The purpose of this retrospective study is to evaluate the clinical performance of Vital-PICASO, an artificial intelligence-based biological signal analysis software designed to predict the risk of in-hospital cardiac arrest within 24 hours using vital-sign data collected 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 will be retrospectively reviewed. Vital-sign variables include systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, body temperature, and, when available, oxygen saturation.
Eligible data will be classified as cardiac arrest-positive or cardiac arrest-negative according to predefined reference-standard criteria. The blinded datasets will then be analyzed using Vital-PICASO, and the software-generated risk scores will be compared with the reference-standard classifications. Predictive performance will be evaluated separately using models that include oxygen saturation and models that do not include oxygen saturation.
Inclusion Criteria:
General Criteria
Cardiac Arrest-Positive Group
Cardiac Arrest-Negative Group
Exclusion Criteria:
General ward inpatients with an eligible in-hospital cardiac arrest documented in the electronic medical record and followed by resuscitation involving cardiopulmonary resuscitation, defibrillation, or advanced life support.
General ward inpatients without cardiac arrest or other predefined clinical deterioration during the selected hospitalization and observation period.
Seoul, South Korea
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