Leveraging electronic health records to identify risky alcohol use prior to surgery

利用电子健康记录在手术前识别危险的饮酒情况

• 批准号: 10604757
• 负责人: Anne Christie Fernandez
• 金额: $ 38.98万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-10 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10604757
• 关键词: Accounting; Address; Adverse event; Agreement; Alcohol abuse; Alcohol consumption; Alcohol withdrawal syndrome; Alcohols; Algorithms; Biological Markers; Classification; Clinic; Clinical; Clinical Data; Clinical Research; Code; Collaborations; Communities; Community Health; Computer Assisted; Consumption; Data; Data Collection; Data Element; Data Set; Early identification; Electronic Health Record; Evaluation; Event; Foundations; Funding; Future; Guidelines; Health; Health Expenditures; Health Services Accessibility; Hospitals; Individual; Institutes; International Classification of Disease Codes; Intervention; Intervention Studies; Label; Lead; Length of Stay; Link; Machine Learning; Measures; Methodology; Methods; Natural Language Processing; Operative Surgical Procedures; Opioid; Outcome; Outcome Study; Patients; Pharmaceutical Preparations; Phase; Phenotype; Postoperative Period; Precision Health; Research; Research Support; Risk; Risk Factors; Severities; Structure; Surgical complication; Testing; Time; Training; Work; alcohol abstinence; alcohol intervention; alcohol risk; alcohol screening; alcohol use disorder; base; care episode; clinical application; cohort; computable phenotypes; computerized tools; cost; direct application; early alcohol use; experience; future implementation; health care service; high risk; hospital readmission; implementation research; improved; innovation; knowledgebase; longitudinal analysis; machine learning method; mortality; novel; phosphatidylethanol; predictive test; prevent; prospective; structured data; surgery outcome; surgical risk; tool; unstructured data

Project Summary/Abstract Patients who consume more than two drinks a day prior to elective surgery are at increased risk of experiencing a myriad of surgical complications, readmissions, and prolonged hospital stays. Fortunately, short-term pre-operative abstinence from alcohol mitigates many surgical risks, and carefully timed interventions can prevent complications and alcohol withdrawal syndrome. However, implementation of pre- operative alcohol interventions requires accurate identification of patients with risky alcohol use at least four weeks prior to surgery. Pre-operative clinics frequently fail to screen for alcohol use or do so too close to the surgery date to allow time for intervention. Electronic health records (EHRs) offer an unprecedented amount of accessible clinical data that can be leveraged to identify risky alcohol use early in the surgical episode of care. Innovative methods are needed to identify data elements and create algorithms to capture risky alcohol use from structured and unstructured EHR data. Natural language processing (NLP) and other machine learning (ML)-based approaches are best suited to extract and analyze alcohol-related clinical narratives, and to synthesize heterogeneous alcohol-related data through computer-assisted methods. The proposed study will leverage EHR data to identify and characterize risky alcohol use among surgical patients to identify cohorts who could benefit from pre-operative alcohol intervention. The study aims are to: 1) develop an electronic, automated computable phenotype to classify risky alcohol use prior to surgery using NLP and ML; 2) validate the algorithm through prospective data collection; and 3) longitudinally evaluate the association between risky alcohol use phenotypes and adverse surgical outcomes including complications and hospital readmissions. Innovative applications of NLP and ML will support evaluation of unstructured EHR data (e.g. clinical notes) and will enable integration of heterogeneous alcohol use data to create the computable phenotype. The aims will be achieved through collaboration of experts in key clinical domains and advanced methodologies. This study will create and validate the first alcohol-specific phenotype-based algorithm for surgical patients, which will support future clinical applications and research into alcohol-related surgical interventions and health outcomes. Study outcomes are expected to have immediate value for identifying cohorts for future implementation research and lead to a new clinical tool for surgical clinics.

项目总结/摘要 在择期手术前一天饮酒超过两杯的患者， 经历了无数的手术并发症、再次入院和长期住院。幸运的是， 术前短期戒酒可以降低许多手术风险， 干预措施可以预防并发症和酒精戒断综合症。然而，实施预- 有效的酒精干预需要准确识别危险酒精使用的患者， 手术前几周。术前诊所经常未能筛查酒精使用，或者这样做太接近 手术日期，以便有时间进行干预。电子健康记录（EHR）提供了前所未有的 可利用的临床数据，可用于在外科护理早期识别危险的酒精使用。 需要创新的方法来识别数据元素并创建算法来捕获危险的酒精使用 结构化和非结构化的EHR数据。自然语言处理（NLP）和其他机器学习 基于ML的方法最适合提取和分析酒精相关的临床叙述， 通过计算机辅助方法合成与酒精相关的异质数据。拟定的研究将 利用EHR数据识别和描述手术患者中的危险酒精使用，以确定队列 能从术前酒精干预中获益本研究的目的是：1）开发一种电子， 使用NLP和ML对手术前的危险酒精使用进行自动化可计算表型分类; 2）验证 该算法通过前瞻性的数据收集;和3）纵向评估风险之间的关联 酒精使用表型和不良手术结果，包括并发症和再次入院。 NLP和ML的创新应用将支持非结构化EHR数据（例如临床记录）的评估 并且将使得能够整合异质酒精使用数据以创建可计算表型。目标 将通过关键临床领域和先进方法的专家合作来实现。这 这项研究将为外科患者创建并验证第一个基于酒精特异性表型的算法， 将支持未来的临床应用和研究酒精相关的外科干预和健康 结果。研究结果预计对确定未来的队列具有直接价值 实施研究并为外科诊所带来新的临床工具。