Artificial intelligence-enhanced risk stratification of cancer therapeutics-related cardiac dysfunction using electrocardiographic images.
Evangelos K OikonomouVeer SanghaLovedeep Singh DhingraArya AminorroayaAndreas CoppiHarlan M KrumholzLauren A BaldassarreRohan KheraPublished in: medRxiv : the preprint server for health sciences (2024)
There is an unmet need for scalable and affordable biomarkers to stratify the risk of cancer therapeutics-related cardiac dysfunction (CTRCD). In this hospital system-based, decade-long cohort of patients without cardiomyopathy receiving anthracyclines or trastuzumab, a validated artificial intelligence algorithm applied to baseline electrocardiographic (AI-ECG) images identified individuals with a 2-fold and 4.8-fold risk of developing any cardiomyopathy or left ventricular ejection fraction <40%, respectively. This supports a role for AI-ECG interpretation of images as a scalable approach for the baseline risk stratification of patients initiating cardiotoxic chemotherapy.
Keyphrases
- artificial intelligence
- ejection fraction
- deep learning
- left ventricular
- machine learning
- aortic stenosis
- big data
- end stage renal disease
- heart failure
- chronic kidney disease
- small molecule
- papillary thyroid
- prognostic factors
- optical coherence tomography
- peritoneal dialysis
- oxidative stress
- squamous cell carcinoma
- left atrial
- emergency department
- coronary artery disease
- acute coronary syndrome
- squamous cell
- hypertrophic cardiomyopathy
- acute myocardial infarction
- epidermal growth factor receptor
- childhood cancer
- drug induced
- tyrosine kinase
- electronic health record
- atrial fibrillation
- adverse drug