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.
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.
Authors: Cal H Robinson; Nivethika Jeyakumar; Bin Luo; Ron Wald; Amit X Garg; Danielle M Nash; Eric McArthur; Jason H Greenberg; David Askenazi; Cherry Mammen; Lehana Thabane; Stuart Goldstein; Rulan S Parekh; Michael Zappitelli; Rahul Chanchlani Journal: J Am Soc Nephrol Date: 2021-05-26 Impact factor: 14.978
Authors: Emma H Ulrich; Erin Hessey; Sylvie Perreault; Marc Dorais; Philippe Jouvet; Veronique Phan; Michael Zappitelli Journal: Crit Care Explor Date: 2022-01-18