Development and validation of a new equation based on plasma creatinine and muscle mass assessed by CT scan to estimate glomerular filtration rate: a cross-sectional study

Lay Summary Performances of creatinine- and demographic-based estimated glomerular filtration rate (eGFR) equations are insufficient in patients for whom non-GFR determinants of serum creatinine level, including low or high muscle masses, are not standard. The total lumbar muscle cross-sectional area (MCSA) taken at the middle of the third lumbar vertebra, and easily measured from unenhanced computed tomography (CT) scan images, is a surrogate marker of total body muscle mass. The muscle mass-based eGFR equation (MMB-eGFR, which included plasma creatinine and MCSA) displayed better performances than conventional creatinine-based eGFR equations in patients with chronic diseases, especially if they had atypical muscle mass. MMB-eGFR performance, including its bias, is only marginally affected by ethnicity. For patients who have CT scans as part of their medical follow-up (especially patients with chronic diseases and thus at risk for sarcopenia), the MMB-eGFR equation might offer the possibility of a more personalized eGFR, without additional cost. Background Inter-individual variations of non-glomerular filtration rate (GFR) determinants of serum creatinine, such as muscle mass, account for the imperfect performance of estimated GFR (eGFR) equations. We aimed to develop an equation based on creatinine and total lumbar muscle cross-sectional area measured by unenhanced computed tomography scan at the third lumbar vertebra. Methods The muscle mass-based eGFR (MMB-eGFR) equation was developed in 118 kidney donor candidates (iohexol clearance) using linear regression. Validation cohorts included 114 healthy subjects from another center (Cr-51-EDTA clearance, validation population 1), 55 patients with chronic diseases (iohexol, validation population 2), and 60 patients with highly discordant creatinine and cystatin C-based eGFR, thus presumed to have atypical non-GFR determinants of creatinine (Cr-51-EDTA, validation population 3). Mean bias was the mean difference between eGFR and measured GFR, precision the standard deviation (SD) of the bias, and accuracy the percentage of eGFR values falling within 20% and 30% of measured GFR. Results In validation population 1, performance of MMB-eGFR was not different from those of CKD-EPICr2009 and CKD-EPICr2021. In validation population 2, MMB-eGFR was unbiased and displayed better precision than CKD-EPICr2009, CKD-EPICr2021 and EKFC (SD of the biases: 13.1 vs 16.5, 16.8 and 15.9 mL/min/1.73 m(2)). In validation population 3, MMB-eGFR had better precision and accuracy {accuracy within 30%: 75.0% [95% confidence interval (CI) 64.0-86.0] vs 51.5% (95% CI 39.0-64.3) for CKD-EPICr2009, 43.3% (95% CI 31.0-55.9) for CKD-EPICr2021, and 53.3% (95% CI 40.7-66.0) for EKFC}. Difference in bias between Black and white subjects was -2.1 mL/min/1.73 m(2) (95% CI -7.2 to 3.0), vs -8.4 mL/min/1.73 m(2) (95% CI -13.2 to -3.6) for CKD-EPICr2021. Conclusion MMB-eGFR displayed better performances than equations based on demographics, and could be applied to subjects of various ethnic backgrounds.