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
Cardio-Oncology Program, Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA.
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Cardiooncology. 2023 Jan 23;9(1):7. doi: 10.1186/s40959-022-00151-0.
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.
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.
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. This trial will determine whether a clinical decision aid tool improves cancer survivors' medication use and imaging surveillance recommendations aligned with current medical guidelines.
ClinicalTrials.Gov Identifier: NCT05377320.
癌症治疗的诸多进展使幸存者数量增加，随之而来的是后续发生心血管疾病的风险更高。确定能够降低这种心血管并发症风险的有效管理策略至关重要。因此，开发计算机驱动的个性化临床决策辅助干预措施，以早期发现有风险的患者、对风险进行分层，并推荐特定的心脏肿瘤学管理指南和专家共识建议，极为重要。
评估使用人工智能驱动的临床决策辅助工具在癌症幸存者患者与心脏病专家就预防心血管疾病进行共同决策中的可行性、可接受性和实用性。
这是一项单中心、双臂、开放标签的随机介入可行性研究。我们将查询临床研究数据仓库中超过4000人的心脏肿瘤学队列，以识别至少200名符合入选标准的成年癌症幸存者。研究参与者将被随机分为临床决策辅助组（患者除现行治疗外还将使用临床决策辅助工具）或对照组（现行治疗）。本研究的主要终点是评估每次患者就诊时所采用的心血管药物和影像学检查是否符合当前医学协会的建议。此外，将通过调查和焦点小组，根据患者和医生的反馈评估对使用临床决策工具的看法。该试验将确定临床决策辅助工具是否能改善癌症幸存者的药物使用情况以及与当前医学指南一致的影像学监测建议。
ClinicalTrials.Gov标识符：NCT05377320。
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