Journal article
A machine learning based model for Out of Hospital cardiac arrest outcome classification and sensitivity analysis
Resuscitation, Vol.138, pp.134-140
05/2019
DOI: 10.1016/j.resuscitation.2019.03.012
PMID: 30885826
Abstract
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.
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.
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.
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.
Details
- Title: Subtitle
- A machine learning based model for Out of Hospital cardiac arrest outcome classification and sensitivity analysis
- Creators
- Samuel Harford - University of Illinois at ChicagoHoushang Darabi - University of Illinois at ChicagoMarina Del Rios - University of Illinois at ChicagoSomshubra Majumdar - University of Illinois at ChicagoFazle Karim - University of Illinois at ChicagoTerry Vanden Hoek - University of Illinois at ChicagoKim Erwin - University of Illinois at ChicagoDennis P. Watson - University of Illinois at Chicago
- Resource Type
- Journal article
- Publication Details
- Resuscitation, Vol.138, pp.134-140
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.resuscitation.2019.03.012
- PMID
- 30885826
- ISSN
- 0300-9572
- eISSN
- 1873-1570
- Grant note
- DOI: 10.13039/100004374, name: Medtronic
- Language
- English
- Date published
- 05/2019
- Academic Unit
- Emergency Medicine
- Record Identifier
- 9984297146902771
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