Faculty Collaboration Database

PrevCardioOncAI: Machine Learning Algorithms for Predicting Cardiovascular Disease in Cancer Survivors. J Am Heart Assoc 2025 Dec 16;14(24):e030363

Date

12/11/2025

Pubmed ID

41378455

Pubmed Central ID

PMC12826905

DOI

10.1161/JAHA.123.030363

Scopus ID

2-s2.0-105025249869 (requires institutional sign-in at Scopus site)

Abstract

BACKGROUND: Cardiovascular disease (CVD) is a leading cause of death in cancer survivors. Predicting CVD risk in this population remains challenging. Risk prediction models that use machine learning algorithms could provide an objective method for accurate risk prediction to facilitate the prevention and management of CVD in cancer survivors. We evaluated previously tested machine learning algorithms and logistic regression (regularized by machine learning methods), in addition to testing and validating newer, more complex machine learning algorithms, for CVD prediction in cancer survivors.

METHODS: This multicenter study used a database of 3835 multiracial cancer survivors with 89 clinical, laboratory, and echocardiographic features over 20 years. Models were trained using repeated random and time-split samples and tested on a separate cohort of 329 patients. Model performance was assessed using the area under the receiver operating characteristic curve.

RESULTS: Regularized logistic regression achieved an area under the receiver operating characteristic curve of 0.845 (heart failure), 0.783 (atrial fibrillation), 0.792 (coronary artery disease), and 0.806 (composite CVD). These are comparable to 0.837 and 0.848 for heart failure, using Bayesian additive regression tree and random forest as more advanced machine learning models, respectively. De novo composite CVD (post-cancer diagnosis) was also predicted with an area under the receiver operating characteristic curve of 0.826 using regularized logistic regression, compared with 0.735 and 0.802 using decision tree and random forest, respectively.

CONCLUSIONS: Regularized logistic regression and advanced machine learning models demonstrated similar predictive performance, with institutional transferability. These tools may support risk stratification and prevention strategies in cardio-oncology using longitudinal data.

REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT05377320.

Author List

Brown SA, Fang MZ, Sparapani R, Zhou Y, Osinski K, Taylor B, Yu D, Blessing J, Shah R, Collier P, BagheriMohamadiPour M, Zhang J, Kothari A, Echefu G, Rickards J, Otto C, Sanchez Z, Olson J, Arruda-Olson A, Cheng YC, Cheng F, Cardio‐Oncology Artificial Intelligence Informatics and Precision Equity, and Patient Similarity Algorithms in the Prevention of Cardiovascular Toxicity Research Team Investigators

Authors

Mehri BagheriMohamadiPour PhD Postdoctoral Researcher 1 in the Pediatrics department at Medical College of Wisconsin
Anai N. Kothari MD Assistant Professor in the Surgery department at Medical College of Wisconsin
Jessica Olson PhD Director, Associate Professor in the Institute for Health and Humanity department at Medical College of Wisconsin
Rodney Sparapani PhD Associate Professor in the Data Science Institute department at Medical College of Wisconsin
Bradley W. Taylor Chief Research Informatics Officer in the Clinical and Translational Science Institute department at Medical College of Wisconsin
Duo Yu PhD Assistant Professor in the Data Science Institute department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Aged
Algorithms
Cancer Survivors
Cardiovascular Diseases
Female
Humans
Logistic Models
Machine Learning
Male
Middle Aged
Neoplasms
Predictive Value of Tests
Risk Assessment
Risk Factors