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
Summary of Findings
Funder: NIH, NIDA
Grant Number: R01ActDA043811
Grant Period: 4/1/2019 – 3/31/2023
Narrative: Research on the association between psychopathology and prescription opioid analgesic use (OAU) has established that mental illness influences risk of chronic OAU (i.e. >90-days), high dose OAU and misuse. We explored the reverse direction of association and found longer OAU and higher opioid doses are associated with increased risk of new onset depression (NOD), independent of pain. Using Veterans Health Affairs (VA) patient data revealed >90-day OAU was associated with a 35% (in VA patients) to 105% (in private sector patients) increased risk of NOD compared to patients with 1-30 day OAU. Our additional studies revealed that OAU is associated with depression recurrence and treatment resistant depression. If these results are confirmed in the present proposal, results have potential to greatly inform interventions to reduce chronic OAU (e.g. treating depression), elucidate pathways to OAU misuse, and generate a body of evidence that informs safe opioid prescribing. To reveal pathways from OAU to NOD and related depression phenotypes (i.e. dysthymia, bipolar, anhedonia, vital exhaustion) we must measure the patients’ pre-existing risk factors and post-OAU events. We will obtain diagnoses and symptom level data and covariates that are not available in the medical record data used in our R21 and strengthen the temporal relationships between OAU and NOD. The central hypothesis driving this research is that pre-OAU risk factors such as a history of depression and post-OAU events such as onset of opioid misuse contribute to NOD.
If NOD is explained by OAU alone and not by pre-existing risk factors, then the opioid epidemic is generating new cases of depression in a large population of middle-aged adults, otherwise not at risk for NOD. Findings will disentangle consequences or correlates of chronic pain per se from those of chronic, high dose OAU. We test whether the OAU-NOD association is moderated by pre-existing depression, substance use disorder (SUD), including opioid use disorder and trauma exposure. We next propose that post-OAU opioid misuse, SUD, poor functioning, low social support and poor sleep quality promote NOD. Using 12 monthly brief assessments, we will determine if change in OAU, independent of change in pain influences, depression trajectories and determine if there is a reciprocal relationship among these variables over time. We will determine if OAU is associated with different depression phenotypes and last determine which subtypes of depression contribute to incident opioid use disorder.
Lead Site: St. Louis University (PI Jeffrey Scherrer)
Participating Sites: HFHS (Site PI Brian Ahmedani)
Summary of Findings: