Optimal Decision Making in Aortic Valve Replacement

描述(由申请人提供)：主动脉狭窄是一种在老年患者中高度流行的疾病，会导致预期寿命缩短、生活质量(QOL)下降和医疗费用增加。在严重的有症状的主动脉狭窄的情况下，瓣膜置换是治疗的主要手段，这传统上意味着心脏直视手术。近年来，经导管主动脉瓣置换术(TAVR)已成为一种侵入性较小的瓣膜置换术，尤其适用于合并多种疾病的老年患者。在主动脉瓣置入(PARTER)试验中，TAVR患者比单纯接受药物治疗的患者有更高的存活率和更好的生活质量。尽管TAVR有好处，但近三分之一的人在治疗后一年内死亡，大约一半的人没有从TAVR中受益(一年后要么死亡，要么生活质量没有改善)。考虑到TAVR的前期风险和成本，在手术前识别不太可能受益的患者可以使患者和从业者 以便就是否接受手术做出更明智的决定。使用合作伙伴试验和其他正在进行的前瞻性研究的数据，我们将构建经济和QOL预测模型，以支持这一新兴技术的最高效使用。为了实现这些目标，我们计划使用生存、QOL和成本的多变量统计和决策分析模型，试图澄清接受TAVR的特定患者的潜在风险和好处，从而量化治疗好处的异质性，并使这些估计能够在患者基础上计算。然后，我们计划在做出治疗决定时，使用一种新的基于网络的技术将这些信息反馈给患者和从业者，该技术可以生成对患者预测的风险和结果的个性化估计。这些对临床结果的估计(例如，QOL)然后可以被合并到患者特定的共享决策工具中。将这些数据前瞻性地提供给患者和从业者，将支持一种新的对话，基于基于证据的、预测的单个患者的结果。此外，经济模型可以支持以最具成本效益的方式分配TAVR的政策决策。总之，这些研究将使这一令人兴奋和创新的最有效和最高效的应用成为可能 医疗技术。

DESCRIPTION (provided by applicant): Aortic stenosis is a highly prevalent disease among elderly patients and causes reduced life expectancy, poor quality of life (QoL), and increased healthcare costs. In the setting of severe, symptomatic aortic stenosis, valve replacement is the mainstay of treatment, which has traditionally meant open-heart surgery. Recently, transcatheter aortic valve replacement (TAVR) has emerged as a less-invasive approach to valve replacement, and is particularly attractive in elderly patients with multiple comorbidities. n the Placement of AoRTic TraNscathetER Valve (PARTNER) Trial, which randomized patients too ill to undergo surgery to medical therapy or TAVR, TAVR patients had improved survival and better QoL than those receiving medical therapy alone. Despite the benefits of TAVR, nearly 1/3 were dead within 1 year of treatment, and approximately half did not benefit from TAVR (either dead or no QoL improvement at 1 year). Given the upfront risks and costs of TAVR, identifying patients, prior to the procedure, who are unlikely to benefit can enable patients and practitioners to make a more informed decision about whether or not to undergo the procedure. Using data from the PARTNER trial and other ongoing prospective studies, we will build economic and QoL prediction models to support the most efficient use of this emerging technology. In order to accomplish these goals, we plan to use both multivariable statistical and decision analytic models of survival, QoL and costs try to clarify the potential risks and benefits of particular patients undergoing TAVR, thus quantifying the heterogeneity of treatment benefits and enabling these estimates to be calculated on a patient-by-patient basis. We then plan to feed this information back to patients and practitioners at the time when the treatment decision is being made using a novel web-based technology that can generate individualized estimates of patients' predicted risks and outcomes. These estimates of clinical outcomes (e.g. QoL) can then be incorporated into patient-specific shared decision-making tools. Providing these data prospectively to patients and practitioners will support a novel dialogue, based on the evidence-based, projected outcomes of the individual patient. In addition, the economic models can support policy decisions that allocate TAVR in the most cost-effective manner. Altogether, these studies will allow for the most effective and efficient application of this exciting and innovative medical technology.