Development and Validation of a Multi-Modal Algorithm for Chronic Kidney Disease Detection in a Hospital Clinical Data Warehouse.

Chronic kidney disease (CKD) is often underdetected in hospitalized older adults due to incomplete coding and laboratory data. Using eHOP, the clinical data warehouse of Rennes University Hospital, we developed and validated a rule-based interpretable multimodal algorithm combining laboratory and textual information for CKD phenotyping. Among 15,112 hospital visits (7,556 with CKD and 7,556 without), the algorithm achieved an accuracy of 92.1 % in agreement with ICD-10, recall 96.5 %, and precision 86.6 %. Validation on a clinician-reviewed subset confirmed the reliability of classifications. Most CKD cases were detected from clinical text. This approach demonstrates the feasibility of transparent multimodal phenotyping within hospital data and provides a foundation for future scalable decision-support systems in chronic disease surveillance.