Evidence map›Paper›PMID 42114451›Full record

ArticleJACC. Advances2026

Artificial Intelligence-Enhanced Electrocardiography and Health Records to Predict Cardiac Arrest.

Surbhi Sharma, Jennifer A Brody, Sam F Friedman, Matthew J Magoon, Mahnaz Maddah, Patrick T Ellinor, Jennifer E Ho, James S Guseh, Thomas D Rea, Michael R Sayre and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JACC. Advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Surbhi SharmaDepartment of Bioengineering, University of Washington, Seattle, Washington, USA.
Jennifer A BrodyCardiovascular Health Research Unit, University of Washington, Seattle, Washington, USA.
Sam F FriedmanData Sciences Platform, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Matthew J MagoonDepartment of Bioengineering, University of Washington, Seattle, Washington, USA.
Mahnaz MaddahData Sciences Platform, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Patrick T EllinorCardiovascular Research Center, Heart and Vascular Institute, Mass General Brigham, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Telemachus and Irene Demoulas Family Foundation Center for Cardiac Arrhythmias, Heart and Vascular Institute, Mass General Brigham, Boston, Massachusetts, USA.
Jennifer E HoDivision of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
James S GusehCardiovascular Research Center, Heart and Vascular Institute, Mass General Brigham, Boston, Massachusetts, USA.
Thomas D ReaDepartment of Medicine, University of Washington, Seattle, Washington, USA; King County Emergency Medical Services, Seattle, Washington, USA.
Michael R SayreDepartment of Emergency Medicine, University of Washington, Seattle, Washington, USA; Seattle Fire Department, Seattle, Washington, USA.
Ali ShojaieDepartment of Biostatistics, University of Washington, Seattle, Washington, USA.
Patrick M BoyleDepartment of Bioengineering, University of Washington, Seattle, Washington, USA; Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, Washington, USA; Center for Cardiovascular Biology, University of Washington, Seattle, Washington, USA. Electronic address: pmjboyle@uw.edu.
Shaan KhurshidCardiovascular Research Center, Heart and Vascular Institute, Mass General Brigham, Boston, Massachusetts, USA; Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Telemachus and Irene Demoulas Family Foundation Center for Cardiac Arrhythmias, Heart and Vascular Institute, Mass General Brigham, Boston, Massachusetts, USA.
Neal A ChatterjeeDivision of Cardiology, Department of Medicine, University of Washington, Seattle, Washington, USA. Electronic address: nchatter@uw.edu.

Funding

Electrocardiogram-based deep learning and decision analysis to improve atrial fibrillation risk estimationK23HL169839 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Shaan Khurshid · 2023 to 2026
$860k
NHLBI NIH HHS K23 HL169839
6 · The paper itself

Abstract

backgroundOut-of-hospital cardiac arrest (OHCA) is a public health burden with the majority occurring in the general population for whom there is no firm strategy to predict risk.

objectivesThe authors evaluated whether artificial intelligence enhanced electrocardiography (ECG) and clinical information from electronic health records (EHRs) can stratify risk of OHCA in the general population.

methodsWe use a case-control study design (matching on age and sex), to derive and temporally validate models to predict OHCA. To evaluate the potential use case of models in a real-world context, we evaluated the 2-year cumulative incidence of OHCA in individuals undergoing ECG in a health care system, while accounting for the competing risk of non-OHCA mortality.

resultsIn the temporal validation cohort, discrimination of OHCA was highest for the multimodal ECG + EHR model (area under the receiver operating characteristic curve: 0.83; area under the precision recall curve: 0.44) followed by the EHR-only model and the ECG-only model (Bonferroni adjusted P for all pairwise comparisons <0.05). In the real-world cohort of individuals undergoing ECG, the EHR + ECG model flagged two-thirds (153 of 228) of those with incident OHCA over a 2-year period as high-risk. Using the ECG + EHR model, the 2-year cumulative incidence of OHCA was 2.4% (95% CI: 2.0%-2.8%) in individuals identified as high-risk compared with 0.5% (95% CI: 0.3%-0.8%) in individuals designated as low risk.

conclusionsIn a large U.S. health care system, artificial intelligence-enhanced ECG and EHR data effectively discriminated individuals at risk of OHCA and identified those at clinically relevant risk of incident OHCA over a 2-year period.

Indexed as

artificial intelligencecardiac arrestelectrocardiogram

Identifiers

PMID42114451
PMCPMC13195318

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.