Effect of Delayed Graft Function on the Outcome and Allograft Survival of Kidney Transplanted Patients from a Deceased Donor

Integrating Clinical and Histopathological Data to Predict Delayed Graft Function in Kidney Transplant Recipients Using Machine Learning Techniques

Background: Given the significant impact of delayed graft function (DGF) on transplant outcomes, the aim of this study was to develop and validate machine learning (ML) models capable of predicting the risk of DGF in deceased-donor kidney transplantation (DDKT). Methods: This retrospective cohort study was conducted using clinical and histopathological data collected between 2018 and 2022 at Ramathibodi Hospital from DDKT donors, recipients, and post-implantation time-zero kidney biopsy samples to develop predictive models. The performance of three ML models (neural network, random forest, and extreme gradient boosting [XGBoost]) and traditional logistic regression on an independent test data set was evaluated using the area under the receiver operating characteristic curve (AUROC) and Brier score calibration. Results: Among 354 DDKT recipients, 64 (18.1%) experienced DGF. The key contributing factors included a donor body mass index > 23 kg/m2, donor diabetes mellitus, a prolonged cold ischemia time, a male recipient, and an interstitial fibrosis/tubular atrophy score of 2-3 in the time-zero kidney biopsy sample. The random forest model had a specificity of 99.96% and an AUROC of 0.9323, the neural network model had a specificity of 97.43% and an AUROC of 0.844, and the XGBoost model had a specificity of 99.81% and an AUROC of 0.989. A traditional statistical model had a specificity of 84.4% and an AUROC of 0.769. Conclusions: Predictive models, especially XGBoost models, have potential as tools for assessing DGF risk post-DDKT, guiding acceptance decisions, and avoiding risky biopsy, and they may be crucial in resource-limited settings.

Impact of donor expanded criteria kidney transplantation on clinical outcomes and survival: A single-center experience

INTRODUCTION

The scarcity of suitable donor organs has led to the inclusion of Expanded Criteria Donor (ECD) kidneys to augment the donor pool, despite potential concerns regarding post-transplant outcomes.

METHODS

This retrospective study analyzed the clinical outcomes of a cohort of 317 kidney transplant recipients from deceased donors at a single center between 2008 and 2018. Patients were categorized into ECD and Standard Criteria Donor (SCD) groups, with primary nonfunctioning grafts excluded. Comprehensive laboratory evaluations were conducted, including HLA typing and serum creatinine levels. Immunosuppressive regimens were standardized, and statistical analyses were performed using the SPSS program.

RESULTS

The sample consisted of 83 (26.18%) patients who received kidney transplants from ECDs and 234 (73.82%) from SCDs. The ECD group showed a longer cold ischemia time (p = 0.019) and a higher rate of delayed graft function (DGF) compared with the SCD group. No significant differences were observed in graft survival (p = 0.370) or patient survival (p = 0.993) between the ECD and SCD groups. However, differences in graft survival were noted between the groups when stratified by DGF status: ECD with DGF vs. ECD without DGF (p = 0.029), ECD with DGF vs. SCD with DGF (p = 0.188), ECD with DGF vs. SCD without DGF (p = 0.022), ECD without DGF vs. SCD with DGF (p = 0.014), ECD without DGF vs. SCD without DGF (p = 0.340), and SCD with DGF vs. SCD without DGF (p = 0.195). No differences in patient survival rates were observed among these groups for all pairwise comparisons (p > 0.05) when stratified by donor criteria and DGF status.

CONCLUSIONS

Graft and patient survival rates were comparable between ECD and SCD kidney transplant recipients.

Predicting kidney allograft survival with explainable machine learning

INTRODUCTION

Despite significant progress over the last decades in the survival of kidney allografts, several risk factors remain contributing to worsening kidney function or even loss of transplants. We aimed to evaluate a new machine learning method to identify these variables which may predict the early graft loss in kidney transplant patients and to assess their usefulness for improving clinical decisions.

MATERIAL AND METHODS

A retrospective cohort study was carried out with 627 kidney transplant patients followed at least three months. All these data were pre-processed, and their selected features were used to develop an automatically working a machine learning algorithm; this algorithm was then applied for training and parameterization of the model; and finally, the tested model was then used for the analysis of patients' features that were the most impactful for the prediction of clinical outcomes. Our models were evaluated using the Area Under the Curve (AUC), and the SHapley Additive exPlanations (SHAP) algorithm was used to interpret its predictions.

RESULTS

The final selected model achieved a precision of 0.81, a sensitivity of 0.61, a specificity of 0.89, and an AUC value of 0.84. In our model, serum creatinine levels of kidney transplant patients, evaluated at the hospital discharge, proved to be the most important factor in the decision-making for the allograft loss. Patients with a weight equivalent to a BMI closer to the normal range prior to a kidney transplant are less likely to experience graft loss compared to patients with a BMI below the normal range. The age of patients at transplantation and Polyomavirus (BKPyV) infection had significant impact on clinical outcomes in our model.

CONCLUSIONS

Our algorithm suggests that the main characteristics that impacted early allograft loss were serum creatinine levels at the hospital discharge, as well as the pre-transplant values such as body weight, age of patients, and their BKPyV infection. We propose that machine learning tools can be developed to effectively assist medical decision-making in kidney transplantation.

Contrast-enhanced ultrasonography-based renal blood perfusion in brain-dead donors predicts early graft function

PURPOSE

The aim of this study was to quantify renal microcirculatory perfusion in braindead donors using contrast-enhanced ultrasonography (CEUS), and to establish an accurate, noninvasive, and convenient index for predicting delayed graft function (DGF) post-transplantation.

METHODS

In total, 90 brain-dead donor kidneys (training group, n=60; validation group, n=30) examined between August 2020 and November 2022 were recruited in this prospective study. CEUS was performed on the kidneys of brain-dead donors 24 hours before organ procurement and time-intensity curves were constructed. The main measures were arrival time, time to peak, and peak intensity of the kidney segmental arteries, cortex, and medulla. Recipients were divided into DGF and non-DGF groups according to early post-transplant graft function. The area under the receiver operating characteristic curve (AUC) was used to assess diagnostic performance.

RESULTS

The arrival time of the kidney segmental artery and cortex and the time interval between the time to peak of the segmental artery and cortex were identified as independent factors associated with DGF by multivariate stepwise regression analysis. A new index for the joint prediction model of three variables, the contrast-enhanced ultrasonography/Kidney Donor Profile index (CEUS-KDPI), was developed. CEUS-KDPI showed high accuracy for predicting DGF (training group: AUC, 0.91; sensitivity, 90.5%; specificity, 92.3%; validation group: AUC, 0.84; sensitivity, 75.0%; specificity, 92.3%).

CONCLUSION

CEUS-KDPI accurately predicted DGF after kidney transplantation. CEUS may be a potential noninvasive tool for bedside examinations before organ procurement and may be used to predict early renal function after kidney transplants kidneys from donors after brain death.

Renal transplant anastomotic time-Every minute counts!

The impact of anastomotic time in renal transplant is under recognized and not well studied. It is one of the few controllable factors that affect the incidence of delayed graft function (DGF). Our study aimed at quantifying the impact of anastomotic time. We performed a retrospective review of 424 renal transplants between the years 2006 and 2020. A total of 247 deceased donor renal transplants formed the study cohort. Patients were divided into two groups based on the presence or absence of DGF. Variables with p < 0.3 were analyzed using the binary logistic regression test. The final analysis showed anastomotic time to be significantly associated with DGF with odds ratio of 1.04 per minute corresponding to 4% increase in DGF incidence with every minute increment in anastomotic time. Other variables that had significant impact on DGF were DCD donor (odds ratio - 8.7) and donor terminal creatinine. We concluded that anastomotic time had significant impact on the development of DGF and hence should be minimized.

Expanded Criteria Donor With Severe Acute Kidney Injury: Worth to Use?

BACKGROUND

Expanded criteria donors (ECDs) may present with acute kidney injury (AKI). Many transplantation centers refuse to use these kidneys because of concerns about poor transplant outcomes, resulting in a high discard rate. However, long-term results of ECDs with AKI (ECDs + AKI) have not been extensively studied.

METHODS

We retrospectively compared outcomes of ECDs with ECDs + AKI. Primary outcome was 5-year allograft and patient survival rate. Secondary outcomes were allograft function, rates of delayed graft function, and allograft rejection.

RESULTS

Of 743 deceased donor kidney transplant recipients, 95 ECD cases were included in this study. There were 38 patients (40%) with ECDs and 57 patients (60%) with ECDs + AKI. Mean donor creatinine was progressively higher with severity of AKI. Five-year graft and patient survival were comparable between ECDs and ECDs + AKI (80.6% vs 81.1%, P = .95 and 91.7% vs 88.7%, P = .73). Mean (SD) allograft estimated glomerular filtration rate was 36.7 (14.5) vs 40.6 (22.7) mL/min/1.73 m² with P = .61, respectively. Multivariate analysis showed factors associated graft loss were delayed graft function (P = .01) and donor-recipient age difference ≥10 years (P = .038), not AKI status.

CONCLUSIONS

Kidney transplant from ECDs + AKI has comparable allograft survival with ECDs without AKI. Use of ECDs + AKI is worthwhile and kidneys from ECDs + AKI should not be discarded. Recipient selection and perioperative care are important to optimize the use of scarce resource.