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

Cardio-Oncology

Research

Brian Y. Chung¹  Sherry-Ann Brown²  Patrick Collier³  Adelaide Arruda-Olson⁴  Peter Noseworthy⁴  Mehri Bagheri Mohamadi Pour⁵  Jun Zhang⁵  Indrajit Choudhuri⁶  Krishna Doshi⁷  Pedro Caraballo⁸  Anai N. Kothari⁹  Rodney Sparapani^1,10  Erin Pederson^1,11  Abdulaziz Hamid^1,11  Ragasnehith Maddula^1,11  Allen Hanna^1,12 

[1] Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA;Cardio-Oncology Program, Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA;Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA;Department of Cardiovascular Medicine, Cleveland Clinic, Cleveland, OH, USA;Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA;Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA;Department of Electrophysiology, Froedtert South, Kenosha, WI, USA;Department of Internal Medicine, Advocate Lutheran General Hospital, Park Ridge, IL, USA;Department of Medicine, Mayo Clinic, Rochester, MN, USA;Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA;Institute for Health and Equity, Medical College of Wisconsin, Milwaukee, WI, USA;Medical College of Wisconsin, Milwaukee, WI, USA;University of Wisconsin-Milwaukee, Milwaukee, WI, USA;

关键词: Cardio-oncology;    Cardiotoxicity;    Cancer survivors;    Machine learning;    Artificial intelligence;    Clinical decision aid;    Clinical decision support;   

DOI  :  10.1186/s40959-022-00151-0

 received in 2022-09-12, accepted in 2022-12-26,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundThe 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.ObjectivesTo 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.DesignThis 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.SummaryThis trial will determine whether a clinical decision aid tool improves cancer survivors’ medication use and imaging surveillance recommendations aligned with current medical guidelines.Trial registrationClinicalTrials.Gov Identifier: NCT05377320

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 1

42004_2022_800_Article_IEq32.gif

40798_2022_490_Article_IEq57.gif