient survival after baseline was developed through multivariate Cox regression analysis, in one half of the research group. Age at start, sex, primary renal disease (PRD) and therapy at baseline were included as possible predictors. A sensitivity analysis has been performed to determine whether listing on the transplant waiting list should be added. The predictive performance of the model was internally validated. Calibration and discrimination were computed in the other half of the research group. Another sensitivity analysis was to assess whether the outcomes differed if the model was developed and tested in two geographical regions, which were less similar than the original development and validation group. No external validation has been performed.ResultsSurvival probabilities were influenced by age, sex, PRD and therapy at baseline (p < 0.001). The calibration and discrimination both showed very reasonable results for the prediction model (C-index = 0.720 and calibration slope for the prognostic index = 1.025, for the 10 year survival). Adding registration on the waiting list for renal transplantation as a predictor did not improve the discriminative power of the model and was therefore not included in the model.ConclusionsWith the presented prediction model, it is possible to give a reasonably accurate estimation on the survival chances of patients who start with RRT, using a limited set of easily available data.

show abstract

Section: Introductionmentioning

confidence: 99%

Survival prognosis after the start of a renal replacement therapy in the Netherlands: a retrospective cohort study

Hemke

Heemskerk²,

Diepen

et al. 2013

BMC Nephrol

View full text Add to dashboard Cite

show abstract

“…Good performance should facilitate implementation, whereas inadequate performance should be alleviated by adjusting the model. In the recent article by Kasiske et al, 37 authors validated their prediction model on several local datasets extracted from the USRDS. That might be one approach before implementation.…”

Section: Discussionmentioning

confidence: 99%

Validating Prediction Models of Kidney Transplant Outcome Using Single Center Data

Tang¹,

Hurdle²,

Poynton³

et al. 2011

ASAIO Journal

View full text Add to dashboard Cite

Prediction of kidney transplant outcome represents an important and clinically relevant problem. Although several prediction models have been proposed based on large, national collections of data, their utility at the local level (where local data distributions may differ from national data) remains unclear. We conducted a comparative analysis that modeled the outcome data of transplant recipients in the national US Renal Data System (USRDS) against a representative local transplant dataset at the University of Utah Health Sciences Center, a regional transplant center. The performance of an identical set of prediction models was evaluated on both national and local data to assess how well national models reflect local outcomes. Compared with the USRDS dataset, several key characteristics of the local dataset differed significantly (e.g., a much higher local graft survival rate; a much higher local percentage of white donors and recipients; and a much higher proportion of living donors). This was reflected in statistically significant differences in model performance. The area under the receiver operating characteristic curve values of the models predicting 1, 3, 5, 7, and 10-year graft survival on the USRDS data were 0.59, 0.63, 0.76, 0.91, and 0.97, respectively. In contrast, in the local dataset, these values were 0.54, 0.58, 0.58, 0.61, and 0.70, respectively. Prediction models trained on a national set of data from the USRDS performed better in the national dataset than in the local data. This might be due to the differences in the data characteristics between the two datasets, suggesting that the wholesale adoption of a prediction model developed on a large national dataset to guide local clinical practice should be done with caution.

show abstract

“…Too many variables do play a role before, during, and after preservation and probably the best way to assess a kidney is to consider donor characteristics, macroscopic evaluation, histology, HMP parameters, and perfusate biomarkers all together. A comprehensive score taking into account all these information can more likely find its place as a reliable albeit not definitive toll for helping clinicians discard not viable organs, chose the right kidney for the right recipient, and select the most appropriate immunosuppressive treatment after transplantation [32][33][34][35].…”

Section: Organ Assessmentmentioning

confidence: 99%

Hypothermic Machine Perfusion in Kidney Transplantation: Back to the Future?

Ferraresso¹,

Favi²

2015

J Transplant Technol Res

View full text Add to dashboard Cite

Static cold storage is currently the most used method of organ preservation worldwide. However, cutting edge technology and dramatic changes in the donor pattern have lately renewed the interest toward hypothermic machine perfusion. Marginal and cardiac death donors show higher rates of primary non function and delayed graft function compared to standard criteria donors. In this setting, machine perfusion may offer several theoretical advantages such as improved organ preservation, continuous graft evaluation, and ex-vivo conditioning of the graft before implantation. These topics have been recently reassessed by several studies. In particular, perfusion characteristics (renal resistance) and perfusate biomarker concentrations (lactate dehydrogenase, aspartate aminotransferase, heart-type fatty acid binding protein, and IL-18) during machine preservation, proved to be reliable tools to rule out graft viability and predict outcomes after transplantation. Treatment strategies acting on tissue repair, cell metabolism, and allorecognition pathway are also under investigation with promising results. Machine perfusion has finally shown its real potential however, stronger evidences and updated cost-effectiveness analysis are needed to fully support its role for the next future.

show abstract

A Simple Tool to Predict Outcomes After Kidney Transplant

Cited by 78 publications

References 25 publications

Survival prognosis after the start of a renal replacement therapy in the Netherlands: a retrospective cohort study

Survival prognosis after the start of a renal replacement therapy in the Netherlands: a retrospective cohort study

Validating Prediction Models of Kidney Transplant Outcome Using Single Center Data

Hypothermic Machine Perfusion in Kidney Transplantation: Back to the Future?

Contact Info

Product

Resources

About