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: 10173503
• 负责人: DAVID H GUSTAFSON
• 金额: $ 13.98万
• 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: 2019-06-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10173503
• 关键词: 2019-nCoV; Accident and Emergency department; Administrative Supplement; Adult; Adult Respiratory Distress Syndrome; Affect; Alcohol or Other Drugs use; Artificial Intelligence; Behavioral; COVID-19; COVID-19 pandemic; Caring; Chronic; Cities; Clinical; Clinical Data; Clinical Trials Network; Communities; Complex; Computing Methodologies; Consumption; Contracts; Data; Data Pooling; Data Set; Detection; Development; Diabetes Mellitus; Disasters; Drug abuse; Drug usage; Early treatment; Electronic Health Record; Ensure; Epidemic; Equipment; Felis catus; General Population; Health; Health Services; Health Status; Health system; Heart Diseases; Home environment; Homelessness; Hospitalization; Hospitals; Hurricane; Hypertension; Illicit Drugs; Individual; Label; Machine Learning; Manuals; Medical; Mental Health; Methods; Modeling; Names; Natural Language Processing; Outcome; Overdose; Patients; Performance; Pharmaceutical Preparations; Poverty; Prevention; Public Health; Radiology Specialty; Reporting; Research; Resources; Respiratory Failure; Respiratory Tract Infections; Risk; Risk Factors; Risk stratification; Semantics; Sensitivity and Specificity; Social support; Source; Standardization; Sterility; Substance Use Disorder; Syndrome; Testing; Training; Treatment outcome; Triage; Validation; Virus; Visit; Vulnerable Populations; Withdrawal; Work; behavioral health; cohort; comorbidity; coronavirus disease; digital; drug market; experience; high risk; improved; individual patient; interoperability; machine learning method; marijuana use; mortality risk; non-opioid analgesic; novel; opioid misuse; overdose death; pandemic disease; predictive modeling; prognostic; screening; social; social exclusion; social stigma; socioeconomics; substance misuse; success; tool; transmission process; unstructured data

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.

项目总结 患有药物使用障碍的人不成比例地经历着无家可归、贫困、 和慢性疾病(糖尿病和高血压)，这是新出现的感染风险因素 SARS-CoV-2(引起新冠肺炎的病毒的正式名称)。不同类型的物质使用 与呼吸道感染的发展和发展为严重的呼吸衰竭有关，也称为 即急性呼吸窘迫综合征(ARDS)。然而，复杂的症状，如ARDS和行为 像药物滥用这样的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病例 还有ARDS。在医院护理和自动化、数据驱动的解决方案期间，这些信息会被常规记录 利用自然语言处理(NLP)可以从非结构化的数据中提取语义和重要的风险因素 临床病历数据。NLP的计算方法从临床笔记中获得含义，从中 机器学习可以预测患者离开AMA或进展为呼吸衰竭的危险因素。我们的 团队开发了具有80%敏感度/特异度的工具来识别个别类型的物质滥用 具有机器学习(ML)的NLP。我们的单中心模型描述了嵌入在笔记中的风险因素(例如， 精神健康状况、社会经济指标)。此外，我们还开发了一个经过外部验证的 机器学习工具，以高精度识别ARDS病例，以便早期治疗。我们的目标是扩大这一领域 通过跨医疗系统共享数据进行工作，并构建可推广的全面分类器 捕获多种类型的物质滥用，用于冠状病毒感染期间的风险分层和预测 大流行。 我们假设，单一模型的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和准确的方法，以及这些信息 可用于识别那些面临中断护理风险的人和那些面临呼吸衰竭风险的人。我们的目标是培训 并在Rush的60,000多例住院的回顾数据集中测试我们的物质滥用分类器 已使用Universal Screen、AUDIT和DAST进行了手动筛选。此管理 补充剂将使我们能够检查滥用物质与新冠肺炎风险之间的相关性，因为 以及在这些现象背景下的急性呼吸窘迫综合征(ARDS)的发展。