Addressing variability in peripheral arterial disease outcomes using machine learning techniques

使用机器学习技术解决外周动脉疾病结果的变异性

• 批准号: 10464976
• 负责人: Elizabeth Hope Weissler
• 金额: $ 4.79万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10464976
• 关键词: Address; Affect; American; Amputation; Blood Vessels; Cardiovascular system; Caring; Cessation of life; Chronic; Clinical; Clinical Trials; Code; Collaborations; Country; Data; Data Set; Databases; Demographic Factors; Disease; Disease Outcome; Disease Progression; Documentation; Elements; Failure; Female; Future; Gender; Goals; Guidelines; Health Services Accessibility; Health system; Healthcare; Healthcare Systems; Hospitalization; Inequality; International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10); Intervention; Investigation; Ischemia; Knowledge; Lead; Life Style; Light; Limb structure; Link; Location; Machine Learning; Medical; Methods; Modeling; Modification; Morbidity - disease rate; Myocardial Infarction; Natural Language Processing; Operative Surgical Procedures; Outcome; Outcomes Research; Patient Care; Patient Preferences; Patients; Peripheral arterial disease; Physicians; Prevalence; Process; Provider; Race; Registries; Research; Research Methodology; Research Technics; Resources; Risk; Risk Factors; Socioeconomic Status; Source; Stroke; System; Techniques; Text; Time; Training; United States; Work; adjudicate; base; care outcomes; claudication; cohort; comorbidity; cost; demographics; design; effective intervention; health literacy; high risk; improved; low socioeconomic status; mortality; novel; patient subsets; provider adherence; tool; treatment effect

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.

项目概要/摘要： 外周动脉疾病（PAD）是美国发病率和死亡率的主要原因， 800万美国人，其中每年有10万人遭受截肢。目前的指南要求医疗 所有PAD患者的治疗和积极的风险因素调整，无论是否有症状， 慢性肢体威胁性缺血（CLTI）或生活方式受限患者的血运重建尝试 跛行。尽管有措辞强硬的护理标准，PAD结局的变异性仍然存在。先前的研究 已经证明，一些人口因素，如性别，种族和社会经济地位， 与PAD护理和结局较差相关，即使在控制合并症时也是如此。不知道是什么 特定患者、提供者和保健系统因素导致这些差异。 了解哪些患者将遭受更糟糕的结果和疾病进展的努力受到了阻碍 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、手术登记或单中心回顾性审查。虽然每 这些方法具有一些优点，但是没有一种方法提供患者和疾病特异性数据的组合， 关于提供者和卫生系统一级提供保健的信息，以及一系列可能的结果， 地点此外，在得出有力结论所需的规模上使用任何这些方法都是不可能的。 时间和资源密集程度高得令人望而却步。本研究的总体目标是使用一种新的自然 语言处理模型，以建立一个组合的EHR/CMS数据库，并使用该数据库预测 PAD患者的不良结局风险最高，但功效和精度有所提高。 该提案包含与杜克福奇合作的计划，后者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EHR中有效地识别PAD患者， 抽象的信息。一旦确定，这些患者可以与他们的CMS结果联系起来， 用于评估患者、医生和医疗保健特定因素如何影响PAD结局。我们的中央 假设由机器学习驱动的自然语言处理将允许有效识别 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措施铺平道路， 包括获得护理、医生遵守国家指南以及患者偏好或健康知识。

项目成果

National trends in repair for type B aortic dissection.

• DOI: 10.1002/clc.23672
• 发表时间: 2021-08
• 期刊: Clinical cardiology
• 影响因子: 2.7
• 作者: Weissler EH;Osazuwa-Peters OL;Greiner MA;Hughes GC;Long CA;Vemulapalli S;Patel MR;Jones WS
• 通讯作者: Jones WS

Younger patients with chronic limb threatening ischemia face more frequent amputations.

• DOI: 10.1016/j.ahj.2021.08.002
• 发表时间: 2021-12
• 期刊: American heart journal
• 影响因子: 4.8
• 作者: Weissler EH;Ford CB;Patel MR;Goodney P;Clark A;Long C;Jones WS
• 通讯作者: Jones WS

The Role for Combined Antithrombotic Therapy With Platelet and Coagulation Inhibition After Lower Extremity Revascularization.

• DOI: 10.1016/j.jcin.2021.01.035
• 发表时间: 2021-04-12
• 期刊: JACC-CARDIOVASCULAR INTERVENTIONS
• 影响因子: 11.3
• 作者: Weissler, E. Hope;Aboyans, Victor;Bauersachs, Rupert;Brodmann, Marianne;Nikol, Sigrid;Debus, Sebastian;Patel, Manesh R.;Jones, W. Schuyler
• 通讯作者: Jones, W. Schuyler

Cardiovascular and Limb Events Following Endovascular Revascularization Among Patients ≥65 Years Old: An American College of Cardiology PVI Registry Analysis.

• DOI: 10.1161/jaha.121.024279
• 发表时间: 2022-06-20
• 期刊: JOURNAL OF THE AMERICAN HEART ASSOCIATION
• 影响因子: 5.4
• 作者: Weissler, E. Hope;Wang, Yongfei;Gales, Jordan M.;Feldman, Dmitriy N.;Arya, Shipra;Secemsky, Eric A.;Aronow, Herbert D.;Hawkins, Beau M.;Gutierrez, J. Antonio;Patel, Manesh R.;Curtis, Jeptha P.;Jones, W. Schuyler;Swaminathan, Rajesh, V
• 通讯作者: Swaminathan, Rajesh, V

Clinician Specialty, Access to Care, and Outcomes Among Patients with Peripheral Artery Disease.

• DOI: 10.1016/j.amjmed.2021.08.025
• 发表时间: 2022-03
• 期刊: The American journal of medicine
• 作者: Weissler EH;Ford CB;Narcisse DI;Lippmann SJ;Smerek MM;Greiner MA;Hardy NC;O'Brien B;Sullivan RC;Brock AJ;Long C;Curtis LH;Patel MR;Jones WS
• 通讯作者: Jones WS