Evidence map›Paper›PMID 39897418›Full record

ArticleInternational journal of cardiology. Heart & vasculature2025

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

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 and 1 more

Abstract read
In one paragraph

Article in International journal of cardiology. Heart & vasculature, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Kyndaron ReinierCenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center Los Angeles CA USA.
Harpriya S ChughCenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center Los Angeles CA USA.
Audrey Uy-EvanadoCenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center Los Angeles CA USA.
Elizabeth HeckardCenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center Los Angeles CA USA.
Marco MathiasCenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center Los Angeles CA USA.
Nichole BossonLos Angeles County EMS Agency Los Angeles CA USA.
Vinicius F CalsavaraDepartment of Computational Biomedicine, Biostatistics Shared Resource, Cedars-Sinai Medical Center Los Angeles CA USA.
Piotr J SlomkaDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center Los Angeles CA USA.
David A ElashoffDepartment of Medicine, Statistics Core, University of California Los Angeles CA USA.
Alex A T BuiMedical & Imaging Informatics Group, University of California, Los Angeles 90095 CA, USA.
Sumeet S ChughCenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center Los Angeles CA USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Electronic health records cohortOut-of-hospital cardiac arrestPredictionSudden cardiac death

Identifiers

PMID39897418
PMCPMC11787554

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.