Development of Risk Prediction Equations for Incident Chronic Kidney Disease

Abstract

Importance: Early identification of individuals at elevated risk of developing chronic kidney disease (CKD) could improve clinical care through enhanced surveillance and better management of underlying health conditions.

Objective: To develop assessment tools to identify individuals at increased risk of CKD, defined by reduced estimated glomerular filtration rate (eGFR).

Design, setting, and participants: Individual-level data analysis of 34 multinational cohorts from the CKD Prognosis Consortium including 5 222 711 individuals from 28 countries. Data were collected from April 1970 through January 2017. A 2-stage analysis was performed, with each study first analyzed individually and summarized overall using a weighted average. Because clinical variables were often differentially available by diabetes status, models were developed separately for participants with diabetes and without diabetes. Discrimination and calibration were also tested in 9 external cohorts (n = 2 253 540).

Exposures: Demographic and clinical factors.

Main outcomes and measures: Incident eGFR of less than 60 mL/min/1.73 m2.

Results: Among 4 441 084 participants without diabetes (mean age, 54 years, 38% women), 660 856 incident cases (14.9%) of reduced eGFR occurred during a mean follow-up of 4.2 years. Of 781 627 participants with diabetes (mean age, 62 years, 13% women), 313 646 incident cases (40%) occurred during a mean follow-up of 3.9 years. Equations for the 5-year risk of reduced eGFR included age, sex, race/ethnicity, eGFR, history of cardiovascular disease, ever smoker, hypertension, body mass index, and albuminuria concentration. For participants with diabetes, the models also included diabetes medications, hemoglobin A1c, and the interaction between the 2. The risk equations had a median C statistic for the 5-year predicted probability of 0.845 (interquartile range [IQR], 0.789-0.890) in the cohorts without diabetes and 0.801 (IQR, 0.750-0.819) in the cohorts with diabetes. Calibration analysis showed that 9 of 13 study populations (69%) had a slope of observed to predicted risk between 0.80 and 1.25. Discrimination was similar in 18 study populations in 9 external validation cohorts; calibration showed that 16 of 18 (89%) had a slope of observed to predicted risk between 0.80 and 1.25.

Conclusions and relevance: Equations for predicting risk of incident chronic kidney disease developed from more than 5 million individuals from 34 multinational cohorts demonstrated high discrimination and variable calibration in diverse populations. Further study is needed to determine whether use of these equations to identify individuals at risk of developing chronic kidney disease will improve clinical care and patient outcomes.

Figure 1.. Variation in the Baseline, Which Was Adjusted for Competing Risk of Incident Estimated Glomerular Filtration Rate (eGRF) of Less Than 60 mL/min/1.73 m²

Each line represents the adjusted baseline risk in an individual cohort. The risk was determined by holding the weighted-average coefficients constant and fitting a multivariable competing risk model in each study. The adjusted subhazard was smoothed using a Weibull distribution. The pooled line represents the weighted mean used in the prediction equation.

Figure 2.. Predicted 5-Year Absolute Risk of Incident Estimated Glomerular Filtration Rate (eGFR) of Less Than 60 mL/min/1.73 m²

Predicted 5-year absolute risk of incident estimated glomerular filtration rate (eGFR) of less than 60 mL/min/1.73 m² is shown for various scenarios for 3 ages and albuminuria categories among those with or without diabetes. All 5-year risks were computed for hypothetical individuals with a baseline eGFR of 90 mL/min/1.73 m². For the 5-year predicted risk in a hypothetical individual with diabetes, the hemoglobin A_1c was also set to 7.7% and the individual was assumed to be receiving an oral diabetes medicine. Scenarios: sex, male or female; ethnicity, nonblack or black; history of cardiovascular disease, yes or no; smoker, yes or no; hypertension, yes or no; and body mass index (BMI), calculated as weight in kilograms divided by height in meters squared, 25 or 35. Each column contains 64 dots representing 64 (all combinations of 6 binary variables) hypothetical scenarios. The dots are shaded from light to dark based on the number of risk factors present, scaled from 0 to 4 based on the presence or absence of cardiovascular disease, smoking, hypertension, and BMI of 35 or higher.