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

Thomas Stehlé; Yaniss Ouamri; Antoine Morel; Emmanuelle Vidal-Petiot; Soraya Fellahi; Lauriane Segaux; Dominique Prié; Philippe Grimbert; Alain Luciani; Vincent Audard; Jean Philippe Haymann; Sébastien Mulé; Eric De Kerviler; Marie-Noëlle Peraldi; Anne Boutten; Marie Matignon; Florence Canouï-Poitrine; Martin Flamant; Frédéric Pigneur

doi:10.1093/ckj/sfad012

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

Clin Kidney J. 2023 Jan 20;16(8):1265-1277. doi: 10.1093/ckj/sfad012. eCollection 2023 Aug.

Authors

Thomas Stehlé^{1

2}, Yaniss Ouamri^{1

3}, Antoine Morel^{1

4}, Emmanuelle Vidal-Petiot^{5

6}, Soraya Fellahi^{7

8}, Lauriane Segaux^{1

4}, Dominique Prié^{9

10}, Philippe Grimbert^{1

2}, Alain Luciani^{1

3}, Vincent Audard^{1

2}, Jean Philippe Haymann^{11

12}, Sébastien Mulé^{1

3}, Eric De Kerviler¹³, Marie-Noëlle Peraldi¹⁴, Anne Boutten¹⁵, Marie Matignon^{1

2}, Florence Canouï-Poitrine^{1

4}, Martin Flamant^{5

6}, Frédéric Pigneur^{1

3}

Affiliations

¹ Univ. Paris Est Créteil, Institut National de la Santé et de la Recherche Médicale (INSERM) U955, Institut Mondor de Recherche Biomédicale (IMRB), Créteil, France.
² Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, Service de Néphrologie et Transplantation, Fédération Hospitalo-Universitaire « Innovative therapy for immune disorders », Créteil, France.
³ Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Service d'Imagerie Médicale, Créteil, France.
⁴ Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri-Mondor, Service de Santé Publique, Créteil, France.
⁵ Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), U1149, Paris, France.
⁶ Assistance Publique des Hôpitaux de Paris (AP-HP), Département de Physiologie-Explorations Fonctionnelles, Hôpital Bichat, Paris, France.
⁷ Université Pierre et Marie Curie Paris 6, Hôpital Pitié-Salpêtrière, Assistance Publique des Hôpitaux de Paris (APHP), Paris, France.
⁸ Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, Département de Biochimie, Créteil, France.
⁹ Université de Paris Cité, Faculté de Médecine, Institut National de la Santé et de la Recherche Médicale (INSERM) U1151, Paris, France.
¹⁰ Assistance Publique des Hôpitaux de Paris (AP-HP), Groupe Hospitalier Necker Enfants Malades, Service de Physiologie et Explorations Fonctionnelles, Paris, France.
¹¹ Univ. Paris Diderot, Sorbonne Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), U1155.
¹² Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux de Paris, hôpital Tenon, Département de Physiologie-Explorations Fonctionnelles, Paris, France.
¹³ Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux de Paris, Hôpital Tenon, Département de Physiologie-Explorations Fonctionnelles, Paris, France.
¹⁴ Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpital Saint Louis, Service de Néphrologie, Paris, France.
¹⁵ Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpitaux de Paris, hôpital Bichat, Département de Biochimie Clinique, Paris, France.

Abstract

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 (⁵¹Cr-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 (⁵¹Cr-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-EPI_Cr2009 and CKD-EPI_Cr2021. In validation population 2, MMB-eGFR was unbiased and displayed better precision than CKD-EPI_Cr2009, CKD-EPI_Cr2021 and EKFC (SD of the biases: 13.1 vs 16.5, 16.8 and 15.9 mL/min/1.73 m²). 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-EPI_Cr2009, 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 m² (95% CI -7.2 to 3.0), vs -8.4 mL/min/1.73 m² (95% CI -13.2 to -3.6) for CKD-EPI_Cr2021.

Conclusion: MMB-eGFR displayed better performances than equations based on demographics, and could be applied to subjects of various ethnic backgrounds.

Keywords: CT scan; creatinine; cystatin C; estimated glomerular filtration rate; muscle mass; sarcopenia.