A machine learning algorithm for early detection of end-stage renal disease |
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A machine learning algorithm for early detection of end-stage renal disease
eMediNexus,  23 January 2021
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Question: What are the challenges with identifying and diagnosing End-Stage Renal Disease (ESRD)

Answer: ESRD is the most severe stage of chronic kidney disease when patients need dialysis or a renal transplant. There is often a delay in recognizing, diagnosing, and treating the various aetiologies of chronic kidney disease. Given that even at stage 4, persons may be asymptomatic, there is often a delay in recognizing, diagnosing, and treating the various aetiologies of CKD. As treatment alternatives are available to slow down renal disease progress, a precise prediction model is required for the identification of patients at raised risk for kidney function deterioration.

Question: What are the different stages of chronic kidney diseases?

Answer: End-stage renal disease describes the most severe last stage (Stage 5) of chronic kidney disease when the kidneys are functioning at 10-15% or less of their normal function. Stage 1 represents a normal renal function, the glomerular filtration rate (GFR) s over 90 ml/Kg/min, and the condition is almost always asymptomatic. Stage 2 is defined by GFR between 60 and 89 ml/kg/min, and although defined by laboratory tests, most individuals are asymptomatic. Stage 3 denotes GFR between 30 and 59 ml/kg/min and is in most cases related to fatigue, fluid retention, and changes in urination. Stage 4 is defined by GFR between 15 and 29 ml/kg/min and is marked by the presence of swelling of the extremities, nausea, and vomiting, along with nerve and cognitive malfunction. At stage 5, the kidneys cannot perform the fluid, electrolyte, and waste exchange required for homeostasis of the body, and without kidney dialysis or renal transplant, a condition incompatible with life.

Question: What is a prediction model for ESRD?

Answer:As ESRD demands kidney dialysis and involves severe comorbidities, accurate prediction of patients who are likely to deteriorate to ESRD at increased chances of mortality is critical. Several methods have been proposed to predict ESRD. Such models use logistic or Cox regression to predict the occurrence of chronic kidney disease and its progression in different populations. Many studies have stressed on building prediction tools for use in patients with CKD, predicting kidney failure, cardiovascular events, and all-cause mortality.

Antineutrophil cytoplasmic antibody-associated vasculitides are autoimmune disorders leading to irreversible damage to affected organs. Recently, a new scoring system has been validated as a clinical-pathological method to improve prediction in CKD.

Reference

Segal Z, Kalifa D, Radinsky K, Ehrenberg B, Elad G, Maor G, Lewis M, Tibi M, Korn L, Koren G. Machine learning algorithm for early detection of end-stage renal disease. BMC Nephrology. 2020; 518.

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