ArticleNature communications2024
Artificial intelligence-enabled prediction of chemotherapy-induced cardiotoxicity from baseline electrocardiograms.
Article in Nature communications, 2024. 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 44 papers, 2 of them syntheses that pooled it.
What it found
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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.
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.
Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images
Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Cardiovascular risk-prediction models in cancer survivors: a meta-analysis.European heart journal · 2026Pooled it
- Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis.Frontiers in pharmacology · 2024Pooled it
- Machine learning-enhanced baseline risk prediction models for cancer therapy related cardiac dysfunction: a systematic review and critical appraisal of methodological quality.European heart journal. Digital health · 2026Article
- Risk-guided cardioprotection in cardio-oncology.Nature cardiovascular research · 2026Review
- Cardiovascular Care in Multiple Myeloma: A Comprehensive Review and Integrated Systems-Based Approach from a UK Cardio-Oncology Centre of Excellence.Current cardiology reports · 2026Review
- Cardiotoxicity of Targeted Therapies in Hematologic Malignancies: From Molecular Mechanisms to Clinical Management.Current treatment options in oncology · 2026Review
- In silico predictions of action potential propagation in doxorubicin cardiotoxicity: A parametric study using preclinical 3D magnetic resonance imaging-based fibrotic left ventricle models.The Journal of physiology · 2026Article
- Empagliflozin Protects Against Doxorubicin Cardiotoxicity: Integrative Assessment of Cardiac Kinetics and Electrophysiology Using Machine Learning in a Rat Model.Medical sciences (Basel, Switzerland) · 2026Article
- Review
- From data to decision: a clinical pipeline for wearable AI in early cardiotoxicity detection.Cardio-oncology (London, England) · 2026Review
- Artificial Intelligence and the Expanding Universe of Cardio-Oncology: Beyond Detection Toward Prediction and Prevention of Therapy-Related Cardiotoxicity-A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2026Review
- Toward Personalized CTRCD Risk Prediction: CHECK HEART Offers a Preliminary Framework.JACC. Asia · 2026Article
- Precision Monitoring Strategies for Chemotherapy-Induced Cardiotoxicity: A Review of Molecular Indicators for Early Detection and Risk Stratification.Reviews in cardiovascular medicine · 2026Review
- Immune checkpoint inhibitor-associated arrhythmias: clinical challenges, artificial intelligence-enhanced monitoring, and management strategies.Frontiers in cardiovascular medicine · 2026Review
- Multimodal fusion of EHR and ECG based on deep learning for predicting new-onset coronary heart disease in cancer patients.Frontiers in cardiovascular medicine · 2026Article
- China-calibration-ready echocardiographic feature modeling for LVEF-derived dysfunction classification: a surrogate-label proof-of-concept.Frontiers in medicine · 2026Article
- Integrated Cardio-oncology Service.Cardiac failure review · 2026Review
- Extracellular Vesicle-Based Strategies for the Prevention of Chemotherapy-Induced Cardiotoxicity.Therapeutics and clinical risk management · 2026Review
- Integrated Model for Predicting Cancer Therapy-Related Cardiac Dysfunction in Non-Hodgkin Lymphoma.Biomedicines · 2025Article
- Artificial Intelligence-Enabled Electrocardiogram for the Detection and Management of Cancer Therapy-Related Cardiotoxicity.Cancer innovation · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
Anthracyclines can cause cancer therapy-related cardiac dysfunction (CTRCD) that adversely affects prognosis. Despite guideline recommendations, only half of the patients undergo surveillance echocardiograms. An AI model detecting reduced left ventricular ejection fraction from 12-lead electrocardiograms (ECG) (AI-EF model) suggests ECG features reflect left ventricular pathophysiology. We hypothesized that AI could predict CTRCD from baseline ECG, leveraging the AI-EF model's insights, and developed the AI-CTRCD model using transfer learning on the AI-EF model. In 1011 anthracycline-treated patients, 8.7% experienced CTRCD. High AI-CTRCD scores indicated elevated CTRCD risk (hazard ratio (HR), 2.66; 95% CI 1.73-4.10; log-rank p < 0.001). This remained consistent after adjusting for risk factors (adjusted HR, 2.57; 95% CI 1.62-4.10; p < 0.001). AI-CTRCD score enhanced prediction beyond known factors (time-dependent AUC for 2 years: 0.78 with AI-CTRCD score vs. 0.74 without; p = 0.005). In conclusion, the AI model robustly stratified CTRCD risk from baseline ECG.
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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.