Using a Neural Network Architecture for the Prediction of Neurologic Outcome for Out-of-Hospital Cardiac Arrests Using Hospital Level Variables and Novel Physiologic Markers

Details

• Personal Author:
  Buhimschi IA ; Darabi H ; Del Rios M ; Khosla S ; Kotini P ; Li J ; Razo M ; Vanden Hoek T
• Description:
  Out-of-hospital cardiac arrest (OHCA) is a major public health burden due to its high mortality rate, sudden nature, and long-term impact on survivors. Consequently, there is a crucial need to create prediction models to better understand patient trajectories and assist clinicians and families in making informed decisions. We studied 107 adult OHCA patients admitted at an academic Emergency Department (ED) from 2018-2023. Blood samples and ocular ultrasounds were acquired at 1, 6, and 24 h after return of spontaneous circulation (ROSC). Six classes of clinical and novel variables were used: (1) Vital signs after ROSC, (2) pre-hospital and ED data, (3) hospital admission data, (4) ocular ultrasound parameters, (5) plasma protein biomarkers and (6) sex steroid hormones. A base model was built using 1 h variables in classes 1-3, reasoning these are available in most EDs. Extending from the base model, we evaluated 26 distinct neural network models for prediction of neurological outcome by the cerebral performance category (CPC) score. The top-performing model consisted of all variables at 1 h resulting in an AUROC score of 0.946. We determined a parsimonious set of variables that optimally predicts CPC score. Our research emphasizes the added value of incorporating ocular ultrasound, plasma biomarkers, sex hormones in the development of more robust predictive models for neurological outcome after OHCA. [Description provided by NIOSH]
• Subjects:
  Automation Biomarkers Cardiovascular Diseases Cardiovascular System Occupational Health Safety
• Keywords:
  Author Keywords: Neural Network Cardiac Arrest Machine Learning Neural Networks Neurological Reactions Prediction Model SHAP Analysis
• ISSN:
  2306-5354
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Chicago Region [OSHA] ; Illinois
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  12
• Issue:
  2
• NIOSHTIC Number:
  nn:20070565
• Citation:
  Bioengineering 2025 Feb; 12(2):124
• Contact Point Address:
  Houshang Darabi, Department of Mechanical and Industrial Engineering, University of Illinois Chicago, 942 W Taylor St., Chicago, IL 60607, USA
• Email:
  hdarabi@uic.edu
• Federal Fiscal Year:
  2025
• Performing Organization:
  University of Illinois at Chicago
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Bioengineering
• End Date:
  20290630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:904ce67f1e4f4d4224f7ec63cb9d88fd3ea74e8d799d694244c205a8de20491d525fbcb22174e5e30cb9589eb603fd5410e5f5fb6155381b868965b3af759540
• Download URL:
  https://stacks.cdc.gov/view/cdc/208748/cdc_208748_DS1.pdf
• File Type:
  [PDF - 687.47 KB ]