External Validation of Deep Learning-Based Cardiac Arrest Risk Management System for Predicting In-Hospital Cardiac Arrest in Patients Admitted to General Wards Based on Rapid Response System Operating and Nonoperating Periods: A Single-Center Study.

Journal: Critical care medicine
PMID:

Abstract

OBJECTIVES: The limitations of current early warning scores have prompted the development of deep learning-based systems, such as deep learning-based cardiac arrest risk management systems (DeepCARS). Unfortunately, in South Korea, only two institutions operate 24-hour Rapid Response System (RRS), whereas most hospitals have part-time or no RRS coverage at all. This study validated the predictive performance of DeepCARS during RRS operation and nonoperation periods and explored its potential beyond RRS operating hours.

Authors

  • Kyung-Jae Cho
    VUNO, Seoul, South Korea.
  • Kwan Hyung Kim
    Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Jaewoo Choi
    Department of Research and Development, VUNO, Seoul, Republic of Korea.
  • Dongjoon Yoo
    Department of Research and Development, VUNO, Seoul, Republic of Korea.
  • Jeongmin Kim
    Department of Anesthesiology and Pain Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.