Utilizing community level factors to improve prediction of out of hospital cardiac arrest outcome using machine learning

Sam Harford; Houshang Darabi; Sara Heinert; Joseph Weber; Teri Campbell; Pavitra Kotini-Shah; Eddie Markul; Katie Tataris; Terry Vanden Hoek; Marina Del Rios

doi:10.1016/j.resuscitation.2022.07.006

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Utilizing community level factors to improve prediction of out of hospital cardiac arrest outcome using machine learning

Journal article

Peer reviewed

Utilizing community level factors to improve prediction of out of hospital cardiac arrest outcome using machine learning

Sam Harford, Houshang Darabi, Sara Heinert, Joseph Weber, Teri Campbell, Pavitra Kotini-Shah, Eddie Markul, Katie Tataris, Terry Vanden Hoek and Marina Del Rios

Resuscitation, Vol.178, pp.78-84

09/2022

DOI: 10.1016/j.resuscitation.2022.07.006

PMCID: PMC9728593

PMID: 35817268

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Abstract

To evaluate the impact of community level information on the predictability of out-of-hospital cardiac arrest (OHCA) survival. We used the Cardiac Arrest Registry to Enhance Survival (CARES) to geocode 9,595 Chicago incidents from 2014 to 2019 into community areas. Community variables including crime, healthcare, and economic factors from public data were merged with CARES. The merged data were used to develop ML models for OHCA survival. Models were evaluated using Area Under the Receiver Operating Characteristic curve (AUROC) and features were analyzed using SHapley Additive exPansion (SHAP) values. Baseline results using CARES data achieved an AUROC of 84%. The final model utilizing community variables increased the AUROC to 88%. A SHAP analysis between high and low performing community area clusters showed the high performing cluster is positively impacted by good health related features and good community safety features positively impact the low performing cluster. Utilizing community variables helps predict neurologic outcomes with better performance than only CARES data. Future studies will use this model to perform simulations to identify interventions to improve OHCA survival.

Machine Learning

Community

Health disparities

Out of hospital cardiac arrest

Survival

Details

Title: Subtitle: Utilizing community level factors to improve prediction of out of hospital cardiac arrest outcome using machine learning
Creators: Sam Harford - University of Illinois Chicago
Houshang Darabi - University of Illinois Chicago
Sara Heinert - Rutgers, The State University of New Jersey
Joseph Weber - John H. Stroger, Jr. Hospital of Cook County
Teri Campbell - University of Chicago
Pavitra Kotini-Shah - University of Illinois Chicago
Eddie Markul - Advocate Illinois Masonic Medical Center
Katie Tataris - University of Chicago
Terry Vanden Hoek - University of Illinois Chicago
Marina Del Rios - University of Iowa
Resource Type: Journal article
Publication Details: Resuscitation, Vol.178, pp.78-84
DOI: 10.1016/j.resuscitation.2022.07.006
PMID: 35817268
PMCID: PMC9728593
NLM abbreviation: Resuscitation
ISSN: 0300-9572
eISSN: 1873-1570
Publisher: Elsevier B.V
Grant note: DOI: 10.13039/100008523, name: Center for Clinical and Translational Science, University of Illinois at Chicago, award: UL1TR002003
Language: English
Date published: 09/2022
Academic Unit: Emergency Medicine
Record Identifier: 9984297137902771

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8 Times Cited - Web of Science