A Machine Learning Based Model for out of Hospital Cardiac Arrest Outcome Classification and Sensitivity Analysis

Details

• Personal Author:
  Darabi H ; Del Rios M ; Erwin K ; Harford S ; Karim F ; Majumdar S ; Vanden Hoek T ; Watson DP
• Description:
  Background: Out-of-hospital cardiac arrest (OHCA) affects nearly 400,000 people each year in the United States of which only 10% survive. Using data from the Cardiac Arrest Registry to Enhance Survival (CARES), and machine learning (ML) techniques, we developed a model of neurological outcome prediction for OHCA in Chicago, Illinois. Methods: Rescue workflow data of 2639 patients with witnessed OHCA were retrieved from Chicago's CARES. An Embedded Fully Convolutional Network (EFCN) classification model was selected to predict the patient outcome (survival with good neurological outcomes or not) based on 27 input features with the objective of maximizing the average class sensitivity. Using this model, sensitivity analysis of intervention variables such as bystander cardiopulmonary resuscitation (CPR), targeted temperature management, and coronary angiography was conducted. Results: The EFCN classification model has an average class sensitivity of 0.825. Sensitivity analysis of patient outcome shows that an additional 33 patients would have survived with good neurological outcome if they had received lay person CPR in addition to CPR by emergency medical services and 88 additional patients would have survived if they had received the coronary angiography intervention. Conclusions: ML modeling of the complex Chicago OHCA rescue system can predict neurologic outcomes with a reasonable level of accuracy and can be used to support intervention decisions such as CPR or coronary angiography. The discriminative ability of this ML model requires validation in external cohorts to establish generalizability. [Description provided by NIOSH]
• Subjects:
  Cardiovascular Diseases Cardiovascular System Delivery Of Health Care Emergency Responders Occupational Health Safety Software
• Keywords:
  Author Keywords: Out Of Hospital Cardiac Arrest Cardiac Arrest Computerized Simulation Computer Software Emergency Treatment Health Care Machine Learning Medical Care Neurological Outcome
• ISSN:
  0300-9572
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Illinois ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  134-140
• Volume:
  138
• NIOSHTIC Number:
  nn:20063643
• Citation:
  Resuscitation 2019 May; 138:134-140
• Contact Point Address:
  Marina Del Rios, Department of Emergency Medicine, University of Illinois at Chicago, Chicago, Illinois, United States
• Email:
  mdelrios@uic.edu
• Federal Fiscal Year:
  2019
• Performing Organization:
  University of Illinois at Chicago
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Resuscitation
• End Date:
  20290630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:8c7983aeeb4152844c00b4bb6ba9471f67dee827eb826bf13cb62d4f3161a13eb217f7d7a7de396ceb760e6fb54945c3ec38cdb2b2361cc01223651c5de116f9
• Download URL:
  https://stacks.cdc.gov/view/cdc/222211/cdc_222211_DS1.pdf
• File Type:
  [PDF - 703.93 KB ]