Observational study of sudden cardiac arrest risk (OSCAR): Rationale and design of an electronic health records cohort

Background: Out-of-hospital sudden cardiac arrest (SCA) is a major cause of mortality and improved risk prediction is needed. The Observational Study of Sudden Cardiac Arrest Risk (OSCAR) is an electronic health records (EHR)-based cohort study of patients receiving routine medical care in the Cedar...

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Main Authors: Kyndaron Reinier, Harpriya S. Chugh, Audrey Uy-Evanado, Elizabeth Heckard, Marco Mathias, Nichole Bosson, Vinicius F. Calsavara, Piotr J. Slomka, David A. Elashoff, Alex A.T. Bui, Sumeet S Chugh
Format: Article
Language:English
Published: Elsevier 2025-02-01
Series:International Journal of Cardiology: Heart & Vasculature
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Online Access:http://www.sciencedirect.com/science/article/pii/S235290672500017X
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author Kyndaron Reinier
Harpriya S. Chugh
Audrey Uy-Evanado
Elizabeth Heckard
Marco Mathias
Nichole Bosson
Vinicius F. Calsavara
Piotr J. Slomka
David A. Elashoff
Alex A.T. Bui
Sumeet S Chugh
author_facet Kyndaron Reinier
Harpriya S. Chugh
Audrey Uy-Evanado
Elizabeth Heckard
Marco Mathias
Nichole Bosson
Vinicius F. Calsavara
Piotr J. Slomka
David A. Elashoff
Alex A.T. Bui
Sumeet S Chugh
author_sort Kyndaron Reinier
collection DOAJ
description Background: Out-of-hospital sudden cardiac arrest (SCA) is a major cause of mortality and improved risk prediction is needed. The Observational Study of Sudden Cardiac Arrest Risk (OSCAR) is an electronic health records (EHR)-based cohort study of patients receiving routine medical care in the Cedars-Sinai Health System (CSHS) in Los Angeles County, CA designed to evaluate predictors of SCA. This paper describes the rationale, objectives, and study design for the OSCAR cohort. Methods and Results: The OSCAR cohort includes 379,833 Los Angeles County residents with at least one patient encounter at CSHS in each of two consecutive calendar years from 2016 to 2020. We obtained baseline cohort characteristics from the EHR from 2012 until the start of follow-up, including demographics, vital signs, clinical diagnoses, cardiac tests and imaging, procedures, laboratory results, and medications. Follow-up will continue until Dec. 31, 2025, with an expected median follow-up time of ∼ 7 years. The primary outcome is out-of-hospital SCA of likely cardiac etiology attended by Los Angeles County Emergency Medical Services (LAC-EMS). The secondary outcome is total mortality identified using California Department of Public Health – Vital Records death certificates. We will use conventional approaches (diagnosis code algorithms) and artificial intelligence (natural language processing, deep learning) to define patient phenotypes and biostatistical and machine learning approaches for analysis. Conclusions: The OSCAR cohort will provide a large, diverse dataset and adjudicated SCA outcomes to facilitate the derivation and testing of risk prediction models for incident SCA.
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spelling doaj-art-2b4d308e1b0641d9800dbdad3c0f04ec2025-08-20T03:11:06ZengElsevierInternational Journal of Cardiology: Heart & Vasculature2352-90672025-02-015610161410.1016/j.ijcha.2025.101614Observational study of sudden cardiac arrest risk (OSCAR): Rationale and design of an electronic health records cohortKyndaron Reinier0Harpriya S. Chugh1Audrey Uy-Evanado2Elizabeth Heckard3Marco Mathias4Nichole Bosson5Vinicius F. Calsavara6Piotr J. Slomka7David A. Elashoff8Alex A.T. Bui9Sumeet S Chugh10Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center Los Angeles CA USA; Corresponding authors.Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center Los Angeles CA USACenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center Los Angeles CA USACenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center Los Angeles CA USACenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center Los Angeles CA USALos Angeles County EMS Agency Los Angeles CA USA; Harbor-UCLA Medical Center, Department of Emergency Medicine, and The Lundquist Institute Torrance CA USA; David Geffen School of Medicine at UCLA Los Angeles CA USADepartment of Computational Biomedicine, Biostatistics Shared Resource, Cedars-Sinai Medical Center Los Angeles CA USADivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center Los Angeles CA USADepartment of Medicine, Statistics Core, University of California Los Angeles CA USAMedical & Imaging Informatics Group, University of California, Los Angeles 90095 CA, USACenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center Los Angeles CA USA; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center Los Angeles CA USA; Corresponding authors.Background: Out-of-hospital sudden cardiac arrest (SCA) is a major cause of mortality and improved risk prediction is needed. The Observational Study of Sudden Cardiac Arrest Risk (OSCAR) is an electronic health records (EHR)-based cohort study of patients receiving routine medical care in the Cedars-Sinai Health System (CSHS) in Los Angeles County, CA designed to evaluate predictors of SCA. This paper describes the rationale, objectives, and study design for the OSCAR cohort. Methods and Results: The OSCAR cohort includes 379,833 Los Angeles County residents with at least one patient encounter at CSHS in each of two consecutive calendar years from 2016 to 2020. We obtained baseline cohort characteristics from the EHR from 2012 until the start of follow-up, including demographics, vital signs, clinical diagnoses, cardiac tests and imaging, procedures, laboratory results, and medications. Follow-up will continue until Dec. 31, 2025, with an expected median follow-up time of ∼ 7 years. The primary outcome is out-of-hospital SCA of likely cardiac etiology attended by Los Angeles County Emergency Medical Services (LAC-EMS). The secondary outcome is total mortality identified using California Department of Public Health – Vital Records death certificates. We will use conventional approaches (diagnosis code algorithms) and artificial intelligence (natural language processing, deep learning) to define patient phenotypes and biostatistical and machine learning approaches for analysis. Conclusions: The OSCAR cohort will provide a large, diverse dataset and adjudicated SCA outcomes to facilitate the derivation and testing of risk prediction models for incident SCA.http://www.sciencedirect.com/science/article/pii/S235290672500017XSudden cardiac deathOut-of-hospital cardiac arrestElectronic health records cohortPrediction
spellingShingle Kyndaron Reinier
Harpriya S. Chugh
Audrey Uy-Evanado
Elizabeth Heckard
Marco Mathias
Nichole Bosson
Vinicius F. Calsavara
Piotr J. Slomka
David A. Elashoff
Alex A.T. Bui
Sumeet S Chugh
Observational study of sudden cardiac arrest risk (OSCAR): Rationale and design of an electronic health records cohort
International Journal of Cardiology: Heart & Vasculature
Sudden cardiac death
Out-of-hospital cardiac arrest
Electronic health records cohort
Prediction
title Observational study of sudden cardiac arrest risk (OSCAR): Rationale and design of an electronic health records cohort
title_full Observational study of sudden cardiac arrest risk (OSCAR): Rationale and design of an electronic health records cohort
title_fullStr Observational study of sudden cardiac arrest risk (OSCAR): Rationale and design of an electronic health records cohort
title_full_unstemmed Observational study of sudden cardiac arrest risk (OSCAR): Rationale and design of an electronic health records cohort
title_short Observational study of sudden cardiac arrest risk (OSCAR): Rationale and design of an electronic health records cohort
title_sort observational study of sudden cardiac arrest risk oscar rationale and design of an electronic health records cohort
topic Sudden cardiac death
Out-of-hospital cardiac arrest
Electronic health records cohort
Prediction
url http://www.sciencedirect.com/science/article/pii/S235290672500017X
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