Evidence map›Paper›PMID 42034100›Full record

ArticleJACC. Advances2026

Prediction of Pregnancy-Related Cardiovascular Outcomes Using Electrocardiogram-Based Deep Learning Estimation of Cardiorespiratory Fitness.

Logan Brown, Yunong Zhao, Valentina D'Souza, Danielle Pace, Kaitlyn James, Camille Powe, Timothy Churchill, Katherine E Economy, Mahnaz Maddah, Patrick T Ellinor 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.

Logan BrownDivision of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Yunong ZhaoDivision of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Valentina D'SouzaData Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Danielle PaceData Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Kaitlyn JamesDepartment of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Camille PoweDepartment of Obstetrics and Gynecology, Massachusetts General Hospital, Boston, Massachusetts, USA; Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
Timothy ChurchillDivision of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Katherine E EconomyDepartment of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Mahnaz MaddahData Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Patrick T EllinorDivision of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Disease Institute, The Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA; CardioVascular Institute and Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Jennifer E HoCardioVascular Institute and Division of Cardiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
Shaan KhurshidDivision of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Disease Institute, The Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA; Telemachus and Irene Demoulas Family Foundation Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts, USA.
James Sawalla GusehDivision of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA.
Emily S LauDivision of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Disease Institute, The Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA. Electronic address: elau6@mgh.harvard.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

backgroundPeak oxygen consumption (peak VO

objectivesThe purpose of this study was to examine the association of deep learning ECG-predicted peak VO

methodsWe evaluated ECG-estimated peak VO

resultsAmong 3,650 pregnancies from 3,437 women (mean age at delivery 33 ± 6 years), median ECG-estimated VO

conclusionsLower ECG-estimated cardiorespiratory fitness was associated with a higher risk of pregnancy-related CV complications, supporting artificial intelligence-enabled ECG analysis as a scalable tool for antepartum risk stratification of pregnancy-related CV complications.

Indexed as

cardiorespiratory fitnesscardiovascular outcomesdeep learningpregnancyrisk prognostication

Identifiers

PMID42034100
PMCPMC13129435

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.