Open and laparoscopic donor nephrectomy: activity and outcomes from all Australasian transplant centers

Retroperitoneal robot-assisted live-donor nephrectomy: A single-center study

Background

As the demand for kidney transplants continues to increase globally, healthcare institutions face a challenge to bridge the gap between patients waitlisted for kidney transplants and the number of donors. A major factor influencing the donor's decision is the operative risk and potential complications of the surgery. Open surgical approaches have been vastly replaced with laparoscopic donor nephrectomies as the standard of practice. However, there is a growing body of evidence pointing towards its potential superiority over laparoscopic methods. In this study, we aim to present our experience on outcomes of Robotic-Assisted Live Donor Nephrectomies (RALDN), the first series of its kind in the United Arab Emirates (UAE).

Methods

We retrospectively collected data from patients who underwent RALDN at Mediclinc City Hospital. Demographic data, laboratory investigations, and operative details were collected and analyzed.

Results

Seven patients underwent RALDN between 2021 and April 2022 at our facility. Four donors were male while three were female. Median length of hospital stay was 4 days. In our study, one of the patients suffered from a Clavien-Dindo grade IV complication which necessitated prolonged admission.

Conclusion

We conclude that RALDN is a safe method for donor kidney procurement, carrying a low risk of morbidity and mortality. This method could potentially evolve the number of kidney donors to address the issue of high kidney transplant demand.

Comparison of Surgical Techniques in Living Donor Nephrectomy: A Systematic Review and Bayesian Network Meta-Analysis

Background

The aim of this study was to compare and evaluate surgical techniques used for living donor nephrectomy (LDN).

Material/Methods

We performed a meta-analysis to compare 4 surgical techniques: open LDN (OLDN), laparoscopic LDN (LLDN), hand-assisted LLDN (HALLDN), and robot-assisted LLDN (RLDN).

Results

No significant differences were found among these surgical techniques in terms of BMI, donor postoperative complications, 1-year graft survival, and DGF. Compared to the OLDN, the other 3 surgical techniques preferred to harvest the left kidney. When the right kidney was chosen as a donor, OLDN was the first-choice surgical technique. EBL was significantly lower in the HALLDN, LLDN, and RLDN groups when compared to the OLDN group. However, operative time and WIT were significantly shorter in the OLDN group. The RLDN group had an increased rate of donor intraoperative complications and a significantly lower VAS on day 1. The OLDN group required more morphine intake than the LLDN group. The length of hospital stay was significantly longer and AR was significantly higher in the OLDN group than in the LLDN and HALLDN groups.

Conclusions

There are no significant differences in donor postoperative complications, recipient DGF, and graft survival among the 4 surgical techniques. OLDN reduces WIT and operation time, but increases EBL and AR. RLDN and LLDN reduce the length of hospital stay, morphine intake, and VAS, and thus accelerate recovery. However, RLDN is associated with increased intraoperative complications.

Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study

Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients.  Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature.  However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia.   Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1 st, 2007, to December 31 st, 2017.  This included 3,758 live donor transplants and 7,365 deceased donor transplants.  Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models.  The best predictive model will be selected based on the model's performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia.   Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques.  Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.

Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study

Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients.  Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature.  However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia.   Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1 st, 2007, to December 31 st, 2017. This included 3,758 live donor transplants and 7,365 deceased donor transplants. Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models (for live donor and deceased donor transplants). The best predictive model will be selected based on the model's performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia. Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques. Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.

Multidisciplinary management of complicated bilateral renal artery aneurysm in a woman of childbearing age

Ruptured renal artery aneurysm (RAA) during pregnancy is a rare condition associated with high mortality rates to both the mother and the foetus. We report on a 41-year-old woman at her second trimester who presented with shock to the emergency department as a result of a ruptured left RAA. While the bleeding was successfully treated with angiographic embolization, a contralateral RAA, also at risk of rupture, was discovered. Due to its position on the artery bifurcation, this lesion was considered not suitable for interventional radiology and was therefore managed by hand-assisted retroperitoneoscopic nephrectomy, ex-vivo repair and autotransplantation. This was done in order to preserve renal mass and give our patient a chance of having future pregnancies without risk of rupture. Three years later, her renal function is normal, there is no evidence of recurrence, and more importantly she had two successful and uncomplicated pregnancies.

Evolution of laparoscopic live donor nephrectomy: a single-center experience with 1510 cases over 14 years

OBJECTIVE

This study evaluated the outcomes of laparoscopic donor nephrectomy (LDN) and proposed modifications for kidney donation surgery. From February 1997 to February 2011, 1510 LDNs were performed.

PATIENTS AND METHODS

Surgical modifications included a modified open access technique for entry into the abdominal cavity, using vascular clips for safe and cost-effective control of the renal pedicle, control of the lumbar veins, and adrenal vein using bipolar cautery instead of clips, and leaving the gonadal vein intact with the ureter. Kidneys were extracted by hand through a Pfannenstiel incision. Heparin was not used after the first 300 cases to prevent potential hemorrhagic complications.

RESULTS

Although three major vascular injuries occurred using the closed access method that were managed successfully, no access-related complications occurred using the modified open access technique. Clip failure did not happen in any cases. Patient and graft survival at 1 year post-transplantation were 96.5% and 95.5%, respectively, and at 5 years post-transplantation were 95.3% and 89.5%, respectively.

CONCLUSION

The proposed surgical modifications are based on 14 years of experience and 1510 cases, and make LDN simple, safe, and cost-effective. The excellent recipient and graft outcomes with minimal morbidity obtained further confirm that LDN can be considered as the gold standard for kidney donation surgery.

Laparoscopic nephrectomy, ex vivo angioplasty, and renal autotransplant for a renal artery aneurysm: a case report

We describe in this report the case of a renal aneurysm in a 42-year-old woman. The aneurysm measured 27 mm in diameter, and was sited at the first bifurcation of the renal artery. We performed laparoscopic nephrectomy, ex vivo angioplasty and renal autotransplant to avoid ischemic damage to the kidney during reconstruction. The patient recovered and was discharged from the hospital without any complications. Hence, we suggest these treatments can be effectively done in patients with complex renal aneurysms.