Evidence map›Paper›PMID 39221857›Full record

ArticleCirculation. Cardiovascular quality and outcomes2025

Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images.

Evangelos K Oikonomou, Veer Sangha, Lovedeep S Dhingra, Arya Aminorroaya, Andreas Coppi, Harlan M Krumholz, Lauren A Baldassarre, Rohan Khera

Abstract read
In one paragraph

Article in Circulation. Cardiovascular quality and outcomes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 1 pooled it
–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

45 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  14. Signaling pathways and potential therapeutic agents in trastuzumab-induced cardiotoxicity.Apoptosis : an international journal on programmed cell death · 2026
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  15. Research Priorities and Future Directions in Cardio-Oncology.Current treatment options in oncology · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Evangelos K OikonomouSection of Cardiovascular Medicine, Department of Internal Medicine (E.K.O., V.S., L.S.D., A.A., H.M.K., L.A.B., R.K.), Yale School of Medicine, New Haven, CT.ORCID 0000-0003-4362-0720
Veer SanghaSection of Cardiovascular Medicine, Department of Internal Medicine (E.K.O., V.S., L.S.D., A.A., H.M.K., L.A.B., R.K.), Yale School of Medicine, New Haven, CT.ORCID 0000-0002-8524-1203
Lovedeep S DhingraSection of Cardiovascular Medicine, Department of Internal Medicine (E.K.O., V.S., L.S.D., A.A., H.M.K., L.A.B., R.K.), Yale School of Medicine, New Haven, CT.ORCID 0000-0002-5664-4126
Arya AminorroayaSection of Cardiovascular Medicine, Department of Internal Medicine (E.K.O., V.S., L.S.D., A.A., H.M.K., L.A.B., R.K.), Yale School of Medicine, New Haven, CT.ORCID 0000-0003-3197-2657
Andreas CoppiCenter for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT (A.C., H.M.K., R.K.).ORCID 0000-0002-5243-552X
Harlan M KrumholzSection of Cardiovascular Medicine, Department of Internal Medicine (E.K.O., V.S., L.S.D., A.A., H.M.K., L.A.B., R.K.), Yale School of Medicine, New Haven, CT.ORCID 0000-0003-2046-127X
Lauren A BaldassarreSection of Cardiovascular Medicine, Department of Internal Medicine (E.K.O., V.S., L.S.D., A.A., H.M.K., L.A.B., R.K.), Yale School of Medicine, New Haven, CT.ORCID 0000-0003-4173-3449
Rohan KheraSection of Cardiovascular Medicine, Department of Internal Medicine (E.K.O., V.S., L.S.D., A.A., H.M.K., L.A.B., R.K.), Yale School of Medicine, New Haven, CT.ORCID 0000-0001-9467-6199

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Translating Personalized Inference from Randomized Clinical Trials to Real-World Cardiovascular CareR01HL167858 · NHLBI · YALE UNIVERSITY · PI Rohan Khera · 2024 to 2026
$2.3M
Evaluating and Improving Utilization of Evidence-Based Medical Therapy in Patients with Heart Failure using Automated Tools in the Electronic Health RecordK23HL153775 · NHLBI · YALE UNIVERSITY · PI KHERA, ROHAN · 2021 to 2025
$918k
A multi-modal approach for efficient, point-of-care screening of hypertrophic cardiomyopathyF32HL170592 · NHLBI · YALE UNIVERSITY · PI OIKONOMOU, EVANGELOS · 2023 to 2024
$166k
NCATS NIH HHS UL1 TR001863NHLBI NIH HHS F32 HL170592NHLBI NIH HHS K23 HL153775NHLBI NIH HHS R01 HL167858
6 · The paper itself

Abstract

backgroundRisk stratification strategies for cancer therapeutics-related cardiac dysfunction (CTRCD) rely on serial monitoring by specialized imaging, limiting their scalability. We aimed to examine an application of artificial intelligence (AI) to ECG images as a surrogate for imaging risk biomarkers and its association with early CTRCD.

methodsAcross a US-based health system (2013-2023), we identified 1550 patients (aged, 60 [interquartile range, 51-69] years, 1223 [78.9%] women) without cardiomyopathy who received anthracyclines or trastuzumab for breast cancer or non-Hodgkin lymphoma and had ECG performed ≤12 months before treatment. We deployed a validated AI model of left ventricular systolic dysfunction to baseline ECG images and defined low-, intermediate-, and high-risk groups based on AI-ECG left ventricular systolic dysfunction probabilities of <0.01, 0.01 to 0.1, and ≥0.1 (positive screen), respectively. We explored the association with early CTRCD (new cardiomyopathy, heart failure, or left ventricular ejection fraction <50%), or left ventricular ejection fraction <40%, up to 12 months after treatment. In a mechanistic analysis, we assessed the association between global longitudinal strain and AI-ECG left ventricular systolic dysfunction probabilities in studies performed within 15 days of each other.

resultsAmong 1550 patients without known cardiomyopathy (median follow-up, 14.1 [interquartile range, 13.4-17.1] months), 83 (5.4%), 562 (36.3%), and 905 (58.4%) were classified as high, intermediate, and low risk, respectively, by baseline AI-ECG. A high-risk versus low-risk AI-ECG screen (≥0.1 versus <0.01) was associated with a 3.4-fold and 13.5-fold higher incidence of CTRCD (adjusted hazard ratio, 3.35 [95% CI, 2.25-4.99]) and left ventricular ejection fraction <40% (adjusted hazard ratio, 13.52 [95% CI, 5.06-36.10]), respectively. Post hoc analyses supported longitudinal increases in AI-ECG probabilities within 6 to 12 months of a CTRCD event. Among 1428 temporally linked echocardiograms and ECGs, AI-ECG left ventricular systolic dysfunction probabilities were associated with worse global longitudinal strain (global longitudinal strain, -19% [interquartile range, -21% to -17%] for probabilities <0.1, to -15% [interquartile range, -15% to -9%] for ≥0.5 [

conclusionsAI applied to baseline ECG images can stratify the risk of early CTRCD associated with anthracycline or trastuzumab exposure in the setting of breast cancer and non-Hodgkin lymphoma therapy.

Indexed as

AnthracyclinesAntineoplastic AgentsArtificial IntelligenceElectrocardiographyTrastuzumabVentricular Dysfunction, LeftVentricular Function, LeftAgedCardiotoxicityFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesRisk AssessmentAnthracyclinesAntineoplastic AgentsTrastuzumabartificial intelligencecardiomyopathieselectrocardiographyheart diseasesrisk assessment

Identifiers

PMID39221857
PMCPMC11745701

What OpenQuestion holds

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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.