Samuel Harford1, Houshang Darabi1, Marina Del Rios2, Somshubra Majumdar1, Fazle Karim1, Terry Vanden Hoek3, Kim Erwin4, Dennis P Watson5. 1. Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, Illinois, United States. 2. Department of Emergency Medicine, University of Illinois at Chicago, Chicago, Illinois, United States. Electronic address: mdelrios@uic.edu. 3. Department of Emergency Medicine, University of Illinois at Chicago, Chicago, Illinois, United States. 4. Department of Population Health Sciences, University of Illinois at Chicago, Chicago, Illinois, United States. 5. Center of Dissemination and Implementation Science, University of Illinois at Chicago, Chicago, Illinois, United States.
Abstract
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.
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.
Authors: Samuel Harford; Marina Del Rios; Sara Heinert; Joseph Weber; Eddie Markul; Katie Tataris; Teri Campbell; Terry Vanden Hoek; Houshang Darabi Journal: BMC Med Inform Decis Mak Date: 2022-01-25 Impact factor: 2.796