Predicting risk of mortality and revision after dysvascular amputation

描述（由申请人提供）： 项目概要/摘要：患有糖尿病和/或周围动脉血管疾病的退伍军人患者有下肢截肢的显著风险。目前还没有诊断测试可以准确或可靠地预测哪个截肢水平会愈合，以及患者的具体情况， 死亡风险、再截肢风险或活动性结局将在每个大截肢水平。因此，决策往往是根据临床经验经验作出的。这导致VHA中下肢截肢的相对频率存在极大的差异，因为外科医生以不同的方式优先考虑风险和受益。横断面研究表明，截肢水平与活动性、再次截肢和死亡率的结局之间存在相关性。如果在经股动脉水平（TF）进行截肢，则死亡风险更大，活动度降低的可能性更大，但发生一期愈合的可能性非常大。相比之下，经跖骨节段（TM）截肢的翻修率非常高，死亡率低，愈合后的患者活动度良好。经胫骨（TT）截肢后的结果介于这两个极端之间。这些一般和非特异性结果可能受到患者特定因素的强烈影响，但目前没有证据可以指导决定哪一个是最重要的。 适当的截肢水平，也不是为个别病人提供输入的基础上自己的优先事项。这种不确定性给手术团队和患者带来了挑战。文献表明，非常需要开发预测模型，以更好地为手术建议提供信息，同时考虑以患者为中心的优先事项。具体目标声明：我们将使用临床和管理VA数据库来开发和验证两个患者特异性多变量预测模型。具体目标1：开发并验证经跖骨（TM）、经胫骨（TT）和经股动脉（TF）截肢术后血管障碍事件1年死亡风险的患者特异性多变量预测模型。具体目标二：开发并验证患者特异性多变量预测模型，该模型可预测TM、TT和TF截肢后1年同侧再次截肢（相同或更高截肢水平）手术风险。这些预测模型的成功开发将补充目前由VA RR&D资助开发的移动结果预测模型。它们将使外科医生能够更好地量化与在每个主要下肢截肢水平进行截肢相关的患者特定结局概率。将这些概率传达给患者将使他们能够在平衡其个人优先级（活动性、死亡率和再次截肢）时为决策提供输入。

DESCRIPTION (provided by applicant): Project Summary / Abstract: Veteran patients with Diabetes and/or Arterial Peripheral Vascular Disease are at significant risk for lower extremity amputation. There is no diagnostic test that can accurately or reliably predict which amputation level will heal, and what the patient specific, mortality risk, re-amputation risk or mobility outcome will be at each major amputation level. Decisions are therefore often made empirically based upon clinical experience. This has led to extreme variability in the relative frequencies of lower extremity amputation across VHA, as surgeons prioritize the risks and benefits in different ways. Cross sectional research has shown that there is a relationship between amputation level and the outcomes of mobility, re-amputation, and mortality. If an amputation is performed at the transfemoral level (TF) there is a greater risk of mortality, a greater likelihood of reduced mobility, but a very good probability tht primary healing will take place. In contrast an amputation at the transmetatarsal level (TM) is associated with very high revision rates, low mortality and for those that heal, a good mobility outcome. The outcome after transtibial (TT) amputation lies between these two extremes. These general and non-specific outcomes are likely strongly influenced by patient specific factors, but there is no evidence available at this time to guide decisions about which is the most appropriate amputation level, nor for the individual patient to provide input based upon their own priorities. This uncertainty leads to challenges for both the surgical team, and the patient. The literature indicates there is an important need for predictive models to be developed to better inform surgical recommendations and at the same time allow for patient centric priorities to be considered. Specific Aims Statement: We will use both clinical and administrative VA databases to develop and validate two patient-specific multivariable prediction models. Specific Aim 1: To develop and validate a patient-specific multivariable prediction model for 1-year mortality risk after incident dysvascular transmetatarsal (TM), transtibial (TT), and transfemoral (TF) amputation. Specific Aim 2: To develop and validate a patient-specific multivariable prediction model that predicts 1-year ipsilateral re-amputation (same or higher amputation level) surgery risk after incident TM, TT, and TF amputation. The successful development of these predictive models will complement a mobility outcome prediction model whose development is currently funded by VA RR&D. Together they will enable surgeons to better quantify patient specific outcome probabilities associated with performing an amputation at each major lower extremity amputation level. Communication of these probabilities to the patient will enable them to provide input to the decision as they balance their personal priorities of mobility, mortality and re-amputation.