# Improving the Identification and Management of Suicide Risk among Patients Using Prescription Opioids

> **NIH NIH R01** · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · 2020 · $853,369

## Abstract

ABSTRACT
The opioid epidemic in the US, coupled with emerging data on the association between opioid use and suicidal
behavior, highlights the urgent need for research that identifies patients at greatest risk and provides strategies
to mitigate this risk. However, the development of risk algorithms that provide clinical guidance for providers
with patients using opioids who may have underlying mental disorders or comorbid diagnoses has been
nonexistent. Recent work has demonstrated the compelling need for a better understanding of, and clinical
decision support tools to address, suicide risk among prescription opioid drug users. The proposed study
provides an opportunity to address gaps in both the identification and clinical management of suicide risk in
patients using prescription opioids. We propose to use transfer learning approaches to identify the clinical and
demographic characteristics associated with elevated risk of suicidal behavior among prescription opioid users;
to develop clinical phenotypes of patients with higher risk of suicidal behavior associated with prescription
opioids, and to incorporate these phenotypes in a clinical decision support platform that can be used for
identification and intervention at the point of care; and to conduct a pilot study implementing and evaluating the
impact of the clinical decision support platform in the existing clinical workflow in 3 diverse clinical settings. Our
approach is specifically designed to address the incomplete picture of patient risk in existing models due to the
fragmentation of clinical care across settings. We do this by developing algorithms that can statistically
“borrow” information from more comprehensive patient datasets and apply it to more limited datasets in a
particular healthcare setting. The proposed work will draw on comprehensive clinical data from a mature
health information exchange containing more than 2.3 million patients across the spectrum of clinical care to
develop a statistically robust method to measure suicide risk associated with prescription opioid use. The
clinical decision support tool developed under this proposal will provide a generalizable platform that could be
extended to other opioid related risks, e.g., OUD and overdose. The potential public health significance of this
study is substantial. The fragmentation of the healthcare system, particularly in relation to patients' behavioral
health needs, highlights the critical need to cultivate comprehensive, system-wide approaches to identifying
and managing at patients using prescription opioids who are at risk of suicide.

## Key facts

- **NIH application ID:** 10155895
- **Project number:** 3R01MH112148-03S1
- **Recipient organization:** UNIVERSITY OF CONNECTICUT SCH OF MED/DNT
- **Principal Investigator:** Robert H Aseltine
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $853,369
- **Award type:** 3
- **Project period:** 2020-09-18 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10155895

## Citation

> US National Institutes of Health, RePORTER application 10155895, Improving the Identification and Management of Suicide Risk among Patients Using Prescription Opioids (3R01MH112148-03S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10155895. Licensed CC0.

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