Introduction: Administrative data is increasingly used for chronic disease surveillance; however, its validity to define cases of chronic kidney disease (CKD) in children is unknown. We sought to evaluate the performance of case definitions for CKD in children.
Methods: We utilized population-based administrative data from the Manitoba Center for Health Policy to evaluate the validity of algorithms based on a combination of hospital claims, outpatient physician visits, and pharmaceutical use over 1-3 years in children <18 years of age. Algorithms were compared with a laboratory-based definition (estimated glomerular filtration rate < 90 ml/min/1.73 m2 and/or presence of proteinuria).
Results: All algorithms evaluated had very low sensitivity (0.20-0.39) and moderate positive predictive value (0.52-0.68). Algorithms had excellent specificity (0.98-0.99) and negative predictive value (0.96-0.97). Receiver operating characteristic (ROC) curves indicate fair accuracy (0.60-0.68). Sensitivity improved with increasing years of data. One or more physician claims and one or more prescriptions over 3 years had the highest sensitivity and ROC.
Conclusions: The sensitivity of administrative data algorithms for CKD is unacceptably low for a screening test. Specificity is excellent; therefore, children without CKD are correctly identified. Alternate data sources are required for population-based surveillance of this important chronic disease.