Multi-Modality Risk Prediction of Cardiovascular Diseases for Breast Cancer Cohort in the All of Us Research Program
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Moderator
Jifan Gao
University of Wisconsin-Madison
Presenter
Statement of Purpose
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality among breast cancer (BC) survivors, often exacerbated by the cardiotoxic effects of cancer therapies. Traditional risk prediction models frequently fall short in capturing the multifaceted nature of CVD risk, particularly within diverse populations. Leveraging the extensive and heterogeneous dataset of the All of Us Research Program, our study aimed to develop a multi-modality predictive model that integrates electronic health records (EHRs), patient surveys, and social determinants of health (SDoH) to enhance CVD risk stratification in BC survivors.
With the data from All of Us research workbench, by employing Adaptive Lasso and Random Forest regression models, we demonstrated that incorporating SDoH alongside clinical data significantly improves the prediction of six CVD outcomes. Notably, factors such as age and prior coronary events emerged as dominant predictors, while SDoH clustering provided nuanced insights into social influences on health outcomes. Our study framework demonstrates the liability of the All of Us’s diverse dataset in developing a multi-modality predictive model for CVD in BC survivors risk stratification in oncological survivorship. The data integration pipeline and subsequent predictive models establish a methodological foundation for future research into personalized healthcare.
Learning Objectives
- Review the Integration of Multi-Modal Data: Gain insights into how combining EHRs, patient-reported outcomes, SDoH, and potentially Genomic can enhance the accuracy of CVD risk prediction models for breast cancer survivors.
- Evaluate Predictive Modeling Techniques: Learn about the application and comparative performance of Adaptive Lasso and Random Forest regression models in the context of multi-modal health data.
- Recall the Role of Social Determinants in Health Outcomes from subjects' questionnaire: Recognize the impact of SDoH on cardiovascular risk and the importance of incorporating these factors into predictive analytics to inform equitable healthcare strategies.
Additional Information
The target audience for this activity includes physicians, nurses, other healthcare providers, and medical informaticians.
No commercial support was received for this activity.
Completion of this “Other Activity (Regularly Scheduled Series – RSS)” is demonstrated by participating in the live webinar or viewing the on-demand recording, engaging with presenters during the live session by submitting questions, and completing the evaluation survey at the conclusion of the course.
Learners may claim credit and download a certificate upon submission of the evaluation. Participation in additional resources and the course forum is encouraged but optional.
The American Medical Informatics Association is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The American Medical Informatics Association designates this Other activity (Regularly Scheduled Series (RSS)) for a maximum of 12 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
The American Medical Informatics Association is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center's Commission on Accreditation.
- Nurse Planner (Content): Robin Austin, PhD, DNP, DC, RN, NI-BC, FAMIA, FAAN
- Approved Contact Hours: 12 CME/CNE
It is the policy of the American Medical Informatics Association (AMIA) to ensure that Continuing Medical Education (CME) activities are independent and free of commercial bias. To ensure educational content is objective, balanced, and guarantee content presented is in the best interest of its learners and the public, the AMIA requires that everyone in a position to control educational content disclose all financial relationships with ineligible companies within the prior 24 months. An ineligible company is one whose primary business is producing, marketing, selling, re-selling or distributing healthcare products used by or on patients. Examples can be found at accme.org.
In accordance with the ACCME Standards for Integrity and Independence in Accredited Continuing Education, AMIA has implemented mechanisms prior to the planning and implementation of this CME activity to identify and mitigate all relevant financial relationships for all individuals in a position to control the content of this CME activity.
In accordance with the ACCME Standards for Integrity and Independence in Accredited Continuing Education, AMIA has implemented mechanisms prior to planning and implementation of this CME activity to identify and mitigate all relevant financial relationships for all individuals in a position to control the content of this CME activity.
Faculty and planners who refuse to disclose any financial relationships with ineligible companies will be disqualified from participating in the educational activity.
For an individual with no relevant financial relationship(s), course participants must be informed that no conflicts of interest or financial relationship(s) exist.
Disclosures
Disclosures of relevant financial relationships of all planners and presenters of the Journal Club.
Planning Committee
The planning committee and reviewers reported that they have no relevant financial relationship(s) with ineligible companies to disclose.
- Joanna Abraham, PhD, FACMI, FAMIA
- Jifan Gao, MS
- Frances Hsu, BS, MS
- Sonish Sivarajkumar
- Song Wang, MS
- Faisal Yaseen
Presenter(s)
The following presenters have no relevant financial relationship(s) with ineligible companies to disclose.
AMIA Staff
The following staff have no relevant financial relationship(s) with ineligible companies to disclose.
- Jennifer Wahl
- Melissa Kauffman
*All of the relevant financial relationships listed for these individuals have been mitigated.
