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Longitudinal Formulas to Estimate GFR in Children with CKD

Alison G. Abraham^*, George J. Schwartz, Susan Furth^*,, Bradley A. Warady, and Alvaro Muñoz^*

^* Department of Epidemiology, Bloomberg School of Public Health and Department of Pediatrics, School of Medicine, Johns Hopkins University, Baltimore, Maryland; Department of Pediatrics, School of Medicine, University of Rochester, Rochester, New York; and Department of Pediatrics, Children's Mercy Hospital, Kansas City, Missouri

Correspondence: Dr. Alison G. Abraham,Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205. Phone: 410-502-9763; Fax: 410-955-7581; E-mail: agump{at}jhsph.edu

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 GFR₂ = a[GFR₁/40]^b[X₂/X₁]^c, where GFR₂ and GFR₁ represented the current and previous years' iGFR, 40 ml/min per 1.73 m² was the cohort mean, and X₂/X₁ 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.