Optimal Decision Making in Aortic Valve Replacement

描述（由申请方提供）：主动脉瓣狭窄是老年患者中的一种高度流行疾病，可导致预期寿命缩短、生活质量（QoL）差和医疗费用增加。在严重的症状性主动脉瓣狭窄的情况下，瓣膜置换术是治疗的主要手段，传统上意味着心脏直视手术。最近，经导管主动脉瓣置换术（TAVR）已成为一种微创的瓣膜置换术方法，在患有多种合并症的老年患者中特别有吸引力。在AoRTic经导管心脏瓣膜植入（PARTNER）试验中，该试验将因病情过重而无法接受手术治疗的患者随机分配至药物治疗或TAVR，TAVR患者的生存率和生活质量均优于单纯接受药物治疗的患者。尽管有TAVR的受益，但近1/3的患者在治疗1年内死亡，约一半患者未从TAVR中获益（1年时死亡或QoL无改善）。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人员 做出更明智的决定是否接受手术。利用PARTNER试验和其他正在进行的前瞻性研究的数据，我们将建立经济和生活质量预测模型，以支持最有效地使用这一新兴技术。为了实现这些目标，我们计划使用生存、生活质量和成本的多变量统计和决策分析模型，试图阐明接受TAVR的特定患者的潜在风险和获益，从而量化治疗获益的异质性，并使这些估计值能够在逐个患者的基础上进行计算。然后，我们计划在使用一种新的基于网络的技术做出治疗决定时，将这些信息反馈给患者和医生，该技术可以对患者的预测风险和结果进行个性化估计。这些临床结果（如生活质量）的估计，然后可以纳入患者特定的共享决策工具。前瞻性地向患者和从业者提供这些数据将支持基于个体患者的循证预测结果的新对话。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.