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

TL;DR

This study developed an AI tool to estimate sodium intake in CKD patients using urine data and clinical variables, aiming to improve personalized management.

Contribution

The novel contribution is an AI-based model using 15 key variables to predict sodium intake in CKD patients with 71% accuracy.

Findings

The model matched predicted and observed sodium intake categories in 71% of cases.

The tool was developed using data from 493 patients across six French centers.

External validation is needed to confirm the model's robustness and generalizability.

Abstract

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.

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