Predicting long-term outcomes of kidney transplantation in the era of artificial intelligence.

The ability to accurately predict long-term kidney transplant survival can assist nephrologists in making therapeutic decisions. However, predicting kidney transplantation (KT) outcomes is challenging due to the complexity of the factors involved. Artificial intelligence (AI) has become an increasingly important tool in the prediction of medical outcomes. Our goal was to utilize both conventional and AI-based methods to predict long-term kidney transplant survival. Our study included 407 KTs divided into two groups (group A: with a graft lifespan greater than 5 years and group B: with poor graft survival). We first performed a traditional statistical analysis and then developed predictive models using machine learning (ML) techniques. Donors in group A were significantly younger. The use of Mycophenolate Mofetil (MMF) was the only immunosuppressive drug that was significantly associated with improved graft survival. The average estimated glomerular filtration rate (eGFR) in the 3rd month post-KT was significantly higher in group A. The number of hospital readmissions during the 1st year post-KT was a predictor of graft survival. In terms of early post-transplant complications, delayed graft function (DGF), acute kidney injury (AKI), and acute rejection (AR) were significantly associated with poor graft survival. Among the 35 AI models developed, the best model had an AUC of 89.7% (Se: 91.9%; Sp: 87.5%). It was based on ten variables selected by an ML algorithm, with the most important being hypertension and a history of red-blood-cell transfusion. The use of AI provided us with a robust model enabling fast and precise prediction of 5-year graft survival using early and easily collectible variables. Our model can be used as a decision-support tool to early detect graft status.

Toward generalizing the use of artificial intelligence in nephrology and kidney transplantation.

With its robust ability to integrate and learn from large sets of clinical data, artificial intelligence (AI) can now play a role in diagnosis, clinical decision making, and personalized medicine. It is probably the natural progression of traditional statistical techniques. Currently, there are many unmet needs in nephrology and, more particularly, in the kidney transplantation (KT) field. The complexity and increase in the amount of data, and the multitude of nephrology registries worldwide have enabled the explosive use of AI within the field. Nephrologists in many countries are already at the center of experiments and advances in this cutting-edge technology and our aim is to generalize the use of AI among nephrologists worldwide. In this paper, we provide an overview of AI from a medical perspective. We cover the core concepts of AI relevant to the practicing nephrologist in a consistent and simple way to help them get started, and we discuss the technical challenges. Finally, we focus on the KT field: the unmet needs and the potential role that AI can play to fill these gaps, then we summarize the published KT-related studies, including predictive factors used in each study, which will allow researchers to quickly focus on the most relevant issues.

Peritoneal dialysis in the era of COVID-19: experience of a Tunisian center.

The COVID-19 pandemic has transformed the health landscape by hampering the management of patients with chronic diseases. Providing optimal healthcare has become a critical issue, especially for patients with end-stage renal disease (ESRD) receiving in-center dialysis. Peritoneal Dialysis (PD) has the advantage of being a home-based therapy. Several papers about COVID-19 in the chronic kidney disease (CKD) population have been published, but few studies focused on the PD population, with limited case series. In this paper, we share our strategy for managing PD patients during the pandemic and describe the characteristics of 24 episodes of COVID-19 that occurred in our PD patients. Also, we report the impact of the pandemic on different outcomes and discuss the challenges of renal replacement therapy (RRT) in the time of COVID-19 and the advantages of PD. During the period from December 2019 to September 2021, 127 patients received PD in our center. Among them, we recorded 24 episodes of COVID-19 that occurred in 20 patients, corresponding to an incidence of 8.4 per 1000 patient-months. None of the 20 patients with COVID-19 were vaccinated and there was a significant male gender predominance in the COVID-19 group compared to the non-COVID-19 group. The prevalence of diabetic nephropathy and primary glomerulonephritis were also significantly higher in the COVID-19 group. The revealing symptoms were asthenia, dry cough, and the deterioration of general conditions in 100%, 75%, and 63% of the patients, respectively. A biological inflammatory syndrome was found in 30% of the patients. Chest computed tomography (CT) scan, performed in 5 patients, showed features of COVID pneumonia with an average extent of damage of 55%. The rate of patients starting PD during the study period was comparable to that before the pandemic. Furthermore, we did not find a significant difference between the infected and the non-infected groups regarding the incidence of peritonitis, PD technique failure, and mortality (6.1 [0-1.46] vs 3.9 [0.15-0.64] deaths per 1000 patient-months. COVID-19 does not seem to have influenced the outcomes of our patients treated with PD even before the launch of mass immunization in our country. Thus, PD can be a great option for RRT in the era of the COVID-19 pandemic since many issues could be managed remotely to avoid regular hospital visits and contribute to maintaining social distancing, which is the cornerstone of breaking the chain of transmission of the novel virus.