Validating Prediction Models of Kidney Transplant Outcome Using Single Center Data

A pre-transplantation risk assessment tool for graft survival in Dutch pediatric kidney recipients

Background

A prediction model for graft survival including donor and recipient characteristics could help clinical decision-making and optimize outcomes. The aim of this study was to develop a risk assessment tool for graft survival based on essential pre-transplantation parameters.

Methods

The data originated from the national Dutch registry (NOTR; Nederlandse OrgaanTransplantatie Registratie). A multivariable binary logistic model was used to predict graft survival, corrected for the transplantation era and time after transplantation. Subsequently, a prediction score was calculated from the β-coefficients. For internal validation, derivation (80%) and validation (20%) cohorts were defined. Model performance was assessed with the area under the curve (AUC) of the receiver operating characteristics curve, Hosmer-Lemeshow test and calibration plots.

Results

In total, 1428 transplantations were performed. Ten-year graft survival was 42% for transplantations before 1990, which has improved to the current value of 92%. Over time, significantly more living and pre-emptive transplantations have been performed and overall donor age has increased (P < .05).The prediction model included 71 829 observations of 554 transplantations between 1990 and 2021. Other variables incorporated in the model were recipient age, re-transplantation, number of human leucocyte antigen (HLA) mismatches and cause of kidney failure. The predictive capacity of this model had AUCs of 0.89, 0.79, 0.76 and 0.74 after 1, 5, 10 and 20 years, respectively (P < .01). Calibration plots showed an excellent fit.

Conclusions

This pediatric pre-transplantation risk assessment tool exhibits good performance for predicting graft survival within the Dutch pediatric population. This model might support decision-making regarding donor selection to optimize graft outcomes.

Trial registration

ClinicalTrials.gov Identifier: NCT05388955.

Risk Factors for 1-Year Graft Loss After Kidney Transplantation: Systematic Review and Meta-Analysis

BACKGROUND AND OBJECTIVES

With expansion of the pool of kidney grafts, through the use of higher-risk donors, and increased attention to donor management strategies, the 1-year graft survival rate is subject to change. It is, therefore, useful to elucidate 1-year graft survival rates by dissecting the characteristics of the low-risk and high-risk kidney transplant cases. The objective of our study was to evaluate factors purported to influence the risk of 1-year graft loss in kidney transplant recipients.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We searched bibliographic databases from 2000 to 2017 and included observational studies that measured the association between donor, recipient, the transplant operation, or early postoperative complications, and 1-year death-censored graft loss.

RESULTS

We identified 35 eligible primary studies, with 20 risk factors amenable to meta-analysis. Six factors were associated with graft loss, with moderate to high degree of certainty: donor age (hazard ratio [HR], 1.11 per 10-year increase; 95% confidence interval [95% CI], 1.04 to 1.18), extended criteria donors (HR, 1.35; 95% CI, 1.28 to 1.42), deceased donors (HR, 1.54; 95% CI, 1.32 to 1.82), number of HLA mismatches (HR, 1.08 per one mismatch increase; 95% CI, 1.07 to 1.09), recipient age (HR, 1.17 per 10-year increase; 95% CI, 1.09 to 1.25), and delayed graft function (HR, 1.89; 95% CI, 1.46 to 2.47) as risk factors for 1-year graft loss. Pooled analyses also excluded, with a high degree of certainty, any associations of cold ischemia time, recipient race, pretransplant body mass index, diabetes, and hypertension with 1-year graft loss.

CONCLUSIONS

Recipient age, donor age, standard versus extended criteria donor, living versus deceased donor, HLA mismatch, and delayed graft function all predicted 1-year graft survival. The effect of each risk factor is small.

Predicting quality of deceased donor kidneys: the harvesting surgeon as a prognostic factor?

INTRODUCTION

The quality of donor organs is a crucial factor with regard to graft survival and function in kidney transplant recipients. The prognostic importance of surgeon-related factors during organ harvesting on graft quality has been almost unknown. Our aim was to find out whether surgical expertise as reflected by the time required for kidney retrieval influences graft survival.

METHODS

In this retrospective study, we analyzed the records of 200 patients who received a cadaveric renal graft at our institution between 2000 and 2005. Graft survival and function were examined at discharge and after 1, 2, 3, and 5 years post-transplantation with the estimated glomerular filtration rate (GFR) using the Cockroft-Gault formula as a surrogate marker. We gathered the pertinent data on harvesting procedures from Eurotransplant donor reports. We correlated the length of time from cold organ perfusion to nephrectomy with graft survival. Statistical evaluation was performed using correlation analysis.

RESULTS

There was no statistically significant correlation between the time the surgeon needed for kidney retrieval (starting from cold perfusion) and the outcome of transplantation.

CONCLUSION

It would seem to be obvious that the longer a cadaveric donor kidney remains in the donor's body after cold perfusion, the worse the outcome will be. Our findings, however, did not prove this hypothesis even when looking at abdominal and combined abdominal and thoracic harvesting procedures separately.

Predicting three-year kidney graft survival in recipients with systemic lupus erythematosus

Predicting the outcome of kidney transplantation is important in optimizing transplantation parameters and modifying factors related to the recipient, donor, and transplant procedure. As patients with end-stage renal disease (ESRD) secondary to lupus nephropathy are generally younger than the typical ESRD patients and also seem to have inferior transplant outcome, developing an outcome prediction model in this patient category has high clinical relevance. The goal of this study was to compare methods of building prediction models of kidney transplant outcome that potentially can be useful for clinical decision support. We applied three well-known data mining methods (classification trees, logistic regression, and artificial neural networks) to the data describing recipients with systemic lupus erythematosus (SLE) in the US Renal Data System (USRDS) database. The 95% confidence interval (CI) of the area under the receiver-operator characteristic curves (AUC) was used to measure the discrimination ability of the prediction models. Two groups of predictors were selected to build the prediction models. Using input variables based on Weka (a open source machine learning software) supplemented with additional variables of known clinical relevance (38 total predictors), the logistic regression performed the best overall (AUC: 0.74, 95% CI: 0.72-0.77)-significantly better (p < 0.05) than the classification trees (AUC: 0.70, 95% CI: 0.67-0.72) but not significantly better (p = 0.218) than the artificial neural networks (AUC: 0.71, 95% CI: 0.69-0.73). The performance of the artificial neural networks was not significantly better than that of the classification trees (p = 0.693). Using the more parsimonious subset of variables (six variables), the logistic regression (AUC: 0.73, 95% CI: 0.71-0.75) did not perform significantly better than either the classification tree (AUC: 0.70, 95% CI: 0.68-0.73) or the artificial neural network (AUC: 0.73, 95% CI: 0.70-0.75) models. We generated several models predicting 3-year allograft survival in kidney transplant recipients with SLE that potentially can be used in practice. The performance of logistic regression and classification tree was not inferior to more complex artificial neural network. Prediction models may be used in clinical practice to identify patients at risk.