ArticleJournal of the American Heart Association2020
Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients.
Article in Journal of the American Heart Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
49 citing papers in PubMed, 3 syntheses or guidelines pooled it, 93 citations in OpenAlex.
- Cardiotoxicity in pediatric oncology: a systematic review and meta-analysis.Pediatric research · 2026Pooled it
- Machine learning methods for predicting adverse drug events: A systematic review.British journal of clinical pharmacology · 2026Pooled it
- Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- A systematic review of artificial intelligence in radiotherapy associated cardiovascular toxicity.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
- PrevCardioOncAI: Machine Learning Algorithms for Predicting Cardiovascular Disease in Cancer Survivors.Journal of the American Heart Association · 2025Observational
- Exploring Predictive Risk Factors for Myocardial Injury in Children Treated with Anthracyclines: A Pilot Study.Cardiovascular toxicology · 2025Article
- Leveraging artificial intelligence for risk stratification of inherited cardiomyopathies in under-resourced settings.Heart rhythm O2 · 2025Article
- Development of an Electronic Medical Record-Based Score for Heart Failure Prediction in Cancer Survivors.JACC. Advances · 2025Article
- Radiomics early assessment of post chemotherapy cardiotoxicity in cancer patients using 2D echocardiography imaging an interpretable machine learning study.Scientific reports · 2025Article
- CD163Basic research in cardiology · 2025Article
- Artificial Intelligence to Enhance Precision Medicine in Cardio-Oncology: A Scientific Statement From the American Heart Association.Circulation. Genomic and precision medicine · 2025Review
- Applications, challenges and future directions of artificial intelligence in cardio-oncology.European journal of clinical investigation · 2025Review
- AI and Smart Devices in Cardio-Oncology: Advancements in Cardiotoxicity Prediction and Cardiovascular Monitoring.Diagnostics (Basel, Switzerland) · 2025Review
- Cancer-Therapy-Related Cardiac Dysfunction: Latest Advances in Prevention and Treatment.Life (Basel, Switzerland) · 2025Review
- Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review.Current cardiology reports · 2025Review
- Discovery of plasma proteins and metabolites associated with left ventricular cardiac dysfunction in pan-cancer patients.Cardio-oncology (London, England) · 2025Article
- Machine learning applications in risk management: Trends and research agenda.F1000Research · 2025Review
- Machine learning models using multimodal data accurately predict chemotherapy-induced cardiotoxicity in breast cancer.Frontiers in cardiovascular medicine · 2025Article
- Machine learning-based preoperative prediction of perioperative venous thromboembolism in Chinese lung cancer patients: a retrospective cohort study.Frontiers in oncology · 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
20 authors at 4 institutions in 2 countries.
Funding
Abstract
Background The growing awareness of cardiovascular toxicity from cancer therapies has led to the emerging field of cardio-oncology, which centers on preventing, detecting, and treating patients with cardiac dysfunction before, during, or after cancer treatment. Early detection and prevention of cancer therapy-related cardiac dysfunction (CTRCD) play important roles in precision cardio-oncology. Methods and Results This retrospective study included 4309 cancer patients between 1997 and 2018 whose laboratory tests and cardiovascular echocardiographic variables were collected from the Cleveland Clinic institutional electronic medical record database (Epic Systems). Among these patients, 1560 (36%) were diagnosed with at least 1 type of CTRCD, and 838 (19%) developed CTRCD after cancer therapy (de novo). We posited that machine learning algorithms can be implemented to predict CTRCDs in cancer patients according to clinically relevant variables. Classification models were trained and evaluated for 6 types of cardiovascular outcomes, including coronary artery disease (area under the receiver operating characteristic curve [AUROC], 0.821; 95% CI, 0.815-0.826), atrial fibrillation (AUROC, 0.787; 95% CI, 0.782-0.792), heart failure (AUROC, 0.882; 95% CI, 0.878-0.887), stroke (AUROC, 0.660; 95% CI, 0.650-0.670), myocardial infarction (AUROC, 0.807; 95% CI, 0.799-0.816), and de novo CTRCD (AUROC, 0.802; 95% CI, 0.797-0.807). Model generalizability was further confirmed using time-split data. Model inspection revealed several clinically relevant variables significantly associated with CTRCDs, including age, hypertension, glucose levels, left ventricular ejection fraction, creatinine, and aspartate aminotransferase levels. Conclusions This study suggests that machine learning approaches offer powerful tools for cardiac risk stratification in oncology patients by utilizing large-scale, longitudinal patient data from healthcare systems.
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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.