Improving surgical decision-making by measuring and predicting long-term loss of independence after surgery

手术后的生活质量对退伍军人，他们的家人和VA医疗保健至关重要 系统-特别是对体弱病人谁是不太可能出院回家，更有可能再次入院 30天内，并且术后死亡率和并发症率大幅增加。一 当外科医生在术前筛查虚弱并进行调整时， 相应的治疗方案。虽然生存是一个重要的结果，体弱患者，许多优先质量 生活的基础是保持独立。然而，关于手术后长期独立的数据缺乏 因为手术登记集中在30天的结果，通常不评估功能状态或其他 更多以患者为中心的质量措施。大手术后到专业护理机构的出院率范围 对于术前住在家里的虚弱患者，从30%到40%，但这并不意外，因为他们很可能 从短期康复中受益。然而，在以下方面存在重大的知识差距：（a） 病人恢复独立或继续制度化;和（B）如何有效地沟通这些 围手术期风险，以支持与患者价值观一致的手术治疗的共同决策。 术后独立性丧失（LOI）是一项非常重要但难以衡量的以患者为中心的 结果，包括对患者重要的广泛活动。在这里，我们将LOI狭义地定义为 首次手术后在家以外居住的天数。LOI可以解决 快速地（即，短期LOI）或变成长期/永久的（即，长期投资）。长期风险因素 LOI是未知的，但可能包括虚弱，紧急手术，和程序，施加高生理 应力关于潜在LOI的预后对于外科医生和患者来说是至关重要的， 与患者的目标和价值观相一致的手术治疗决定。具体而言，我们的目标是： 目的1：描述术后365天内术后LOI的常见轨迹， 医疗保健利用模式及其与虚弱的相关性中具有临床意义的异质性， 急诊手术和手术压力在一个大的退伍军人样本中，我们将量化设置和持续时间 手术后一年的医疗保健利用率，建模LOI和相关风险因素的轨迹。 我们推测，LOI在身体虚弱的人以及在紧急情况和高压力之后更常见。 手术 目标2：调整风险分析指数（RAI）以预测长期LOI，并探索LOI的替代模型 预测. RAI是一项包含14项的手术脆弱性调查指标，在手术开始时需要30秒进行管理。 床旁检测并预测术后死亡率和并发症。我们将调整RAI，以建立，校准， 验证预测长期LOI的模型，并探索使用以下变量预测LOI的替代模型： 电子健康记录和现有风险措施。我们假设来自RAI的项目反应 可以在护理点准确预测LOI。 目标3：探索关键利益相关者关于如何更好地协调共同决策的观点 关于手术治疗和病人的价值观和目标。我们将与外科医生进行焦点小组讨论， 与患者及其护理人员进行半结构化访谈，以探讨围手术期风险的观点， 使共同决策与患者价值观相一致的策略。研究将集中在外科医生， 患者及其非正式护理人员在做出手术治疗决定时， 在这种情况下，LOI预后的重要性，以及如何最好地传达LOI的风险， 在操作上实施，以告知和改善偏好敏感的手术选择。

Quality of life following surgery is critically important to Veterans, their families, and the VA Healthcare System—especially for frail patients who are less likely to be discharged to home, more likely to be readmitted within 30 days, and have substantially increased rates of postoperative mortality and complications. A substantial survival benefit occurs for frail patients when surgeons preoperatively screen for frailty and adjust treatment plans accordingly. Although survival is an important outcome for frail patients, many prioritize quality of life based on preservation of independence. Yet data about long-term independence after surgery is lacking because surgical registries focus on 30-day outcomes and typically do not assess functional status or other more patient-centered quality measures. Discharge rates to skilled nursing facilities after major surgery range from 30% to 40% for frail patients living at home preoperatively, but this is not unexpected as they are likely to benefit from short-term rehabilitation. However, critical knowledge gaps exist regarding (a) whether these patients regain their independence or remain institutionalized; and (b) how to effectively communicate those perioperative risks to support shared decisions about surgical treatment aligned with patients' values. Postoperative Loss of Independence (LOI) is a critically important, but difficult to measure patient-centered outcome, encompassing a broad range of activities important to patients. Here we narrowly define LOI as the number of days living elsewhere than home after presenting from home for the index surgery. LOI may resolve quickly (i.e., short-term LOI) or become protracted/permanent (i.e., long-term LOI). Risk factors for long-term LOI are unknown, but likely include frailty, emergency surgery, and procedures that impose high physiological stress. Prognosis regarding potential LOI is essential for surgeons and patients to make better-informed decisions about surgical treatment that align with patients' goals and values. Specifically, we aim to: Aim 1: Characterize common trajectories of postoperative LOI in the 365 days after surgery to describe clinically meaningful heterogeneity in the patterns of healthcare utilization and their associations with frailty, emergency surgery, and operative stress. In a large Veteran sample, we will quantify the setting and duration of healthcare utilization in the year following surgery, modeling trajectories of LOI and associated risk factors. We hypothesize that LOI will be more common among the frail and after both emergency and high-stress surgery. Aim 2: Adapt the Risk Analysis Index (RAI) to predict long-term LOI and explore alternative models for LOI prediction. The RAI is a 14-item survey measure of surgical frailty that requires 30 seconds to administer at the point-of-care and predicts postoperative mortality and complications. We will adapt the RAI to build, calibrate, and validate models predicting long-term LOI and explore alternative models predicting LOI using variables in the Electronic Health Record and existing risk measures. We hypothesize that item responses from the RAI can accurately predict LOI at the point of care. Aim 3: Explore key stakeholders' perspectives on how LOI risk prognosis might better align shared decisions about surgical treatment with patients' values and goals. We will conduct focus groups with surgeons and semi-structured interviews with patients and their caregivers to explore perspectives on perioperative risks and strategies for aligning shared decisions with patients' values. Research will focus on the factors surgeons, patients, and their informal caregivers consider when making surgical treatment decisions, the relative importance of LOI prognosis in this context, and how the risk of LOI can be best communicated and operationally implemented to inform and improve preference-sensitive surgical choices.