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A multi-site trial to test benefits of adding a personalized risk calculator to an online decision aid for left ventricular assist device

A multi-site trial to test benefits of adding a personalized risk calculator to an online decision aid for left ventricular assist device
一项多站点试验,测试将个性化风险计算器添加到左心室辅助装置在线决策辅助中的好处
批准号:
10662246
负责人:
Jennifer Blumenthal-Barby
金额:
$39.25万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
未结题
起止时间:
2020-09-30 至 2025-07-31

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PROJECT SUMMARY Continuous flow left ventricular assist devices (LVADs) now rival heart transplant as the gold standard for advanced heart failure. However, uncertainty persists over which patients are most likely to benefit from treatment. New technologies for a more personalized approach to risk prediction and supported decision- making are crucially needed to improve current clinical risk calculators and decision aids which lack the ability to (a) calculate patients’ individualized and highly variable personalized risks, (b) communicate those risks to patients in the context of their values and goals and discuss these factors with their health care team, and (c) capitalize upon digitalized health systems and platforms to provide most expeditious and efficient updates to rapidly-changing risk. This represents a significant gap in informed and high-quality decision making that also potentially negatively affects health and clinical outcomes. The objective of this proposal is to improve patients’ informed and shared decision making about LVAD by providing a holistic and personalized clinical decision support system for LVAD candidates that interactively guides their understanding of how available treatment options align with their expressed values, given their personalized risk estimates, and provides them with a tool to communicate these values to their clinical team. We will do this by updating and integrating a validated online risk prediction and communication tool, the Cardiac Outcomes Risk Assessment (CORA) developed by our colleagues at Cornell University, with our efficacy-tested decision aid (Deciding Together) for LVAD. To accomplish this, in Aim 1 we will conduct in-depth qualitative interviews to identify patients’ and physicians’ practical, ethical, and contextual considerations while using CORA, while our colleagues at Cornell simultaneously update and (re-)validate the updated model against existing LVAD risk prediction models, as well as integrate CORA with an adapted and digitalized version of our decision aid. In Aim 2, we will then translate patients' and physicians’ practical, ethical and contextual considerations into concrete improvements to CORA and conduct acceptability/feasibility testing with patients and physicians using the updated, validated system. These steps will culminate in Aim 3, a multi-site randomized controlled trial to evaluate the impact of a personalized approach to clinical decision making on informed and values- concordant choice and shared decision-making outcomes. This contribution will be significant because it will address the urgent need to better identify and respond to the specific and dynamic nature of patient needs in seeking advanced heart failure treatment. Our approach is innovative in that it harnesses state of the art technology prediction models and LVAD decision support, synthesizing diverse expertise from a team of bioengineers, computer scientists, leading heart failure cardiologists, decision scientists, and medical ethicists. This five-year project is feasible in that it builds on 6 years of research on the development, implementation and dissemination of LVAD decision support and a decade of research into accurate risk prediction models for LVAD.
期刊论文(7)
专著(0)
科研奖励(0)
会议论文
Trust criteria for artificial intelligence in health: normative and epistemic considerations.
健康人工智能的信任标准:规范和认知考虑。
DOI: 10.1136/jme-2023-109338
发表时间: 2023
期刊: Journal of medical ethics
影响因子: 4.1
作者: [Kostick-Quenet,Kristin, Lang,BenjaminH, Smith,Jared, Hurley,Meghan, Blumenthal-Barby,Jennifer]
通讯作者: Blumenthal-Barby,Jennifer
Avoiding "toxic knowledge": the importance of framing personalized risk information in clinical decision-making.
避免“有毒知识”:在临床决策中构建个性化风险信息的重要性。
DOI: 10.2217/pme-2020-0174
发表时间: 2021
期刊: Personalized medicine
影响因子: 2.3
作者: [Kostick,KristinM, Blumenthal-Barby,JS]
通讯作者: Blumenthal-Barby,JS
Responsibility Gaps and Black Box Healthcare AI: Shared Responsibilization as a Solution.
责任差距和黑匣子医疗保健人工智能:共享责任作为解决方案。
DOI: 10.1007/s44206-023-00073-z
发表时间: 2023
期刊: Digital society : ethics, socio-legal and governance of digital technology
影响因子: --
作者: [Lang,BenjaminH, Nyholm,Sven, Blumenthal-Barby,Jennifer]
通讯作者: Blumenthal-Barby,Jennifer
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