Trajectories of prescription opioids filled over time

Elmer, Jonathan; Fogliato, Riccardo; Setia, Nikita; Mui, Wilson; Lynch, Michael J.; Hulsey, Eric; Nagin, Daniel S.

doi:10.1371/journal.pone.0222677

Cited by 15 publications

(27 citation statements)

References 24 publications

(21 reference statements)

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“…For example, to identify individuals at risk for developing OUD, phenotype risk scores [48,49] could be constructed by agnostically training and testing a risk model using diagnosis codes (for OUD and other relevant predictors) and opioid prescriptions. Such a strategy would not only acknowledge OUD risk in the absence of an OUD diagnosis but could also allow for modeling of OUD risk trajectories over time [50]. Second, our descriptive data confirm the relevance of several predictors of opioid misuse (e.g., age, substance and psychiatric comorbidities) that have been previously incorporated into algorithms identifying opioid misuse but not necessarily validated [19,51] into algorithms identifying opioid misuse.…”

Section: Discussionsupporting

confidence: 63%

Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data

Jennings

Lee

Rocha³

et al. 2022

Complex Psychiatry

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Introduction. Opioid use disorders (OUD) constitute a major public health issue, and we urgently need alternative methods for characterizing risk for OUD. Electronic health records (EHR) are useful tools for understanding complex medical phenotypes, but have been underutilized for OUD because of challenges related to underdiagnosis, binary diagnostic frameworks, and minimally-characterized reference groups. As a first step in addressing these challenges, a new paradigm is warranted that characterizes risk for opioid prescription misuse on a continuous scale of severity, i.e., as a continuum. Methods. Across sites within the PsycheMERGE network, we extracted prescription opioid data and diagnoses that co-occur with OUD (including psychiatric and substance use disorders, pain-related diagnoses, HIV, and hepatitis C) 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 prescriptions, minimal exposure, and chronic exposure and then compared the comorbidity profiles of these groups to the full registries and to those with OUD diagnostic codes. Results. Our results confirm that EHR data reflects known higher prevalence of substance use disorders, psychiatric disorders, medical, and pain diagnoses in patients with OUD diagnoses and chronic opioid use. Comorbidity profiles that distinguish opioid exposure are strikingly consistent across large health systems, indicating the phenotypes described in this new quantitative framework are robust to health systems differences. Conclusion. This work indicates that EHR prescription opioid data can serve as a platform to characterize complex risk markers for OUD using existing data.

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Section: Discussionsupporting

confidence: 63%

Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data

Jennings

Lee

Rocha³

et al. 2022

Complex Psychiatry

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“…Previous studies have evaluated initial opioid trajectories among opioid-naïve patients; 34 , 35 however, to our knowledge, this is the first study to identify trajectories among patients initiating LTOT that provides insights on the course of opioid therapy among patients on the most risky opioid therapy regimens over the two years after initiating LTOT. Nine trajectories were identified that we broadly characterized into three overall opioid therapy patterns: persistent days of opioid coverage, reductions in opioid coverage, or discontinuation of opioid therapy.…”

Section: Discussionmentioning

confidence: 99%

Trajectories of Opioid Coverage After Long-Term Opioid Therapy Initiation Among a National Cohort of US Veterans

Hayes

Gressler

et al. 2021

JPR

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Purpose The objective of this study was to identify the trajectories that patients take after initiating long-term opioid therapy (LTOT). Materials and Methods Using a retrospective cohort design, veterans with chronic non-cancer pain (CNCP) initiating LTOT were identified. Group-based trajectory models were used to identify opioid therapy trajectories based on days of opioid supply (primary outcome) and average daily morphine milligram equivalent dose (AMME; secondary outcome) in each 180-day period following initiation of LTOT. Results A total of 438,398 veterans with CNCP initiated LTOT. Nine trajectories were identified: 33.7% with persistent, high days covered, 17.7% with persistent, moderate days covered, 16.6% with slow, persistent days-covered reduction, 2.4% with days-covered reduction followed by increase, 4.6% with delayed days-covered reduction, 4.1% with rapid days-covered reduction, 10.9% with moderate-paced discontinuation, 3.4% with delayed discontinuation, and 6.5% with rapid discontinuation. Patients following discontinuation trajectories were more likely to be younger, persons of color, use more supportive services (eg, physical therapy), and received less opioid days’ supply and lower doses prior to initiating LTOT as compared to patients following persistent opioid days-covered trajectories. AMME trajectories were similar to days-covered trajectories. Conclusion Among persons initiating LTOT, nine opioid trajectories emerged which can be broadly characterized into three main trajectory groups: persistent opioid therapy (2 trajectories), reductions in opioid therapy (4 trajectories), and discontinuation (3 trajectories). A majority of patients (51.4%) maintained persistent opioid therapy. Further research is needed to assess the risks of opioid-related adverse outcomes among the identified trajectories.

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“…For example, to identify individuals at risk for developing OUD, phenotype risk scores (Bastarache et al, 2019; Ruderfer et al, 2020) could be constructed by agnostically training and testing a risk model using diagnosis codes (for OUD and other relevant predictors) and prescriptions. Such a strategy would not only acknowledge OUD risk in the absence of an OUD diagnosis, but could also allow for modeling of OUD risk trajectories over time (Elmer et al, 2019). Second, our descriptive data confirms the relevance of several predictors of opioid misuse (e.g., age, substance and psychiatric comorbidities) that have been previously incorporated (but not necessarily validated; Canan et al, 2017; Schirle et al, 2021) into algorithms identifying opioid misuse.…”

Section: Discussionmentioning

confidence: 99%

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

Jennings

Lee

Rocha³

et al. 2021

Preprint

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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.

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Trajectories of prescription opioids filled over time

Cited by 15 publications

References 24 publications

Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data

Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data

Trajectories of Opioid Coverage After Long-Term Opioid Therapy Initiation Among a National Cohort of US Veterans

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

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