Addressing variability in peripheral arterial disease outcomes using machine learning techniques

项目摘要/摘要： 外周动脉疾病(PAD)是美国发病率和死亡率的主要原因，影响到超过 800万美国人，其中每年有10万人遭受重大截肢。当前的指导方针规定了医疗 对所有PAD患者进行治疗和积极的危险因素修改，无论是否有症状， 慢性肢体威胁缺血(CLTI)或生活方式受限患者的血运重建尝试 跛行。尽管制定了严格的护理标准，但PAD结果的可变性仍然存在。前期研究 已经证明了一些人口统计因素，如性别、种族和社会经济地位 即使在控制了合并症的情况下，也会导致较差的垫护理和预后。目前还不清楚是什么 特定的患者、提供者和医疗保健系统因素导致了这些差异。 了解哪些患者将遭受更糟糕的结局和疾病进展的努力受到了阻碍 通过当代成果研究技术。大多数PAD结果研究依赖于 行政索赔数据库、程序登记或单中心回溯性审查。虽然每一位 这些方法有一些优势，没有一种方法提供患者和疾病特定数据的组合， 关于提供者和卫生系统级别的护理提供的信息，以及各种可能的结果 地点。此外，在必要的规模上使用这些方法中的任何一种来得出强有力的结论是 令人望而却步的时间和资源密集型。这项研究的总体目标是使用一种新的天然 语言处理模型，以建立组合的EHR/CMS数据库，并使用该数据库来预测 PAD患者预后不良的风险最高，功率和精确度都有所提高。 这份提案包含了与Duke Forge合作的计划，Duke Forge带来了自然语言方面的专业知识 处理和机器学习，以便在我们的电子病历中有效地识别PAD患者 关于它们的抽象信息。一旦确诊，这些患者就可以与他们的CMS结果联系起来，从而 用于评估患者、医生和医疗保健特定因素如何影响PAD结果。我们的中央 假设由机器学习提供动力的自然语言处理将允许有效地识别 PAD患者，从而促进对PAD差异的更高功率和更高质量的调查 结果。 这项研究将为未来针对结果不平等来源的干预铺平道路，可能 包括获得护理的机会、医生对国家指南的遵守以及患者的偏好或健康素养。

Project Summary/Abstract: Peripheral arterial disease (PAD) is a major cause of morbidity and mortality in the United States, affecting over eight million Americans, of whom 100,000 a year suffer major amputation. Current guidelines dictate medical treatment and aggressive risk factor modification for all PAD patients, whether symptomatic or not, with revascularization attempts for patients with chronic limb threatening ischemia (CLTI) or lifestyle-limiting claudication. Despite strongly-worded standards of care, variability in PAD outcomes persists. Prior research has demonstrated that some demographic factors such as gender, race, and socioeconomic status are associated with worse PAD care and outcomes even when controlling for comorbidities. It is unknown what specific patient, provider, and healthcare system factors lead to these disparities. Efforts to understand which patients will suffer worse outcomes and disease progression have been hampered by contemporary outcomes research techniques. The majority of PAD outcomes research relies on administrative claims databases, procedural registries, or single center retrospective reviews. While each of these methods has some advantages, none offer the combination of patient- and disease-specific data, information about care provision on a provider and health-system level, and outcomes across a range of possible locations. Furthermore, use of any of these methods at the scale necessary to draw powerful conclusions is prohibitively time- and resource-intensive. The overall objective of this research is to use a novel natural language processing model to build a combined EHR/CMS database and to use that database to predict which PAD patients are at highest risk of poor outcomes with improved power and precision. This proposal contains plans for collaboration with Duke Forge, who bring expertise in natural language processing and machine learning in order to efficiently identify PAD patients within our EHR and efficiently abstract information about them. Once identified, these patients can be linked to their CMS outcomes, allowing for assessment of how patient-, physician-, and healthcare-specific factors affect PAD outcomes. Our central hypothesis is that natural language processing powered by machine learning will permit efficient identification of patients with PAD, thereby facilitating higher-powered and higher-quality investigation into disparities in PAD outcomes. This research will pave the way for future interventions targeting sources of outcome inequality, possibly including access to care, physician adherence to national guidelines, and patient preferences or health literacy.