Identification of Impeding Factors for Dry Weight Achievement in End-Stage Renal Disease After Appropriate Kidney Graft Function

In children with end-stage renal disease (ESRD), health-related quality of life (HRQOL) is a useful and important clinical measure for monitoring the child's well-being and functional status. One of the commonly used generic HRQOL instruments in children is the Pediatric Quality of Life Inventory, because an ESRD-specific instrument for children is still lacking. In the limited studies of HRQOL in children with ESRD, a significant effect of ESRD is seen, with significantly lower HRQOL scores than are seen in healthy children. In future, a pediatric ESRD-specific instrument is needed to address differences in HRQOL between children on hemodialysis, on peritoneal dialysis, and with a kidney graft.

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Robot-assisted kidney transplantation. First experience

Russian Journal of Transplantology and Artificial Organs ◽

10.15825/1995-1191-2020-2-125-131 ◽

2020 ◽

Vol 22 (2) ◽

pp. 125-131

Author(s):

S. V. Shchekaturov ◽

I. V. Semeniakin ◽

A. K. Zokoev ◽

T. B. Makhmudov ◽

R. R. Poghosyan

Keyword(s):

Kidney Transplantation ◽

Renal Disease ◽

Kidney Transplant ◽

End Stage Renal Disease ◽

Graft Function ◽

Surgical Techniques ◽

Clinical Report ◽

Robot Assisted ◽

Stage Renal Disease ◽

End Stage

Kidney transplantation is the preferred renal replacement therapy for patients with end-stage renal disease. Traditional surgical approaches consisting of vascular and urinary outflow reconstruction during kidney transplant have been sufficiently studied and standardized. However, surgical techniques are still evolving. The objective of this clinical report is to focus the attention of kidney transplant surgeons and specialists on the currently trending robot-assisted kidney transplantation (RAKT) as a minimally invasive procedure for surgical treatment of patients with end-stage renal disease. In our first experience, good primary graft function was achieved. This shows that RAKT is a surgical option. With considerable number of surgeries and experience, RAKT outcomes would be improved significantly.

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Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease

Frontiers in Physiology ◽

10.3389/fphys.2021.790086 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xiaoyi Guo ◽

Wei Zhou ◽

Yan Yu ◽

Yinghua Cai ◽

Yuan Zhang ◽

...

Keyword(s):

Renal Disease ◽

End Stage Renal Disease ◽

Rbf Neural Network ◽

Bioelectrical Impedance ◽

Dry Weight ◽

Impedance Analysis ◽

Artificial Intelligence Technology ◽

Stage Renal Disease ◽

Computed Tomography Image ◽

End Stage

Dry weight (DW) is an important dialysis index for patients with end-stage renal disease. It can guide clinical hemodialysis. Brain natriuretic peptide, chest computed tomography image, ultrasound, and bioelectrical impedance analysis are key indicators (multisource information) for assessing DW. By these approaches, a trial-and-error method (traditional measurement method) is employed to assess DW. The assessment of clinician is time-consuming. In this study, we developed a method based on artificial intelligence technology to estimate patient DW. Based on the conventional radial basis function neural (RBFN) network, we propose a multiple Laplacian-regularized RBFN (MLapRBFN) model to predict DW of patient. Compared with other model and body composition monitor, our method achieves the lowest value (1.3226) of root mean square error. In Bland-Altman analysis of MLapRBFN, the number of out agreement interval is least (17 samples). MLapRBFN integrates multiple Laplace regularization terms, and employs an efficient iterative algorithm to solve the model. The ratio of out agreement interval is 3.57%, which is lower than 5%. Therefore, our method can be tentatively applied for clinical evaluation of DW in hemodialysis patients.

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