Great Lakes Node of the Drug Abuse Clinical Trials Network

药物滥用临床试验网络五大湖节点

• 批准号: 10335544
• 负责人: Niranjan Subhash Karnik
• 金额: $ 11.67万
• 依托单位: RUSH UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-15 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10335544
• 关键词: Academic Medical Centers; Adolescent; African American; Aging; Area; Artificial Intelligence; Asian; Buprenorphine; Caring; Cessation of life; Chicago; Cities; Clinical Trials; Clinical Trials Network; Collaborations; Communities; County; Data Analytics; Distance Education; Drug abuse; Electronic Health Record; Emergency department visit; Epidemic; Federal Government; Gender Identity; Geography; Health; Health system; Heroin; Home; Homelessness; Hospitals; Illinois; Indiana; Individual; Intervention; Latino; Learning; Life Cycle Stages; Los Angeles; Mentorship; Methods; Midwestern United States; Minority; Modeling; Morbidity - disease rate; National Institute of Drug Abuse; Native Americans; Natural Language Processing; New York; Opioid; Opioid Analgesics; Pacific Island Americans; Pathway interactions; Population; Prevention strategy; Professional Education; Professional Practice; Protocols documentation; Public Health; Refugees; Research; Research Methodology; Research Personnel; Research Training; Rest; Rural; Sex Orientation; Side; System; Testing; Training; Training Support; United States National Institutes of Health; Universities; Veterans; Vulnerable Populations; Wisconsin; Work; addiction; adolescent health; base; collaborative care; cooking; digital; eHealth; ethnic diversity; experience; health disparity; interest; mHealth; medical schools; metropolitan; mobile computing; mortality; novel; opioid misuse; opioid overdose; opioid treatment program; overdose death; population health; programs; racial and ethnic; racial diversity; rural area; screening; sexual identity; socioeconomics; substance misuse; suburb; success; telehealth; urban area; waiver; young adult

PROJECT SUMMARY Individuals with substance use disorders are disproportionately experiencing homelessness, poverty, and chronic medical conditions (diabetes and hypertension), which are emerging risk factors for contracting SARS-CoV-2 (official name for the virus that causes COVID-19). Different types of substance use have been associated with development of respiratory infections and progression to severe respiratory failure, also known as Acute Respiratory Distress Syndrome (ARDS). However, complex syndromes like ARDS and behavioral conditions like substance misuse are difficult to identify from the electronic health record. Clinical notes and radiology reports provide a rich source of information that may be used to identify cases of substance misuse and ARDS. This information is routinely recorded during hospital care, and automated, data-driven solutions with natural language processing (NLP) can extract semantics and important risk factors from the unstructured data of clinical notes. The computational methods of NLP derive meaning from clinical notes, from which machine learning can predict risk factors for patients leaving AMA or progressing to respiratory failure. Our team developed tools with >80% sensitivity/specificity to identify individual types of substance misuse using NLP with machine learning (ML). Our single-center models delineated risk factors embedded in the notes (e.g., mental health conditions, socioeconomic indicators). Further, we have developed and externally validated a machine learning tool to identify cases of ARDS with high accuracy for early treatment. We aim to expand this work by pooling data across health systems and build a generalizable and comprehensive classifier that captures multiple types of substance misuse for use in risk stratification and prognostication during the COVID pandemic. We hypothesize that a single-model NLP substance misuse classifier will provide a standardized, interoperable, and accurate approach for universal analysis of hospitalized patients, and that such information can be used to identify those at risk for disrupted care and those at risk for respiratory failure. We aim to train and test our substance misuse classifiers at Rush in a retrospective dataset of over 60,000 hospitalizations that have been manually screened with the universal screen, AUDIT, and DAST. This Administrative Supplement will allow us to examine the correlations between substances of misuse and risk for COVID-19 as well as development of Acute Respiratory Distress Syndrome (ARDS) in the context of these phenomena.

项目概要 患有药物滥用障碍的人不成比例地经历无家可归、贫困、 和慢性疾病（糖尿病和高血压），这是感染的新危险因素 SARS-CoV-2（引起 COVID-19 的病毒的官方名称）。不同类型的物质使用 与呼吸道感染的发展和严重呼吸衰竭的进展有关，也已知 称为急性呼吸窘迫综合征（ARDS）。然而，ARDS 和行为等复杂综合征 药物滥用等情况很难从电子健康记录中识别出来。临床记录和 放射学报告提供了丰富的信息来源，可用于识别药物滥用案例 和急性呼吸窘迫综合征。这些信息在医院护理期间定期记录，并采用自动化、数据驱动的解决方案 自然语言处理（NLP）可以从非结构化数据中提取语义和重要的风险因素 临床记录数据。 NLP 的计算方法从临床记录中获取意义，从中 机器学习可以预测患者退出 AMA 或进展为呼吸衰竭的危险因素。我们的 团队开发了灵敏度/特异性 >80% 的工具来识别各种类型的物质滥用 NLP 与机器学习 (ML)。我们的单中心模型描绘了注释中嵌入的风险因素（例如， 心理健康状况、社会经济指标）。此外，我们还开发并进行了外部验证 机器学习工具可高精度识别 ARDS 病例以进行早期治疗。我们的目标是扩大这一范围 通过汇集整个卫生系统的数据并建立一个可概括和全面的分类器来工作 捕获多种类型的药物滥用，用于新冠疫情期间的风险分层和预测 大流行。 我们假设单一模型 NLP 物质滥用分类器将提供标准化的、 对住院患者进行普遍分析的可互操作且准确的方法，并且此类信息 可用于识别那些有护理中断风险和呼吸衰竭风险的人。我们的目标是训练 并在 Rush 的超过 60,000 例住院治疗的回顾性数据集中测试我们的药物滥用分类器 已使用通用屏幕、AUDIT 和 DAST 进行手动筛选。本行政 补充材料将使我们能够检查滥用物质与 COVID-19 风险之间的相关性： 以及在这些现象的背景下急性呼吸窘迫综合征（ARDS）的发展。