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

Identifying individuals at risk for incident chronic kidney disease (CKD; estimated glomerular filtration rate [eGFR] <60 mL/min/1.73 m²) could aid in prevention and disease surveillance.

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).

This is a retrospective cohort study using administrative data.

This study was conducted in Manitoba and Ontario, Canada.

This study included 413 948 adults (18 or older) with an eGFR > 70 mL/min/1.73 m² from Manitoba (derivation cohort; 2006-2016) with external validation in 7 747 513 adults from Ontario, Canada.

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 m² during and up to 10 years of follow-up. In an additional analysis, we defined incident CKD using repeat eGFR measures.

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.

Among individuals from Manitoba [53% women, mean (SD) age 51 (17), mean (SD) baseline eGFR 95 (14) mL/min/1.73 m², median (interquartile range) ACR 0.7 mg/mmol (1-3)], incident CKD occurred in 11.4% during a median follow-up time of 4.5 (Q1 = 2.3, Q3 = 7.6) years of follow-up. The final model included six variables (age, sex, baseline eGFR, hemoglobin, hypertension, and diabetes) and yielded a five-year area under the curve of 86.0 (no ACR) and 80.2 (with ACR). Model performance was excellent in external validation.

Only individuals with measures of all model predictors (complete case analysis) were included.

Equations using routinely collected population-level, administrative data variables can accurately predict the onset of CKD with or without ACR.