This retrospective study evaluated the clinical performance of Vital-PICASO, an artificial intelligence-based biological signal analysis software designed to predict the risk of hypotension within 24 hours using vital-sign data from adult general ward inpatients.
Electronic medical record data from patients aged 19 years or older who were admitted to a general ward at Seoul National University Hospital were retrospectively reviewed. Hypotension was defined as systolic blood pressure below 90 mmHg. Eligible data were classified as hypotension-positive or hypotension-negative according to predefined reference-standard criteria.
Vital-PICASO was applied to the selected vital-sign datasets, and the software-generated hypotension risk scores were compared with the reference-standard classifications. Predictive performance was evaluated separately using data that included oxygen saturation and data that excluded oxygen saturation. Because the study used previously collected medical records, there was no direct participant contact and the software results were not used to guide patient care.