The VHA AMPREDICT Decision Support Tool: Translating Success to Point of Care

慢性肢体威胁性缺血(CLTI)患者的截肢水平决策是具有挑战性的。这个 截肢水平的选择会深刻地影响手术死亡、再次住院、 截肢、功能活动，以及最终的生活质量。影响经济增长的最重要因素之一 截肢节段的决定是为了保持活动能力，因为它与生活质量有关。然而， 更多远端截肢的潜在活动益处可能无法实现，因为 影响愈合，需要持续的伤口护理，最终截肢到更高的水平。一项额外的 在这一人群中，关键的决策因素是死亡风险，这比大多数癌症都要高 诊断。这种有限的生存使外科医生和病人都必须做出决定， 最好确保患者的剩余生命年数符合他们的价值观和优先事项。退伍军人健康管理局 数据显示，2005-2014年间，意外截肢(TM)的比例是 所有CLTI截肢的10%至30%，经胫骨(TT)和 经股(TF)截肢。TM截肢的增加可能是由于 血运重建，更好的团队合作，以及保留脚踝关节将使 机动性；然而，目前尚不清楚在决定中如何考虑不愈合的风险。平衡这些风险 处于医生和患者之间复杂的共享决策(SDM)过程的核心 确定截肢的“最佳”水平。成功实施可持续发展管理的重要资源 是临床决策支持工具(DST)。它们为临床医生提供了患者特定的风险，反映了 实时的。为了给医生配备这样的工具，我们利用我们之前的预测模型创建了 AMPREDICT DST.它是一个在线DST，包括主页、预测者页面和患者结果页面 特定的一年死亡率和截肢风险以及达到基本独立水平的可能性 三个截肢节段(TM、TT、Tf)的活动度。它已经通过了VHA医生的成功测试 全国范围内。DST在临床护理中的成功实施也需要用户群体的支持 因为需要克服潜在的执行障碍。我们在医生用户中对DST的评估是 提供了将在当前提案中涉及的重要见解。许多供应商都讨论了 将DST集成到EHR中的重要性，并自动填充预测值以最大限度地减少实施 负担。此外，血管外科医生建议考虑额外的血管诊断和治疗。 在预测模型中，被认为在临床决策过程中很重要的预测因素。这是 在最近文献的前沿发展的支持下。因此，这笔赠款的目标有三个。 首先，我们将通过以下方式提高AMPREDICT预测模型在更现代人口中的质量 用可用的类似预测指标取代电子健康记录中不容易获得的预测指标，以及评价 更多的血管预测指标。我们将通过系统的开发过程来实现这一点，考虑到 人口统计，社会支持，合并症，健康因素，既往血管手术，实验室价值， 截肢前EHR中提供的药物和精神健康预测指标。第二，我们将 执行必要的规划和编程，将增强型AMPREDICT DST整合到VHA中 EHR通过CPRS和CERER促进自动填充。这将通过合作实现 与截肢者系统护理联合Cerner工作组合作，并与一家拥有丰富经验的公司合作 在VHA EHR内实施风险计算器。第三，我们将评估增强版的可用性和效果 DST在EHR中对医患决策冲突、患者决策后悔和满意度的影响。[半- 对外科医生的结构化定性访谈将评估影响执行的临床/机构因素。] 总而言之，这将导致一项重要的临床进步，这是一生中最具变化的临床决策之一。

Amputation level decision making in patients with chronic limb threatening ischemia (CLTI) is challenging. The choice of amputation level in this population can profoundly affect risk of operative mortality, re-hospitalization, reamputation, functional mobility, and ultimately quality of life. One of the most important factors influencing the amputation level decision is the preservation of mobility because of its association with quality of life. However, the potential mobility benefits of more distal amputations may not be realized because of the increased risk of compromised healing, need for ongoing wound care, and ultimately reamputation to a higher level. An additional factor critical in decision making in this population is a mortality risk which is higher than the majority of cancer diagnoses. This limited survival creates an imperative for both surgeon and patient to make decisions that will best ensure the patient’s remaining life years conform to their values and priorities. Veteran Health Administration data suggest that between 2005-2014, the proportion of incident transmetatarsal (TM) amputations tripled from 10% to 30% of all CLTI amputations with a corresponding decrease in the proportion of transtibial (TT) and transfemoral (TF) amputations. The increase in TM amputations may be driven by the improvement in revascularizations, greater teamwork, and the assumption that preserving the ankle joint will enable superior mobility; however, it is unclear how the risks of not healing are considered in the decision. Balancing these risks is at the core of a complex shared decision-making (SDM) process between physicians and patients as they determine the “best” level of amputation. An important resource that enables successful implementation of SDM are clinical decision support tools (DSTs). They provide clinicians with patient specific risks for key outcomes in real time. To equip physicians with such a tool, we leveraged our prior prediction models to create the AMPREDICT DST. It is an online DST that includes a home page, predictor pages, and a result page with patient specific one-year mortality and reamputation risks and probability of achieving a basic level of independent mobility, at each of three amputation levels (TM TT, TF). It has undergone successful testing by VHA physicians nationwide. The successful implementation of the DST in clinical care requires buy in from user groups as well as the need to overcome potential implementation obstacles. Our evaluation of the DST in physician users has provided important insights that will be addressed in the current proposal. Many of the providers discussed the importance of DST integration into the EHR with auto-population of the predictors to minimize the implementation burden. Further, vascular surgeons recommended considering additional vascular diagnostic and therapeutic predictors that are viewed as important in the clinical decisional process, in the prediction models. This is supported by cutting edge developments in the recent literature. The goals of this grant are therefore threefold. First, we will enhance the quality of the AMPREDICT prediction models in a more contemporary population by replacing predictors not readily available in the EHR with similar predictors that are available, and the evaluation of additional vascular predictors. We will accomplish this through a systemic development process considering demographics, social support, comorbidities, health factors, prior vascular procedures, laboratory values, medications, and mental health predictors available in the EHR just prior to the amputation. Second, we will perform the necessary planning and programming to integrate the enhanced AMPREDICT DST into the VHA EHR to facilitate the auto-population through CPRS and Cerner. This will be accomplished through collaborating with the Amputee System of Care joint Cerner workgroup and collaborating with a firm with significant experience implementing risk calculators within the VHA EHR. Third, we will evaluate the usability and effect of the enhanced DST within the EHR on physician and patient decisional conflict, patient decisional regret and satisfaction. [Semi- structured qualitative interviews of surgeons will evaluate clinical/institutional factors that affect implementation.] Together this will result in an important clinical advancement in one of the most life changing clinical decisions.