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Variability in estimated glomerular filtration rate by area under the curve predicts renal outcomes in chronic kidney disease.

Chen SC, Lin MY, Huang TH, Hung CC, Chiu YW, Chang JM, Tsai JC, Hwang SJ, Chen HC - ScientificWorldJournal (2014)

Bottom Line: A significant improvement in model prediction was based on the -2 log likelihood ratio statistic.In an adjusted Cox model, a smaller initial eGFR AUC%_12M (P < 0.001), a smaller peak eGFR AUC%_12M (P < 0.001), and a larger negative eGFR slope_12M (P < 0.001) were associated with a higher risk of renal end point.Two calculated formulas: initial eGFR AUC%_12M and eGFR slope_12M were the best predictors.

View Article: PubMed Central - PubMed

Affiliation: Division of Nephrology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung 807, Taiwan ; Department of Internal Medicine, Kaohsiung Municipal Hsiao-Kang Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan ; Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan.

ABSTRACT
Greater variability in renal function is associated with mortality in patients with chronic kidney disease (CKD). However, few studies have demonstrated the predictive value of renal function variability in relation to renal outcomes. This study investigates the predictive ability of different methods of determining estimated glomerular filtration rate (eGFR) variability for progression to renal replacement therapy (RRT) in CKD patients. This was a prospective observational study, which enrolled 1,862 CKD patients. The renal end point was defined as commencement of RRT. The variability in eGFR was measured by the area under the eGFR curve (AUC)%. A significant improvement in model prediction was based on the -2 log likelihood ratio statistic. During a median 28.7-month follow-up, there were 564 (30.3%) patients receiving RRT. In an adjusted Cox model, a smaller initial eGFR AUC%_12M (P < 0.001), a smaller peak eGFR AUC%_12M (P < 0.001), and a larger negative eGFR slope_12M (P < 0.001) were associated with a higher risk of renal end point. Two calculated formulas: initial eGFR AUC%_12M and eGFR slope_12M were the best predictors. Our results demonstrate that the greater eGFR variability by AUC% is associated with the higher risk of progression to RRT.

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Related in: MedlinePlus

Two representative cases to illustrate the eGFR variability. In Case 1, the initial eGFR AUC%_12M (a) and peak eGFR AUC%_12M (b) were 85.0% and 68.9%, respectively. The eGFR slope of Case 1 was −1.06 mL/min/1.73 m2 per month. In Case 2, the initial eGFR AUC%_12M (c) and peak eGFR AUC%_12M (d) were 64.8% and 51.3%, respectively. The eGFR slope of Case 2 was −1.51 mL/min/1.73 m2 per month. Case 2 had greater eGFR variability than that of Case 1.
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fig2: Two representative cases to illustrate the eGFR variability. In Case 1, the initial eGFR AUC%_12M (a) and peak eGFR AUC%_12M (b) were 85.0% and 68.9%, respectively. The eGFR slope of Case 1 was −1.06 mL/min/1.73 m2 per month. In Case 2, the initial eGFR AUC%_12M (c) and peak eGFR AUC%_12M (d) were 64.8% and 51.3%, respectively. The eGFR slope of Case 2 was −1.51 mL/min/1.73 m2 per month. Case 2 had greater eGFR variability than that of Case 1.

Mentions: The rate of renal function decline was assessed by the slope of eGFR, defined as the regression coefficient between eGFR and time in units of mL/min/1.73 m2/year. At least three eGFR measurements were required to estimate eGFR slope. Faster renal function progression was reflected in a larger negative value of the slope. In addition, eGFR AUC% was used to estimate the variability in eGFR. Table 1 showed the specifics of the calculations and illustrates two different methods of estimating eGFR AUC%. A smaller eGFR AUC% indicates greater eGFR variability. Initial eGFR AUC%_12M was defined as initial eGFR for baseline value and estimated variability in eGFR during 12 months. Peak eGFR AUC%_12M was defined as peak eGFR for baseline value and estimated variability in eGFR during 12 months. Figure 2 represents two cases to illustrate the eGFR variability.


Variability in estimated glomerular filtration rate by area under the curve predicts renal outcomes in chronic kidney disease.

Chen SC, Lin MY, Huang TH, Hung CC, Chiu YW, Chang JM, Tsai JC, Hwang SJ, Chen HC - ScientificWorldJournal (2014)

Two representative cases to illustrate the eGFR variability. In Case 1, the initial eGFR AUC%_12M (a) and peak eGFR AUC%_12M (b) were 85.0% and 68.9%, respectively. The eGFR slope of Case 1 was −1.06 mL/min/1.73 m2 per month. In Case 2, the initial eGFR AUC%_12M (c) and peak eGFR AUC%_12M (d) were 64.8% and 51.3%, respectively. The eGFR slope of Case 2 was −1.51 mL/min/1.73 m2 per month. Case 2 had greater eGFR variability than that of Case 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4226187&req=5

fig2: Two representative cases to illustrate the eGFR variability. In Case 1, the initial eGFR AUC%_12M (a) and peak eGFR AUC%_12M (b) were 85.0% and 68.9%, respectively. The eGFR slope of Case 1 was −1.06 mL/min/1.73 m2 per month. In Case 2, the initial eGFR AUC%_12M (c) and peak eGFR AUC%_12M (d) were 64.8% and 51.3%, respectively. The eGFR slope of Case 2 was −1.51 mL/min/1.73 m2 per month. Case 2 had greater eGFR variability than that of Case 1.
Mentions: The rate of renal function decline was assessed by the slope of eGFR, defined as the regression coefficient between eGFR and time in units of mL/min/1.73 m2/year. At least three eGFR measurements were required to estimate eGFR slope. Faster renal function progression was reflected in a larger negative value of the slope. In addition, eGFR AUC% was used to estimate the variability in eGFR. Table 1 showed the specifics of the calculations and illustrates two different methods of estimating eGFR AUC%. A smaller eGFR AUC% indicates greater eGFR variability. Initial eGFR AUC%_12M was defined as initial eGFR for baseline value and estimated variability in eGFR during 12 months. Peak eGFR AUC%_12M was defined as peak eGFR for baseline value and estimated variability in eGFR during 12 months. Figure 2 represents two cases to illustrate the eGFR variability.

Bottom Line: A significant improvement in model prediction was based on the -2 log likelihood ratio statistic.In an adjusted Cox model, a smaller initial eGFR AUC%_12M (P < 0.001), a smaller peak eGFR AUC%_12M (P < 0.001), and a larger negative eGFR slope_12M (P < 0.001) were associated with a higher risk of renal end point.Two calculated formulas: initial eGFR AUC%_12M and eGFR slope_12M were the best predictors.

View Article: PubMed Central - PubMed

Affiliation: Division of Nephrology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung 807, Taiwan ; Department of Internal Medicine, Kaohsiung Municipal Hsiao-Kang Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan ; Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan.

ABSTRACT
Greater variability in renal function is associated with mortality in patients with chronic kidney disease (CKD). However, few studies have demonstrated the predictive value of renal function variability in relation to renal outcomes. This study investigates the predictive ability of different methods of determining estimated glomerular filtration rate (eGFR) variability for progression to renal replacement therapy (RRT) in CKD patients. This was a prospective observational study, which enrolled 1,862 CKD patients. The renal end point was defined as commencement of RRT. The variability in eGFR was measured by the area under the eGFR curve (AUC)%. A significant improvement in model prediction was based on the -2 log likelihood ratio statistic. During a median 28.7-month follow-up, there were 564 (30.3%) patients receiving RRT. In an adjusted Cox model, a smaller initial eGFR AUC%_12M (P < 0.001), a smaller peak eGFR AUC%_12M (P < 0.001), and a larger negative eGFR slope_12M (P < 0.001) were associated with a higher risk of renal end point. Two calculated formulas: initial eGFR AUC%_12M and eGFR slope_12M were the best predictors. Our results demonstrate that the greater eGFR variability by AUC% is associated with the higher risk of progression to RRT.

Show MeSH
Related in: MedlinePlus