Development and Validation of a Canadian Prediction Equation for Incident CKD Using Population-Based, Administrative Data.

Background: Identifying individuals at risk for incident chronic kidney disease (CKD; estimated glomerular filtration rate [eGFR] <60 mL/min/1.73 m Objective: Develop and validate prediction equations to identify individuals at risk of incident CKD using routinely collected administrative data with and without urine albumin-to-creatinine ratio (ACR). Design: This is a retrospective cohort study using administrative data. Setting: This study was conducted in Manitoba and Ontario, Canada. Patients: This study included 413 948 adults (18 or older) with an eGFR > 70 mL/min/1.73 m Measurements: Routinely available variables (demographics, comorbidities, laboratory values) in administrative data sets were used to predict the outcome of incident CKD (stage G3+) defined by a single outpatient eGFR measure <60 mL/min/1.73 m Methods: Time-to-event models, accounting for the competing risk of death, were used to predict new-onset CKD from one to nine years with a data-driven model reduction. Prediction equations stratifying individuals with and without ACR measurements were derived internally and externally validated. Results: Among individuals from Manitoba [53% women, mean (SD) age 51 (17), mean (SD) baseline eGFR 95 (14) mL/min/1.73 m Limitations: Only individuals with measures of all model predictors (complete case analysis) were included. Conclusion: Equations using routinely collected population-level, administrative data variables can accurately predict the onset of CKD with or without ACR.