Source
Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA. agump@jhsph.edu
Abstract
BACKGROUND AND OBJECTIVES:
Whereas current GFR estimating equations approximate direct GFR measurement at a single time point, formulas that capitalize on changes in easily measured biologic parameters could improve the accuracy and precision of GFR estimation.
DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS:
In the Chronic Kidney Disease in Children Cohort (aged 1 to 16 yr), we measured GFR by plasma disappearance of iohexol (iGFR) and biomarkers in the first two annual visits. Models took the form GFR(2) = a[GFR(1)/40](b)[X(2)/X(1)](c), where GFR(2) and GFR(1) represented the current and previous years' iGFR, 40 ml/min per 1.73 m(2) was the cohort mean, and X(2)/X(1) was the change in predictors over time. Using data from 360 participants with a median age of 12.1 yr, we evaluated the predictive performance of a past GFR measurement and 20 other variables using a two-thirds random sample of the data. A one-third sample was reserved for validation.
RESULTS:
Previous iGFR measurements were strongly predictive of subsequent iGFR and adding change in height/serum creatinine significantly improved the explanatory power to 78%. In the validation set, the correlation between estimated and measured GFR was 0.88, and 48 and 88% of estimated GFRs were within 10 and 30% of observed iGFRs. When the past GFR measurement was not used, addition of change in markers to a cross-sectional model did not improve prediction.
CONCLUSIONS:
Longitudinal formulas to estimate iGFR capitalize on the high predictive power of previous iGFR measurements and in this study yielded a parsimonious prediction model with the potential for assessing progression in the clinical setting.