Identifying high-risk comorbidities of short and long-term opioid prescription use

Abstract

BackgroundElectronic health records (EHR) are useful tools for understanding complex medical phenotypes, but they have been underutilized for opioid use disorders (OUD). Patterns of prescription opioid use might provide an objective measure of OUD risk.MethodsWe extracted data for over 2.6 million patients across three health registries (Vanderbilt University Medical Center, Mass General Brigham, Geisinger) between 2005 and 2018. We defined three groups based on levels of opioid exposure: No Prescription, Minimal Exposure (2 prescriptions within 90 days at least once, but never 3 prescriptions <90 days apart), and Chronic Exposure (≥10 opioid prescriptions in a year), and compared them to the full registries and to patients with OUD diagnostic codes. We extracted demographic and clinical characteristics known to co-occur with OUD, including psychiatric and substance use disorders, pain-related diagnoses, HIV, and hepatitis C.ResultsThe prevalence of substance (alcohol, tobacco, cannabis) use disorders was higher in patients with OUD and Chronic Exposure than those with No Prescription or Minimal Exposure. Patients in the OUD and Chronic Exposure groups had more psychiatric (anxiety, depression, schizophrenia, bipolar disorder) and medical comorbidities (pain, hepatitis C, HIV) than those in the Minimal Exposure group. Notably, patients in the Minimal Exposure group had different comorbidity profiles (higher rates of substance use and psychiatric disorders, more pain conditions) than those in the Unscreened or No Prescription groups, highlighting the value of including opioid exposure in studies of OUD.ConclusionsLong-term opioid prescription use may serve as an additional tool to characterize OUD risk.

Accelerating Opioid Use Disorders Research by Integrating Multiple Data Modalities

“…The breadth of EHRs allows us to augment our use of ICD codes with other clinical data, such as data about opioid prescriptions, diagnostic tests, and sociodemographic information. The inclusion of opioid prescriptions can serve as additional tools to identify initial use, problematic opioid use [21], and OUD [21, 38, 43, 52]. For example, while recreational use of illicitly obtained opioids is rarely captured in the EHR, we can characterize forms of prescription opioid misuse via prescription counts or clinical notes, rather than querying a participant about off-label use (e.g., [38, 53]).…”

“…The inclusion of opioid prescriptions can serve as additional tools to identify initial use, problematic opioid use [21], and OUD [21, 38, 43, 52]. For example, while recreational use of illicitly obtained opioids is rarely captured in the EHR, we can characterize forms of prescription opioid misuse via prescription counts or clinical notes, rather than querying a participant about off-label use (e.g., [38, 53]). Furthermore, by using longitudinal patterns of opioid prescriptions, we can uncover trajectories suggestive of misuse.…”

“…Although using an unscreened control group can lead to substantial increases in sample size, it can also result in considerable phenotypic heterogeneity across samples and can bias findings [10, 37], namely, the inability to examine whether an individual would have developed OUD had they been exposed to opioids. The amount of exposure required for such groups is not yet clear, but using opioid exposed control groups is vital for future studies [38].…”