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Delayed Graft Function

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Assessing donor kidney function: the role of CIRBP in predicting delayed graft function post-transplant.

Frontiers in immunology
INTRODUCTION: Delayed graft function (DGF) shortens the survival time of transplanted kidneys and increases the risk of rejection. Current methods are inadequate in predicting DGF. More precise tools are required to assess kidney suitability for tran...

Usefulness of Delayed Introduction of Tacrolimus in Kidney Transplants Using Type-III Donors After Circulatory Death.

Transplantation proceedings
INTRODUCTION: Our study compares 2 immunosuppressive strategies to reduce tacrolimus nephrotoxicity and its risk of acute tubular necrosis: delayed introduction of tacrolimus plus thymoglobulin vs initial tacrolimus plus basiliximab on the results of...

Experience With Hypothermic Machine Perfusion in Expanded Criteria Donors: Functional Outcomes.

Transplantation proceedings
UNLABELLED: Hypothermic machine perfusion (HMP) decreases delayed graft function (DGF) and improves 1-year graft survival in expanded criteria donors (ECDs). Time of HMP could be associated with incidence of DGF.

Prediction of delayed graft function after kidney transplantation: comparison between logistic regression and machine learning methods.

BMC medical informatics and decision making
BACKGROUND: Predictive models for delayed graft function (DGF) after kidney transplantation are usually developed using logistic regression. We want to evaluate the value of machine learning methods in the prediction of DGF.

Robotic Donor Nephrectomy: Against.

European urology focus
The use of robotic techniques in laparoscopic donor nephrectomy currently tends to involve a longer ischemia time without clear advantages, and the cost of robotic surgery is significantly higher. If only one robot is available, then unnecessary prol...

The future is coming: promising perspectives regarding the use of machine learning in renal transplantation.

Jornal brasileiro de nefrologia
INTRODUCTION: The prediction of post transplantation outcomes is clinically important and involves several problems. The current prediction models based on standard statistics are very complex, difficult to validate and do not provide accurate predic...

Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning.

Scientific reports
Machine learning (ML) has shown its potential to improve patient care over the last decade. In organ transplantation, delayed graft function (DGF) remains a major concern in deceased donor kidney transplantation (DDKT). To this end, we harnessed ML t...

Cherry on Top or Real Need? A Review of Explainable Machine Learning in Kidney Transplantation.

Transplantation
Research on solid organ transplantation has taken advantage of the substantial acquisition of medical data and the use of artificial intelligence (AI) and machine learning (ML) to answer diagnostic, prognostic, and therapeutic questions for many year...

An integrated machine learning model enhances delayed graft function prediction in pediatric renal transplantation from deceased donors.

BMC medicine
BACKGROUND: Kidney transplantation is the optimal renal replacement therapy for children with end-stage renal disease; however, delayed graft function (DGF), a common post-operative complication, may negatively impact the long-term outcomes of both t...