ASN: Models Can Predict Risk for Incident Chronic Kidney Disease
Risk equations had median C-values of 0.845 and 0.801 for cohorts with, without diabetes, respectively
FRIDAY, Nov. 8, 2019 (HealthDay News) -- Risk prediction models have been developed for five-year probability of reduced estimated glomerular filtration rate (eGFR), according to a study published online Nov. 8 in the Journal of the American Medical Association to coincide with Kidney Week, the annual meeting of the American Society of Nephrology, held from Nov. 5 to 10 in Washington, D.C.
Robert G. Nelson, M.D., Ph.D., from the National Institutes of Health in Phoenix, and colleagues conducted individual-level data analysis of 5,222,711 individuals from 28 countries to develop assessment tools to identify individuals at increased risk for chronic kidney disease. Discrimination and calibration were tested in nine external cohorts with 2,253,540 individuals.
The researchers included age, sex, race/ethnicity, eGFR, history of cardiovascular disease, ever smoking, hypertension, body mass index, and albuminuria concentration in equations for the five-year risk for reduced eGFR. Diabetes medications, hemoglobin A1c, and the interaction between the two were also included in models for participants with diabetes. The risk equations had a median C statistic of 0.845 and 0.801 for the five-year predicted probability in the cohorts without and with diabetes, respectively. Sixty-nine percent of the 13 study populations had a slope of observed to predicted risk between 0.80 and 1.25 in a calibration analysis; in 18 study populations from external validation cohorts, 89 percent had a slope of observed to predicted risk between 0.80 and 1.25.
"Further study is needed to determine whether these risk equations can improve care," the authors write.
Several authors disclosed financial ties to the pharmaceutical industry.