Great Lakes Node of the Drug Abuse Clinical Trials Network

项目摘要 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（导致COVID-19的病毒的正式名称）。不同类型的物质使用已被 与呼吸道感染的发展和严重呼吸衰竭的进展相关，也称为 急性呼吸窘迫综合征（ARDS）。然而，复杂的综合征，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。临床记录和 放射学报告提供了丰富的信息来源，可用于识别物质滥用的情况 和ARDS。这些信息在医院护理期间被常规记录， 自然语言处理（NLP）可以从非结构化的 临床记录的数据。NLP的计算方法从临床笔记中获得意义， 机器学习可以预测患者离开AMA或进展为呼吸衰竭的风险因素。我们 团队开发的工具具有>80%的灵敏度/特异性，可使用 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，我们还开发了一种 机器学习工具，以高准确度识别ARDS病例，进行早期治疗。我们的目标是扩大这一 通过汇集卫生系统的数据，建立一个可推广的综合分类器， 捕获多种类型的物质滥用，用于COVID期间的风险分层和分类 流行病 我们假设单一模型的NLP物质滥用分类器将提供标准化的， 用于对住院患者进行通用分析的可互操作且准确的方法， 可以用来识别那些有中断护理风险的人和那些有呼吸衰竭风险的人。我们的目标是培养 并在Rush对超过60，000例住院病例的回顾性数据集中测试我们的物质滥用分类器 已经用通用屏幕、AUDIT和DAST手动筛选的。这一行政 补充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发生急性呼吸窘迫综合征（ARDS）。

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.