Artificial intelligence modelling to assess the risk of cardiovascular disease in oncology patients.
Document Type
Article
Publication Date
5-8-2023
Publication Title
European Heart Journal - Digital Health
Keywords
Medical artificial intelligence, Cardio-oncology, Machine learning, Cardiotoxicity, Cardiovascular risk assessment, Cardiovascular disease
ISSN
2634-3916
Volume
4
Issue/No.
4
First Page
302
Last Page
315
Abstract
AIMS: There are no comprehensive machine learning (ML) tools used by oncologists to assist with risk identification and referrals to cardio-oncology. This study applies ML algorithms to identify oncology patients at risk for cardiovascular disease for referrals to cardio-oncology and to generate risk scores to support quality of care.
METHODS AND RESULTS: De-identified patient data were obtained from Vanderbilt University Medical Center. Patients with breast, kidney, and B-cell lymphoma cancers were targeted. Additionally, the study included patients who received immunotherapy drugs for treatment of melanoma, lung cancer, or kidney cancer. Random forest (RF) and artificial neural network (ANN) ML models were applied to analyse each cohort: A total of 20 023 records were analysed (breast cancer, 6299; B-cell lymphoma, 9227; kidney cancer, 2047; and immunotherapy for three covered cancers, 2450). Data were divided randomly into training (80%) and test (20%) data sets. Random forest and ANN performed over 90% for accuracy and area under the curve (AUC). All ANN models performed better than RF models and produced accurate referrals.
CONCLUSION: Predictive models are ready for translation into oncology practice to identify and care for patients who are at risk of cardiovascular disease. The models are being integrated with electronic health record application as a report of patients who should be referred to cardio-oncology for monitoring and/or tailored treatments. Models operationally support cardio-oncology practice. Limited validation identified 86% of the lymphoma and 58% of the kidney cancer patients with major risk for cardiotoxicity who were not referred to cardio-oncology.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
NSUWorks Citation
Al-Droubi, Samer S; Jahangir, Eiman; Kochendorfer, Karl M; Krive, Marianna; Laufer-Perl, Michal; Gilon, Dan; Okwuosa, Tochukwu M; Gans, Christopher P; Arnold, Joshua H; Bhaskar, Shakthi T; Yasin, Hesham A; and Krive, Jacob, "Artificial intelligence modelling to assess the risk of cardiovascular disease in oncology patients." (2023). HPD Articles. 452.
https://nsuworks.nova.edu/hpd_facarticles/452
ORCID ID
0000-0001-6030-5357, 0000-0002-7262-9436, 0000-0003-4528-6367, 0000-0002-1130-3125, 0000-0002-1259-717X.
DOI
10.1093/ehjdh/ztad031
Copyright
© The Author(s) 2023
Comments
Acknowledgements
The authors would like to acknowledge VUMC support from Dr Michael Savona for recruiting medical fellows to help with AI model validation, Dr Kerry Schaffer for contributing ideas to translate this research into medical practice, and Peter Shave and Chris Grabiel for business leadership in translating this research into medical practice. Work was performed at VUMC, Nashville, Tennessee, USA.
Funding
Funding was provided by the Vanderbilt Institute for Clinical and Translation Research through a voucher grant to pay for the cost of extracting de-identified cancer patient data from the synthetic derivative database.