MONDAY, Sept. 27, 2021 (HealthDay News) — The accuracy of estimated glomerular filtration rate (eGFR) equations omitting race is described in two studies published online Sept. 23 in the New England Journal of Medicine.

Lesley A. Inker, M.D., from the Tufts Medical Center in Boston, and colleagues developed new eGFR equations without race using data from two development datasets for serum creatinine and both serum creatinine and cystatin C. The accuracy of new eGFR equations to measure eGFR was compared in a validation dataset. The researchers found that the current creatinine equation that uses age, sex, and race overestimated measured GFR in Blacks in the validation dataset and to a lesser degree in non-Blacks (median, 3.7 and 0.5 mL/min/1.73 m2, respectively). A new equation using age and sex and omitting race underestimated and overestimated measured GFR in Blacks and non-Blacks, respectively. Compared with new creatinine equations, new creatinine-cystatin C equations without race were more accurate, with smaller differences noted between race groups.

Chi-yuan Hsu, M.D., from the University of California in San Francisco, and colleagues conducted a cross-sectional analysis of baseline data from 1,248 participants to estimate GFR. The researchers found that in participants who identified as Black, compared with models including race, a model that omitted race resulted in more underestimation of GFR and lower accuracy using current formulations of GFR estimating equations. Similar estimates of the GFR were seen with incorporation of genetic ancestry data instead of race. When GFR was estimated with use of cystatin C, the incorporation of race or ancestry was not necessary to achieve statistically unbiased and accurate estimates in Black participants.

“Our study showed that the use of serum cystatin C rather than serum creatinine for GFR estimation produced estimates of similar validity while eliminating the negative consequences of race-based approaches,” Hsu and colleagues write.

Abstract/Full Text – Inker

Abstract/Full Text – Hsu


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