Longitudinal formulas to estimate GFR in children with CKD

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.

Figures

Figure 1.

Distribution of iGFR visit 2 measurements by iGFR at visit 1 within the training data (n = 220). The regression line fit for regressing iGFR at visit 2 on iGFR at visit 1 is shown (Table 3, model I). The magnitude of the error between iGFR and estimated GFR (eGFR) is illustrated by the length of the arrows with the origin at the observed data (iGFR1, iGFR2) and the arrowhead at the estimated value (iGFR1, eGFR2 using model IV, Table 3; data point at iGFR2 = 9.5 ml/min per 1.73 m2; not shown).

Figure 2.

Distribution of iGFR visit 2 measurements by iGFR at visit 1 within the validation data (n = 109). The magnitude of the error between iGFR and eGFR is illustrated by the length of the arrows with the origin at the observed data (iGFR1, iGFR2) and the arrowhead at the estimated value (iGFR1, eGFR2 using model IV, Table 3).