Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design

• Published in: Cardio-oncology (London, England) Vol. 9; no. 1; pp. 7 - 10
• Main Authors: Brown, Sherry-Ann, Chung, Brian Y., Doshi, Krishna, Hamid, Abdulaziz, Pederson, Erin, Maddula, Ragasnehith, Hanna, Allen, Choudhuri, Indrajit, Sparapani, Rodney, Bagheri Mohamadi Pour, Mehri, Zhang, Jun, Kothari, Anai N., Collier, Patrick, Caraballo, Pedro, Noseworthy, Peter, Arruda-Olson, Adelaide
• Format: Journal Article
• Language: English
• Published: London BioMed Central 23.01.2023; Springer Nature B.V; BMC
• Subjects: Algorithms; Artificial intelligence; Breast cancer; Cancer survivors; Cardio-oncology; Cardiology; Cardiotoxicity; Cardiovascular disease; Clinical decision aid; Clinical medicine; Collaboration; Consent; Decision making; Electronic health records; Ethnicity; Feasibility studies; Focus groups; Laboratories; Machine learning; Medicine; Medicine & Public Health; Oncology; Patients; Risk assessment; Shared decision making; Surveillance; Cancer survivors; Machine learning; Cardio-oncology; Cardiotoxicity; Clinical decision aid; Clinical decision support; Artificial intelligence
• ISSN: 2057-3804; 2057-3804
• DOI: 10.1186/s40959-022-00151-0

Background The many improvements in cancer therapies have led to an increased number of survivors, which comes with a greater risk of consequent/subsequent cardiovascular disease. Identifying effective management strategies that can mitigate this risk of cardiovascular complications is vital. Therefore, developing computer-driven and personalized clinical decision aid interventions that can provide early detection of patients at risk, stratify that risk, and recommend specific cardio-oncology management guidelines and expert consensus recommendations is critically important. Objectives To assess the feasibility, acceptability, and utility of the use of an artificial intelligence (AI)-powered clinical decision aid tool in shared decision making between the cancer survivor patient and the cardiologist regarding prevention of cardiovascular disease. Design This is a single-center, double-arm, open-label, randomized interventional feasibility study. Our cardio-oncology cohort of > 4000 individuals from our Clinical Research Data Warehouse will be queried to identify at least 200 adult cancer survivors who meet the eligibility criteria. Study participants will be randomized into either the Clinical Decision Aid Group (where patients will use the clinical decision aid in addition to current practice) or the Control Group (current practice). The primary endpoint of this study is to assess for each patient encounter whether cardiovascular medications and imaging pursued were consistent with current medical society recommendations. Additionally, the perceptions of using the clinical decision tool will be evaluated based on patient and physician feedback through surveys and focus groups. Summary This trial will determine whether a clinical decision aid tool improves cancer survivors’ medication use and imaging surveillance recommendations aligned with current medical guidelines. Trial registration ClinicalTrials.Gov Identifier: NCT05377320