Diabetic end-stage renal disease (ESRD) : can health care costs be saved through blood pressure control?

2006 ◽  
Author(s):  
Sau-kong Cheng
Keyword(s):  
Blood Pressure ◽  
Health Care ◽  
Renal Disease ◽  
Health Care Costs ◽  
Blood Pressure Control ◽  
End Stage Renal Disease ◽  
Pressure Control ◽  
Stage Renal Disease ◽  
End Stage ◽  
Care Costs

scholarly journals End-Stage Renal Disease-Associated Managed Care Costs Among Patients With and Without Diabetes

Diabetes Care ◽  
2004 ◽  
Vol 27 (12) ◽  
pp. 2829-2835 ◽  
Author(s):  
A. T. Joyce ◽  
J. M. Iacoviello ◽  
S. Nag ◽  
S. Sajjan ◽  
E. Jilinskaia ◽  
...  
Keyword(s):  
Managed Care ◽  
Renal Disease ◽  
End Stage Renal Disease ◽  
Stage Renal Disease ◽  
End Stage ◽  
Care Costs

scholarly journals Effects of Variability in Blood Pressure, Glucose, and Cholesterol Concentrations, and Body Mass Index on End-Stage Renal Disease in the General Population of Korea

2019 ◽  
Vol 8 (5) ◽  
pp. 755 ◽  
Author(s):  
Mee Kyoung Kim ◽  
Kyungdo Han ◽  
Hun-Sung Kim ◽  
Yong-Moon Park ◽  
Hyuk-Sang Kwon ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Body Mass Index ◽  
General Population ◽  
Renal Disease ◽  
End Stage Renal Disease ◽  
High Variability ◽  
Metabolic Parameters ◽  
Metabolic Parameter ◽  
Stage Renal Disease ◽  
End Stage

Aim: Metabolic parameters, such as blood pressure, glucose, lipid levels, and body weight, can interact with each other, and this clustering of metabolic risk factors is related to the progression to end-stage renal disease (ESRD). The effect of variability in metabolic parameters on the risk of ESRD has not been studied previously. Methods: Using nationally representative data from the Korean National Health Insurance System, 8,199,135 participants who had undergone three or more health examinations between 2005 and 2012 were included in this analysis. Intraindividual variability in systolic blood pressure (SBP), fasting blood glucose (FBG), total cholesterol (TC), and body mass index (BMI) was assessed by examining the coefficient of variation, variability independent of the mean, and average real variability. High variability was defined as the highest quartile of variability and low variability was defined as the lower three quartiles of variability. Results: Over a median (5–95%) of 7.1 (6.5–7.5) years of follow-up after the variability assessment period, 13,600 (1.7/1000 person-years) participants developed ESRD. For each metabolic parameter, an incrementally higher risk of ESRD was observed for higher variability quartiles compared with the lowest quartile. The risk of ESRD was 46% higher in the highest quartile of SBP variability, 47% higher in the highest quartile of FBG variability, 56% higher in the highest quartile of BMI variability, and 108% higher in the highest quartile of TC variability. Compared with the group with low variability for all four parameters, the group with high variability for all four parameters had a significantly higher risk for incident ESRD (hazard ratio (HR) 4.12; 95% CI 3.72–4.57). Conclusions: Variability in each metabolic parameter was an independent predictor of the development of ESRD among the general population. There was a composite effect of the variability in additional metabolic parameters on the risk of ESRD.

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