Hélène Leray-Moragues, Marion Morena, Nicolas Molinari, Leila Chenine, Bernard Canaud, Lotfi Chalabi, Annie Rodriguez, Alexandre Vallée, Jean-Paul Cristol, Centre Hospitalier Universitaire de Montpellier (CHU Montpellier ), Institut de Recherche de Formation en Dyalise, Partenaires INRAE, Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie (MISTEA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), Institut de Recherche Formation en Dyalise, Association Installation à Domicile Epurations Rénales (AIDER), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Nutrition et Alimentation des Populations aux Suds (NutriPass), Université Montpellier 1 (UM1)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)
International audience; Background and Objectives: Protein-energy wasting is common in long-term haemodialysis (HD) patients with chronic kidney disease and is associated with increased morbidity and mortality. The creatinine index (CI) is a simple and useful nutritional parameter reflecting the dietary skeletal muscle protein intake and skeletal muscle mass of the patient. Because of the complexity of creatinine kinetic modeling (CKM) to derive CI, we developed a more simplified formula to estimate CI in HD patients. Design, Setting, Participants & Measurements: A large database of 549 HD patients followed over more than 20 years including monthly CKM-derived CI values was used to develop a simple equation based on patient demographics, predialysis serum creatinine values and dialysis dose ((sp)Kt/V) using mixed regression models. Results: The equation to estimate CI was developed based on age, gender, pre-dialysis serum creatinine concentrations and (sp)Kt/V urea. The equation-derived CI correlated strongly with the measured CI using CKM (correlation coefficient = 0.79, p-value < 0.001). The mean error of CI prediction using the equation was 13.47%. Preliminary examples of few typical HD patients have been used to illustrate the clinical relevance and potential usefulness of CI. Conclusions: The elementary equation used to derive CI using demographic parameters, pre-dialysis serum creatinine concentrations and dialysis dose is a simple and accurate surrogate measure for muscle mass estimation. However, the predictive value of the simplified CI assessment method on mortality deserves further evaluation in large cohorts of HD patients.