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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. [ABSTRACT FROM AUTHOR]

Catheter-related infections (CRI) are a major cause of morbidity and mortality in chronic hemodialysis (HD) patients. In this paper, we share our experience with CRI in HD patients. We recorded 49 cases of CRI among 167 patients during a period of 40 months (January 2018–April 2021). The incidence of CRI was 3.7 per 1000 catheter-days. The revealing symptoms were dominated by fever or chills (90%). Inflammatory signs were observed in 74% of cases with respectively concurrent exit-site (51%) and tunnel infection (6%). The biological inflammatory syndrome was found in 74% of patients (average CRP level = 198.9 mg/l). Blood cultures were performed in all cases and were positive in 65% of cases. Thirteen patients have been diagnosed with Infection complications, which were respectively infective endocarditis in 7 cases, septic arthritis in 3 cases, infective myositis in one case, cerebral thrombophlebitis in 1 case and mediastinitis in 1 case. The death occurred in eleven patients, it was due to septic shock in 9 cases, pulmonary embolism in one case and neurologic alterations related to cerebral thrombophlebitis. The mean seniority in HD was 16.5 months in the group with CRI and 3.7 months in the group without CRI (p < 0.04). We did not notice significant difference in mortality between tunnelled and non-tunnelled catheters. CRI does not seem to be more severe in patients with diabetes. Duration of use of the HD catheter (p < 0.007) and ferritin level (p < 0.0001) were independent factors that predispose to CRI in our population. [ABSTRACT FROM AUTHOR]