AI-BASED Tool to Estimate Sodium Intake in STAGE 3 to 5 CKD Patients—The UniverSel Study

Résumé fourni par la source

Background: Arterial hypertension is highly prevalent among patients with chronic kidney disease (CKD), acting both as a cause and consequence of declining kidney function, and significantly increasing cardiovascular risk. Among modifiable risk factors, diet—particularly excessive sodium intake—plays a central role in the prevention and personalized management of CKD. Methods: This study aimed to develop an innovative, digitally accessible tool to estimate sodium intake in stages 3 to 5 CKD patients, using 24-h urinary sodium excretion as the reference standard. Results: Twenty-five clinical, biological, therapeutic, and dietary variables were collected from 493 patients followed across 6 French centers. A probabilistic Tree-Augmented Naive Bayes model was used to develop the tool based on the 15 most informative variables. The model demonstrated an internal accuracy of 71%, indicating that predicted and observed sodium intake categories matched in 71% of cases. Conclusions: This AI-based prediction model offers a promising clinical tool to estimate daily sodium intake in patients with stages 3 to 5 CKD. However, external validation using independent national and international datasets is essential to establish its robustness and generalizability prior to implementation in routine clinical practice.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.