Predictive modeling: the role of opioid use in suicide risk

Grant Details

Funder: NIDA

Grant Number: R01DA047724

Grant Period: 8/15/2018 – 6/30/2022

Narrative: Suicide deaths and opioid-related overdose deaths have both been increasing in recent years. These two public health crises have substantial overlap: our preliminary work suggests that between 22% and 37% of opioid-related overdoses are suicides or suicide attempts. Healthcare settings are ideal places to intervene to prevent suicides, however clinicians need better tools to recognize the patients at greatest risk. We developed models that predict risk of suicide attempt or death with 83% to 86% accuracy. However, these models do not include important opioid-related variables. In a parallel body of work, we developed algorithms based on coded electronic health record (EHR) data to identify opioid-related overdoses and classify them as unintentional or intentional suicides. The proposed project integrates these two existing lines of research. Our suicide risk prediction dataset includes seven large healthcare systems and approximately 20 million visits by 3 million patients; it is currently being expanded to include additional outcomes and visits through 2016, and additional predictors, however inclusion of opioid-related variables was not part of the funded supplement. In the proposed study, we will determine whether including variables related to illicit and prescribed opioid use, opioid use disorder, discontinuation or significant dose reductions of prescription opioids, or prior non-fatal opioid-related overdoses improves predictions of suicide attempts or death within 90 days following an outpatient healthcare visit. We will also develop models that specifically predict opioid- related suicide attempts and deaths in the sample as a whole and among people prescribed opioid medications, and determine if the predictors of opioid-related suicide attempts or deaths are consistent for men and women. The goal of the proposed work is to maximize the performance of our models in order to create the best available tools for clinicians to help reduce future suicides. We have an established collaboration with the largest national EHR vendor and are working to develop an EHR-based, point-of-care clinical tool to predict suicide attempts and deaths based on our research findings. This work will therefore have a direct impact on clinical practice by providing clinicians with an efficient, evidence-based tool to evaluate suicide risk. The work will also provide critical data on understudied opioid-related predictors and moderators of suicide.

Lead Site: KPNW (PI Bobbi Jo Yarborough)

Participating Sites: HFHS, HPI, KPCO, KPHI, KPSC, KPWA

Current Status

Summary of Findings