BMC medical informatics and decision making
Oct 14, 2015
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.
PURPOSE: Kidney transplantation is the preferred treatment for patients with end-stage renal disease. In order to reduce the morbidity of the open surgery, a robotic-assisted approach has been recently introduced. According to the published literatur...
Immune monitoring is essential for maintaining immune homeostasis after renal transplantation (RT). Peripheral blood lymphocyte subpopulations (PBLSs) are widely used biomarkers for immune monitoring, yet there is no established standard reference fo...
Kidney transplantation is the definitive treatment for end-stage renal disease (ESRD), yet challenges persist in optimizing donor-recipient matching, postoperative care, and immunosuppressive strategies. This study employs bibliometric analysis to ev...
Predicting the outcome of a kidney transplant involving a living donor advances donor decision-making donors for clinicians and patients. However, the discriminative or calibration capacity of the currently employed models are limited. We set out to...
RATIONALE AND OBJECTIVES: End-stage renal disease is characterized by an irreversible decline in kidney function. Despite a risk of chronic dysfunction of the transplanted kidney, renal transplantation is considered the most effective solution among ...
RATIONALE AND OBJECTIVES: Acute kidney injury (AKI) post-renal transplantation often has a poor prognosis. This study aimed to identify patients with elevated risks of AKI after kidney transplantation.
BACKGROUND: Ischemia-reperfusion injury (IRI) is closely associated with numerous severe postoperative complications, including acute rejection, delayed graft function (DGF) and graft failure. Macrophages are central to modulating the aseptic inflamm...
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