Evidence map›Paper›PMID 34993487›Full record

ArticleEuropean heart journal. Digital health2021

ECG-AI: electrocardiographic artificial intelligence model for prediction of heart failure.

Oguz Akbilgic, Liam Butler, Ibrahim Karabayir, Patricia P Chang, Dalane W Kitzman, Alvaro Alonso, Lin Y Chen, Elsayed Z Soliman

Registry-linked trialOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European heart journal. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07163767 (Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images), which is not on this map. Cited by 58 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed, 2 pooled it
8.9field-weighted citation impact, top 1% 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.

NCT07163767 active not recruitingnot on this mapstarted 2025, after this paper: background citation

Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images

TypeobservationalSponsorGuangdong Provincial People's HospitalRan2025 to 2028Enrolled150,000ConditionsAcute Myocardial Infarction (AMI), Electrocardiography, Artifical IntelligenceArmsDeep learning approach of ECG for AMI detection
3 · Its place in the literature

Who cites it

58 citing papers in PubMed, 2 syntheses or guidelines pooled it, 99 citations in OpenAlex.

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  8. ECG Sex Index in children and adolescents.European heart journal. Digital health · 2026
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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

8 authors at 5 institutions in 2 countries.

Oguz AkbilgicDepartment of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, 2160 S 1st Street, Maywood, IL 60153, USA.ORCID https://orcid.org/0000-0003-0313-9254
Liam ButlerDepartment of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, 2160 S 1st Street, Maywood, IL 60153, USA.ORCID https://orcid.org/0000-0001-5880-2282
Ibrahim KarabayirDepartment of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, 2160 S 1st Street, Maywood, IL 60153, USA.ORCID https://orcid.org/0000-0002-7928-176X
Patricia P ChangSections on Cardiovascular Medicine and Geriatrics, Department of Internal Medicine, Wake Forest School of Medicine, 475 Vine Street, Winston-Salem, NC 27101, USA.
Dalane W KitzmanSections on Cardiovascular Medicine and Geriatrics, Department of Internal Medicine, Wake Forest School of Medicine, 475 Vine Street, Winston-Salem, NC 27101, USA.
Alvaro AlonsoDepartment of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Rd. NE Atlanta, GA, 30322, USA.
Lin Y ChenCardiovascular Division, Department of Medicine, University of Minnesota Medical School, 401 East River Parkway, Minneapolis, MN 55455, USA.
Elsayed Z SolimanSections on Cardiovascular Medicine and Geriatrics, Department of Internal Medicine, Wake Forest School of Medicine, 475 Vine Street, Winston-Salem, NC 27101, USA.ORCID https://orcid.org/0000-0001-5632-8150
Wake Forest University · USLoyola University Chicago · USEmory University · USUniversity of Minnesota Medical Center · USUniversity of North Carolina at Chapel Hill · US

Funding

Mentoring in patient-oriented atrial fibrillation and cardiovascular researchK24HL148521 · NHLBI · EMORY UNIVERSITY · PI ALONSO, ALVARO · 2019 to 2023
$593k
NHLBI NIH HHS HHSN268201700001CNHLBI NIH HHS HHSN268201700001INHLBI NIH HHS HHSN268201700002CNHLBI NIH HHS HHSN268201700002INHLBI NIH HHS HHSN268201700003CNHLBI NIH HHS HHSN268201700003INHLBI NIH HHS HHSN268201700004CNHLBI NIH HHS HHSN268201700004INHLBI NIH HHS HHSN268201700005CNHLBI NIH HHS HHSN268201700005INHLBI NIH HHS K24 HL148521
6 · The paper itself

Abstract

aimsHeart failure (HF) is a leading cause of death. Early intervention is the key to reduce HF-related morbidity and mortality. This study assesses the utility of electrocardiograms (ECGs) in HF risk prediction. METHODS AND

resultsData from the baseline visits (1987-89) of the Atherosclerosis Risk in Communities (ARIC) study was used. Incident hospitalized HF events were ascertained by ICD codes. Participants with good quality baseline ECGs were included. Participants with prevalent HF were excluded. ECG-artificial intelligence (AI) model to predict HF was created as a deep residual convolutional neural network (CNN) utilizing standard 12-lead ECG. The area under the receiver operating characteristic curve (AUC) was used to evaluate prediction models including (CNN), light gradient boosting machines (LGBM), and Cox proportional hazards regression. A total of 14 613 (45% male, 73% of white, mean age ± standard deviation of 54 ± 5) participants were eligible. A total of 803 (5.5%) participants developed HF within 10 years from baseline. Convolutional neural network utilizing solely ECG achieved an AUC of 0.756 (0.717-0.795) on the hold-out test data. ARIC and Framingham Heart Study (FHS) HF risk calculators yielded AUC of 0.802 (0.750-0.850) and 0.780 (0.740-0.830). The highest AUC of 0.818 (0.778-0.859) was obtained when ECG-AI model output, age, gender, race, body mass index, smoking status, prevalent coronary heart disease, diabetes mellitus, systolic blood pressure, and heart rate were used as predictors of HF within LGBM. The ECG-AI model output was the most important predictor of HF.

conclusionsECG-AI model based solely on information extracted from ECG independently predicts HF with accuracy comparable to existing FHS and ARIC risk calculators.

Indexed as

ARICArtificial intelligenceDeep learningECGElectrocardiogramHeart failure

Identifiers

PMID34993487
PMCPMC8715759
OpenAlexW3206143748

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

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.