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.

项目摘要 患有药物使用障碍的人不成比例地经历无家可归，贫穷， 和慢性医疗状况（糖尿病和高血压），这是收缩的危险因素 SARS-COV-2（导致COVID-19的病毒的正式名称）。不同类型的药物使用已 与呼吸道感染的发展以及对严重呼吸衰竭的发展有关的，也已知 作为急性呼吸窘迫综合征（ARDS）。但是，复杂的综合征和行为等复杂综合症 从电子健康记录中很难识别诸如滥用药物的条件。临床笔记和 放射学报告提供了丰富的信息来源，可用于识别滥用物质的情况 和ards。此信息通常在医院护理期间记录，并自动化数据驱动的解决方案 使用自然语言处理（NLP）可以从非结构化的语义中提取语义和重要的风险因素 临床注释的数据。 NLP的计算方法从临床注释中得出含义，从中 机器学习可以预测离开AMA或呼吸衰竭的患者的危险因素。我们的 团队开发了具有> 80％灵敏度/特异性的工具，以识别使用单个类型的物质滥用 NLP与机器学习（ML）。我们的单中心模型描述了嵌入在注释中的风险因素（例如， 心理健康状况，社会经济指标）。此外，我们已经开发并在外部验证了 机器学习工具以识别高准确性的ARDS病例以早期治疗。我们旨在扩大这一点 通过跨卫生系统汇总数据的工作，并构建可概括且全面的分类器 捕获多种类型的药物滥用，以用于风险分层和预后。 大流行。 我们假设单模NLP物质滥用分类器将提供标准化的， 可互操作且准确的住院患者的普遍分析的方法，并且此类信息 可以用来识别那些患有干扰护理风险的人和有呼吸道衰竭风险的人。我们的目标是训练 并在Rush中测试我们的物质滥用分类器，该数据集的回顾性数据集超过60,000个住院 通过通用屏幕，审核和Dast手动筛选。这个行政 补充剂将使我们能够检查滥用的物质与Covid-19的风险之间的相关性 以及在这些现象中的急性呼吸遇险综合征（ARDS）的发展。