Evidence map›Paper›PMID 36046430›Full record

ArticleCardiovascular digital health journal2022

Deep learning on resting electrocardiogram to identify impaired heart rate recovery.

Nathaniel Diamant, Paolo Di Achille, Lu-Chen Weng, Emily S Lau, Shaan Khurshid, Samuel Friedman, Christopher Reeder, Pulkit Singh, Xin Wang, Gopal Sarma and 12 more

Open access · goldAbstract read
In one paragraph

Article in Cardiovascular digital health journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.5field-weighted citation impact, top 17% of its field
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

3 citing papers in PubMed, 10 citations in OpenAlex.

  1. Deep learning for electrocardiogram interpretation: Bench to bedside.European journal of clinical investigation · 2025
    Review
  2. Article
  3. Review
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

22 authors at 2 institutions in 2 countries.

Nathaniel DiamantData Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.
Paolo Di AchilleData Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.
Lu-Chen WengCardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts.
Emily S LauCardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts.
Shaan KhurshidCardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts.
Samuel FriedmanData Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.
Christopher ReederData Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.
Pulkit SinghData Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.
Xin WangCardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts.
Gopal SarmaData Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.
Mercedeh GhadessiBayer, AG, Research and Development, Pharmaceuticals, Leverkusen, Germany.
Johanna MielkeBayer, AG, Research and Development, Pharmaceuticals, Wuppertal, Germany.
Eren ElciBayer, AG, Research and Development, Pharmaceuticals, Wuppertal, Germany.
Ivan KryukovBayer, AG, Research and Development, Pharmaceuticals, Wuppertal, Germany.
Hanna M EilkenBayer, AG, Research and Development, Pharmaceuticals, Leverkusen, Germany.
Andrea DerixBayer, AG, Research and Development, Pharmaceuticals, Leverkusen, Germany.
Patrick T EllinorCardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts.
Christopher D AndersonCardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.
Anthony A PhilippakisData Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.
Puneet BatraData Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.
Steven A LubitzCardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts.
Jennifer E HoCardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.
Broad Institute · USBayer (Germany) · DE

Funding

IDENTIFICATION OF COMMON GENETIC VARIANTS FOR ATRIAL FIBRILLATION AND PR INTERVALR01HL092577 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI BENJAMIN, EMELIA J., ELLINOR, PATRICK THOMAS · 2009 to 2025
$20.6M
MULTIDISCIPLINARY RESEARCH TRAINING IN CARDIOLOGYT32HL007208 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Patrick Thomas Ellinor, David E Sosnovik · 1985 to 2026
$16.1M
Sequencing Annotation and Functional Analysis in Risk of Intracerebral HemorrhageR01NS103924 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI ANDERSON, CHRISTOPHER DAVID · 2018 to 2022
$3.4M
Genomics of Cardiac ArrhythmiasR01HL139731 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI ELLINOR, PATRICK THOMAS · 2018 to 2022
$3.3M
Eicosanoid Profiles as Determinants of HFpEFR01HL140224 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI HO, JENNIFER E · 2018 to 2021
$3.1M
The Association of Metabolic Disease and Pulmonary HypertensionR01HL134893 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI HO, JENNIFER E · 2017 to 2021
$2.9M
Mentoring in Arrhythmia ResearchK24HL105780 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI ELLINOR, PATRICK THOMAS · 2011 to 2020
$1.2M
HFpEF Susceptibility in Women: The Role of InflammationK23HL159243 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Emily Lau · 2022 to 2026
$1.1M
Mentoring in Patient-Oriented and Translational HFpEF ResearchK24HL153669 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Jennifer E Ho · 2020 to 2026
$744k
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NHLBI NIH HHS K23 HL159243NHLBI NIH HHS K24 HL105780NHLBI NIH HHS K24 HL153669NHLBI NIH HHS R01 HL092577NHLBI NIH HHS R01 HL134893NHLBI NIH HHS R01 HL139731NHLBI NIH HHS R01 HL140224NHLBI NIH HHS T32 HL007208NINDS NIH HHS R01 NS103924
6 · The paper itself

Abstract

Background and Objective: Postexercise heart rate recovery (HRR) is an important indicator of cardiac autonomic function and abnormal HRR is associated with adverse outcomes. We hypothesized that deep learning on resting electrocardiogram (ECG) tracings may identify individuals with impaired HRR. Methods: We trained a deep learning model (convolutional neural network) to infer HRR based on resting ECG waveforms (HRR Results: Among 56,793 individuals (mean age 57 years, 51% women), the HRR Conclusion: Deep learning-derived estimates of HRR using resting ECG independently associated with future clinical outcomes, including new-onset DM and all-cause mortality. Inferring postexercise heart rate response from a resting ECG may have potential clinical implications and impact on preventive strategies warrants future study.

Indexed as

Diabetes mellitusElectrocardiogramHeart failureMachine learningRisk factor

Identifiers

PMID36046430
PMCPMC9422063
OpenAlexW4283373867

What OpenQuestion 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 OpenQuestion graph.