01/06/2021
Study Shows Machine Learning’s Potential to Predict Cancer Therapy-Related Cardiac Risk
Improvements in cancer care that help patients live longer have been linked to increased risk for cardiac dysfunction and cardiovascular disease, highlighting the clinical need for tools that can help assess risk of cancer therapy-related cardiac dysfunction (CTRCD).
Machine learning-based approaches to risk assessment can be highly effective in predicting various types of cardiac dysfunction among cancer survivors who have received cardiotoxic cancer therapies, according to a new retrospective longitudinal study by researchers from Cleveland Clinic’s Lerner Research Institute; Heart, Vascular & Thoracic Institute; and Taussig Cancer Institute.
Published in the Journal of the American Heart Association, the study represents the first reported large-scale use of a machine learning-based approach for evaluating complications from cancer therapies that can contribute to cardiovascular disease.
Developing machine learning models
The research team, led by Feixiong Cheng, PhD, assistant staff in the Genomic Medicine Institute and Patrick Collier, MD, PhD, co-director of Cleveland Clinic’s Cardio-Oncology Center, developed and evaluated risk assessment machine learning models for six forms of CTRCD: heart failure, atrial fibrillation, coronary artery disease, myocardial infarction, stroke and de novo CTRCD (CTRCD developed after cancer therapy).
They built models for each of the six outcomes using clinical data from 4,309 cancer patients from 1997 to 2018 who had laboratory test and echocardiographic results in Cleveland Clinic’s electronic health record database. The models were then evaluated for predictive performance and generalizability and inspected to identify clinically relevant variables that were associated with CTRCDs.
