# Great Lakes Node of the Drug Abuse Clinical Trials Network

> **NIH NIH UG1** · RUSH UNIVERSITY MEDICAL CENTER · 2020 · $139,752

## Abstract

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.

## Key facts

- **NIH application ID:** 10173503
- **Project number:** 3UG1DA049467-02S1
- **Recipient organization:** RUSH UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** DAVID H GUSTAFSON
- **Activity code:** UG1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $139,752
- **Award type:** 3
- **Project period:** 2019-06-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10173503

## Citation

> US National Institutes of Health, RePORTER application 10173503, Great Lakes Node of the Drug Abuse Clinical Trials Network (3UG1DA049467-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10173503. Licensed CC0.

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