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Clinical Validation of Vital-PICASO for Predicting Cardiac Arrest Within 24 Hours

NCT07742943 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 387

Last updated 2026-08-03

No results posted yet for this study

Summary

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.

Conditions

  • Heart Arrest

Sponsors & Collaborators

  • Huinno AIM

    lead INDUSTRY

Eligibility

Min Age
19 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-08-20
Primary Completion
2025-12-19
Completion
2025-12-19

Countries

  • South Korea

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07742943 on ClinicalTrials.gov