Article in Circulation. Heart failure, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1citing papers in PubMed
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5 · Who and what money
Authors and funding
21 authors.
Shaan Khurshid *Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S. Khurshid, S. Kany, J.W.C., E.S.L., D.P., M.A.A.-A., J.T.R., S.A.L., P.T.E., J.E.H.).ORCID 0000-0002-2840-4539
Samuel F Friedman *Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA (S.F.F., C.R., A.A.P., M.M.).ORCID 0000-0002-0688-2169
Shinwan Kany *Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S. Khurshid, S. Kany, J.W.C., E.S.L., D.P., M.A.A.-A., J.T.R., S.A.L., P.T.E., J.E.H.).ORCID 0000-0001-8113-733X
Valentina D'SouzaTelemachus and Irene Demoulas Family Foundation Center for Cardiac Arrhythmias, Heart and Vascular Institute, Mass General Brigham, Boston (S. Khurshid, V.D.S., P.S., D.P., M.A.A.-A., S.A.L., P.T.E.).
Athar RoshandelpoorCardiology Division, Beth Israel Deaconess Medical Center, Boston, MA (A.R., L.B.K., M.C., J.E.H.).
Leah B KosyakovskyCardiology Division, Beth Israel Deaconess Medical Center, Boston, MA (A.R., L.B.K., M.C., J.E.H.).
Mandana ChitsazanCardiology Division, Beth Israel Deaconess Medical Center, Boston, MA (A.R., L.B.K., M.C., J.E.H.).ORCID 0000-0003-4108-1620
Jonathan W CunninghamCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S. Khurshid, S. Kany, J.W.C., E.S.L., D.P., M.A.A.-A., J.T.R., S.A.L., P.T.E., J.E.H.).ORCID 0000-0003-4481-7867
Pulkit SinghTelemachus and Irene Demoulas Family Foundation Center for Cardiac Arrhythmias, Heart and Vascular Institute, Mass General Brigham, Boston (S. Khurshid, V.D.S., P.S., D.P., M.A.A.-A., S.A.L., P.T.E.).
Emily S LauCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S. Khurshid, S. Kany, J.W.C., E.S.L., D.P., M.A.A.-A., J.T.R., S.A.L., P.T.E., J.E.H.).ORCID 0000-0001-9361-6397
Daniel PipilasCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S. Khurshid, S. Kany, J.W.C., E.S.L., D.P., M.A.A.-A., J.T.R., S.A.L., P.T.E., J.E.H.).ORCID 0000-0001-9577-2801
Mostafa A Al-AlusiCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S. Khurshid, S. Kany, J.W.C., E.S.L., D.P., M.A.A.-A., J.T.R., S.A.L., P.T.E., J.E.H.).ORCID 0000-0002-9359-1641
Joel T RämöCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S. Khurshid, S. Kany, J.W.C., E.S.L., D.P., M.A.A.-A., J.T.R., S.A.L., P.T.E., J.E.H.).ORCID 0000-0002-6429-5149
James P PirruccelloInstitute for Human Genetics (J.P.P.), University of California San Francisco, CA.ORCID 0000-0001-6088-4037
Christopher ReederData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA (S.F.F., C.R., A.A.P., M.M.).
Jonathan W WaksHarvard-Thorndike Electrophysiology Institute, Beth Israel Deaconess Medical Center, Boston, MA (J.W.W.).ORCID 0000-0001-5560-5638
Steven A LubitzCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S. Khurshid, S. Kany, J.W.C., E.S.L., D.P., M.A.A.-A., J.T.R., S.A.L., P.T.E., J.E.H.).ORCID 0000-0002-9599-4866
Anthony A PhilippakisData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA (S.F.F., C.R., A.A.P., M.M.).
Mahnaz MaddahData Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA (S.F.F., C.R., A.A.P., M.M.).ORCID 0000-0002-9837-6000
Patrick T EllinorCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S. Khurshid, S. Kany, J.W.C., E.S.L., D.P., M.A.A.-A., J.T.R., S.A.L., P.T.E., J.E.H.).ORCID 0000-0002-2067-0533
Jennifer E HoCardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S. Khurshid, S. Kany, J.W.C., E.S.L., D.P., M.A.A.-A., J.T.R., S.A.L., P.T.E., J.E.H.).ORCID 0000-0002-7987-4768
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
Endothelial Cell Health Across the Spectrum of Cardiometabolic DiseaseR01HL168889 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Naomi Miriam Hamburg, Jennifer E Ho · 2023 to 2026
$3.1M
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
Long-Term Endothelial Effects of COVID-19 in ObesityR01HL160003 · NHLBI · BETH ISRAEL DEACONESS MEDICAL CENTER · PI HAMBURG, NAOMI MIRIAM, HO, JENNIFER E · 2022 to 2025
$2.8M
Training Program in Cardiovascular ResearchT32HL160522 · NHLBI · BETH ISRAEL DEACONESS MEDICAL CENTER · PI Jennifer E Ho · 2022 to 2026
$2.7M
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
Electrocardiogram-based deep learning and decision analysis to improve atrial fibrillation risk estimationK23HL169839 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Shaan Khurshid · 2023 to 2026
$860k
Deep learning to enable the genetic analysis of aortaK08HL159346 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PIRRUCCELLO, JAMES · 2021 to 2025
$827k
Mentoring in Patient-Oriented and Translational HFpEF ResearchK24HL153669 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI Jennifer E Ho · 2020 to 2026
backgroundECG-based artificial intelligence may enable efficient prediction of incident heart failure (HF) risk to facilitate preventive efforts. Prior models are proprietary, with modest or inconsistent accuracy. We sought to develop and validate a generalizable and publicly available convolutional neural network to predict incident HF using the 12-lead ECG waveform (ECG-to-HF [ECG2HF]).
methodsWe developed ECG2HF in 94 636 patients receiving longitudinal ambulatory care at Massachusetts General Hospital (MGH), and validated it in 3 test sets: MGH, Brigham and Women's Hospital (BWH), and Beth Israel Deaconess Medical Center (BIDMC), among 93 868 individuals aged 30 to 79 years without HF. HF events at 10 years were identified using a validated electronic health record-based natural language processing model. Discrimination was quantified using the area under the receiver operating characteristic curve. We then compared discrimination and net reclassification (at <10%, 10% to 20%, ≥20% 10-year risk categories) using ECG2HF versus the 15-component Pooled Cohorts Equations to Prevent HF score.
resultsThe test sets comprised MGH (13 954 individuals, 441 events, age 57±13 years, 48% women), BWH (54 396 individuals, 1809 events, age 57±13 years, 55% women), and BIDMC (25 457 individuals, 901 events, age 57±13 years, 53% women). Over 10 years, the cumulative risk of HF was 4.6% (95% CI, 4.1-5.0) in MGH, 5.0% (4.8-5.2) in BWH, and 4.4% (4.1-4.7) in BIDMC. ECG2HF discriminated 10-year incident HF in each test set (area under the receiver operating characteristic curve: MGH 0.86 [0.84-0.87]; BWH 0.85 [0.84-0.86]; BIDMC 0.84 [0.83-0.86]). Compared with the Pooled Cohorts Equations to Prevent HF, ECG2HF provided favorable discrimination (improvement in area under the receiver operating characteristic curve MGH/BWH 0.061 [0.025-0.097]; BIDMC 0.038 [-0.0096 to 0.086]) and net reclassification (NRI MGH/BWH 0.16 [0.077-0.24]; BIDMC 0.23 [0.10-0.35]) of 10-year HF risk.
conclusionsECG2HF is a publicly available 12-lead ECG-based artificial intelligence model that discriminates the risk of future HF with favorable and consistent performance across 3 large health care samples from the northeastern United States. ECG2HF may enable efficient prioritization of high-risk individuals for HF-related preventive measures.
Indexed as
Artificial IntelligenceElectrocardiographyHeart FailureAdultAgedConvolutional Neural NetworksElectronic Health RecordsFemaleHumansIncidenceIntelligent SystemsMaleMiddle AgedPrediction AlgorithmsPredictive Value of TestsReproducibility of Resultsartificial intelligenceelectrocardiogramheart failurehumansROC curve
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.
Artificial Intelligence-Enabled ECG Analysis to Predict Incident Heart Failure. · full record | OpenQuestion