Measuring problem prescription opioid use among patients receiving long-term opioid analgesic treatment: development and evaluation of an algorithm for use in EHR and claims data

Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose

BACKGROUND

This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose.

METHODS

In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST).

RESULTS

The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low.

CONCLUSIONS

Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.

Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study

BACKGROUND

Information regarding opioid use disorder (OUD) status and severity is important for patient care. Clinical notes provide valuable information for detecting and characterizing problematic opioid use, necessitating development of natural language processing (NLP) tools, which in turn requires reliably labeled OUD-relevant text and understanding of documentation patterns.

OBJECTIVE

To inform automated NLP methods, we aimed to develop and evaluate an annotation schema for characterizing OUD and its severity, and to document patterns of OUD-relevant information within clinical notes of heterogeneous patient cohorts.

METHODS

We developed an annotation schema to characterize OUD severity based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, 5th edition. In total, 2 annotators reviewed clinical notes from key encounters of 100 adult patients with varied evidence of OUD, including patients with and those without chronic pain, with and without medication treatment for OUD, and a control group. We completed annotations at the sentence level. We calculated severity scores based on annotation of note text with 18 classes aligned with criteria for OUD severity and determined positive predictive values for OUD severity.

RESULTS

The annotation schema contained 27 classes. We annotated 1436 sentences from 82 patients; notes of 18 patients (11 of whom were controls) contained no relevant information. Interannotator agreement was above 70% for 11 of 15 batches of reviewed notes. Severity scores for control group patients were all 0. Among noncontrol patients, the mean severity score was 5.1 (SD 3.2), indicating moderate OUD, and the positive predictive value for detecting moderate or severe OUD was 0.71. Progress notes and notes from emergency department and outpatient settings contained the most and greatest diversity of information. Substance misuse and psychiatric classes were most prevalent and highly correlated across note types with high co-occurrence across patients.

CONCLUSIONS

Implementation of the annotation schema demonstrated strong potential for inferring OUD severity based on key information in a small set of clinical notes and highlighting where such information is documented. These advancements will facilitate NLP tool development to improve OUD prevention, diagnosis, and treatment.

Identifying patients with opioid use disorder using International Classification of Diseases (ICD) codes: Challenges and opportunities

BACKGROUND AND AIMS

International Classification of Diseases (ICD) diagnosis codes are often used in research to identify patients with opioid use disorder (OUD), but their accuracy for this purpose is not fully evaluated. This study describes application of ICD-10 diagnosis codes for opioid use, dependence and abuse from an electronic health record (EHR) data extraction using data from the clinics' OUD patient registries and clinician/staff EHR entries.

DESIGN

Cross-sectional observational study.

SETTING

Four rural primary care clinics in Washington and Idaho, USA.

PARTICIPANTS

307 patients.

MEASUREMENTS

This study used three data sources from each clinic: (1) a limited dataset extracted from the EHR, (2) a clinic-based registry of patients with OUD and (3) the clinician/staff interface of the EHR (e.g. progress notes, problem list). Data source one included records with six commonly applied ICD-10 codes for opioid use, dependence and abuse: F11.10 (opioid abuse, uncomplicated), F11.20 (opioid dependence, uncomplicated), F11.21 (opioid dependence, in remission), F11.23 (opioid dependence with withdrawal), F11.90 (opioid use, unspecified, uncomplicated) and F11.99 (opioid use, unspecified with unspecified opioid-induced disorder). Care coordinators used data sources two and three to categorize each patient identified in data source one: (1) confirmed OUD diagnosis, (2) may have OUD but no confirmed OUD diagnosis, (3) chronic pain with no evidence of OUD and (4) no evidence for OUD or chronic pain.

FINDINGS

F11.10, F11.21 and F11.99 were applied most frequently to patients who had clinical diagnoses of OUD (64%, 89% and 79%, respectively). F11.20, F11.23 and F11.90 were applied to patients who had a diagnostic mix of OUD and chronic pain without OUD. The four clinics applied codes inconsistently.

CONCLUSIONS

Lack of uniform application of ICD diagnosis codes make it challenging to use diagnosis code data from EHR to identify a research population of persons with opioid use disorder.

Identification of opioid use disorder using electronic health records: Beyond diagnostic codes

BACKGROUND

We used structured and unstructured electronic health record (EHR) data to develop and validate an approach to identify moderate/severe opioid use disorder (OUD) that includes individuals without prescription opioid use or chronic pain, an underrepresented population.

METHODS

Using electronic diagnosis grouper text from EHRs of ~1 million patients (2012-2020), we created indicators of OUD-with "tiers" indicating OUD likelihood-combined with OUD medication (MOUD) orders. We developed six sub-algorithms with varying criteria (multiple vs single MOUD orders, multiple vs single tier 1 indicators, tier 2 indicators, tier 3 and 4 indicators). Positive predictive values (PPVs) were calculated based on chart review to determine OUD status and severity. We compared demographic and clinical characteristics of cases identified by the sub-algorithms.

RESULTS

In total, 14,852 patients met criteria for one of the sub-algorithms. Five sub-algorithms had PPVs ≥0.90 for any severity OUD; four had PPVs ≥0.90 for moderate/severe OUD. Demographic and clinical characteristics differed substantially between groups. Of identified OUD cases, 31.3% had no past opioid analgesic orders, 79.7% lacked evidence of chronic prescription opioid use, and 43.5% lacked a chronic pain diagnosis.

DISCUSSION

Incorporating unstructured data with MOUD orders yielded an approach that adequately identified moderate/severe OUD, identified unique demographic and clinical sub-groups, and included individuals without prescription opioid use or chronic pain, whose OUD may stem from illicit opioids. Findings show that incorporating unstructured data strengthens EHR algorithms for identifying OUD and suggests approaches limited to populations with prescription opioid use or chronic pain exclude many individuals with OUD.

State- and County-Level Geographic Variation in Opioid Use Disorder, Medication Treatment, and Opioid-Related Overdose Among Medicaid Enrollees

Importance

The opioid crisis disproportionately affects Medicaid enrollees, yet little systematic evidence exists regarding how prevalence of and health care utilization for opioid use disorder (OUD) vary across geographical areas.

Objectives

To characterize state- and county-level variation in claims-based prevalence of OUD and rates of medication treatment for OUD and OUD-related nonfatal overdose among Medicaid enrollees.

Design, Setting, and Participants

This cross-sectional study used data from the Transformed Medicaid Statistical Information System Analytic Files from January 1, 2016, to December 31, 2018. Participants were Medicaid enrollees with or without OUD in 46 states; Washington, DC; and Puerto Rico who were aged 18 to 64 years and not dually enrolled in Medicare. The analysis was conducted between September 2022 and April 2023.

Exposure

Calendar-year OUD prevalence.

Main Outcomes and Measures

The main outcomes were claims-based measures of OUD prevalence and rates of medication treatment for OUD and opioid-related nonfatal overdose. Individual records were aggregated at the state and county level, and variation was assessed within and across states.

Results

Of the 76 390 817 Medicaid enrollee-year observations included in our study (mean [SD] enrollee age, 36.5 [1.6] years; 59.0% female), 2 280 272 (3.0%) had a claims-based OUD (mean [SD] age, 38.9 [3.6] years; 51.4% female). Of enrollees with OUD, 41.2% were eligible due to Medicaid expansion, 46.4% had other substance use disorders, 55.8% had mental health conditions, 55.2% had claims indicating some form of OUD medication, and 5.8% had claims indicating an overdose during a calendar year. Claims-based outcomes exhibited substantial variation across states: OUD prevalence ranged from 0.6% in Arkansas and Puerto Rico to 9.7% in Maryland, rates of OUD medication treatment ranged from 17.7% in Kansas to 82.8% in Maine, and rates of overdose ranged from 0.3% in Mississippi to 10.5% in Illinois. Pronounced variation was also found within states (eg, OUD prevalence in Maryland ranged from 2.2% in Prince George's County to 21.6% in Cecil County).

Conclusions and Relevance

In this cross-sectional study of Medicaid enrollees from 2016 to 2018, claims-based prevalence of OUD and rates of OUD medication treatment and opioid-related overdose varied substantially across and within states. Further research appears to be needed to identify important factors influencing this variation.

Trends in Use of Medication to Treat Opioid Use Disorder During the COVID-19 Pandemic in 10 State Medicaid Programs

Importance

Federal and state agencies granted temporary regulatory waivers to prevent disruptions in access to medication for opioid use disorder (MOUD) during the COVID-19 pandemic, including expanding access to telehealth for MOUD. Little is known about changes in MOUD receipt and initiation among Medicaid enrollees during the pandemic.

Objectives

To examine changes in receipt of any MOUD, initiation of MOUD (in-person vs telehealth), and the proportion of days covered (PDC) with MOUD after initiation from before to after declaration of the COVID-19 public health emergency (PHE).

Design, Setting, and Participants

This serial cross-sectional study included Medicaid enrollees aged 18 to 64 years in 10 states from May 2019 through December 2020. Analyses were conducted from January through March 2022.

Exposures

Ten months before the COVID-19 PHE (May 2019 through February 2020) vs 10 months after the PHE was declared (March through December 2020).

Main Outcomes and Measures

Primary outcomes included receipt of any MOUD and outpatient initiation of MOUD via prescriptions and office- or facility-based administrations. Secondary outcomes included in-person vs telehealth MOUD initiation and PDC with MOUD after initiation.

Results

Among a total of 8 167 497 Medicaid enrollees before the PHE and 8 181 144 after the PHE, 58.6% were female in both periods and most enrollees were aged 21 to 34 years (40.1% before the PHE; 40.7% after the PHE). Monthly rates of MOUD initiation, representing 7% to 10% of all MOUD receipt, decreased immediately after the PHE primarily due to reductions in in-person initiations (from 231.3 per 100 000 enrollees in March 2020 to 171.8 per 100 000 enrollees in April 2020) that were partially offset by increases in telehealth initiations (from 5.6 per 100 000 enrollees in March 2020 to 21.1 per 100 000 enrollees in April 2020). Mean monthly PDC with MOUD in the 90 days after initiation decreased after the PHE (from 64.5% in March 2020 to 59.5% in September 2020). In adjusted analyses, there was no immediate change (odds ratio [OR], 1.01; 95% CI, 1.00-1.01) or change in the trend (OR, 1.00; 95% CI, 1.00-1.01) in the likelihood of receipt of any MOUD after the PHE compared with before the PHE. There was an immediate decrease in the likelihood of outpatient MOUD initiation (OR, 0.90; 95% CI, 0.85-0.96) and no change in the trend in the likelihood of outpatient MOUD initiation (OR, 0.99; 95% CI, 0.98-1.00) after the PHE compared with before the PHE.

Conclusions and Relevance

In this cross-sectional study of Medicaid enrollees, the likelihood of receipt of any MOUD was stable from May 2019 through December 2020 despite concerns about potential COVID-19 pandemic-related disruptions in care. However, immediately after the PHE was declared, there was a reduction in overall MOUD initiations, including a reduction in in-person MOUD initiations that was only partially offset by increased use of telehealth.

Duration of medication for opioid use disorder during pregnancy and postpartum by race/ethnicity: Results from 6 state Medicaid programs

BACKGROUND

Medication for opioid use disorder (MOUD) is evidence-based treatment during pregnancy and postpartum. Prior studies show racial/ethnic differences in receipt of MOUD during pregnancy. Fewer studies have examined racial/ethnic differences in MOUD receipt and duration during the first year postpartum and in the type of MOUD received during pregnancy and postpartum.

METHODS

We used Medicaid administrative data from 6 states to compare the percentage of women with any MOUD and the average proportion of days covered (PDC) with MOUD, overall and by type of MOUD, during pregnancy and four postpartum periods (1-90 days, 91-180 days, 181-270 days, and 271-360 days postpartum) among White non-Hispanic, Black non-Hispanic, and Hispanic women diagnosed with OUD.

RESULTS

White non-Hispanic women were more likely to receive any MOUD during pregnancy and all postpartum periods compared to Hispanic and Black non-Hispanic women. For all MOUD types combined and for buprenorphine, White non-Hispanic women had the highest average PDC during pregnancy and each postpartum period, followed by Hispanic women and Black non-Hispanic women (e.g., for all MOUD types, 0.49 vs. 0.41 vs. 0.23 PDC, respectively, during days 1-90 postpartum). For methadone, White non-Hispanic and Hispanic women had similar average PDC during pregnancy and postpartum, and Black non-Hispanic women had substantially lower PDC.

CONCLUSIONS

There are stark racial/ethnic differences in MOUD during pregnancy and the first year postpartum. Reducing these inequities is critical to improving health outcomes among pregnant and postpartum women with OUD.

Prevalence of Testing for Human Immunodeficiency Virus, Hepatitis B Virus, and Hepatitis C Virus Among Medicaid Enrollees Treated With Medications for Opioid Use Disorder in 11 States, 2016-2019

BACKGROUND

Limited information exists about testing for human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV) among Medicaid enrollees after starting medication for opioid use disorder (MOUD), despite guidelines recommending such testing. Our objectives were to estimate testing prevalence and trends for HIV, HBV, and HCV among Medicaid enrollees initiating MOUD and examine enrollee characteristics associated with testing.

METHODS

We conducted a serial cross-sectional study of 505 440 initiations of MOUD from 2016 to 2019 among 361 537 Medicaid enrollees in 11 states. Measures of MOUD initiation; HIV, HBV, and HCV testing; comorbidities; and demographics were based on enrollment and claims data. Each state used Poisson regression to estimate associations between enrollee characteristics and testing prevalence within 90 days of MOUD initiation. We pooled state-level estimates to generate global estimates using random effects meta-analyses.

RESULTS

From 2016 to 2019, testing increased from 20% to 25% for HIV, from 22% to 25% for HBV, from 24% to 27% for HCV, and from 15% to 19% for all 3 conditions. Adjusted rates of testing for all 3 conditions were lower among enrollees who were male (vs nonpregnant females), living in a rural area (vs urban area), and initiating methadone or naltrexone (vs buprenorphine). Associations between enrollee characteristics and testing varied across states.

CONCLUSIONS

Among Medicaid enrollees in 11 US states who initiated medications for opioid use disorder, testing for human immunodeficiency virus, hepatitis B virus, hepatitis C virus, and all 3 conditions increased between 2016 and 2019 but the majority were not tested.

Algorithms to Identify Nonmedical Opioid Use

The rise in nonmedical opioid overdoses over the last two decades necessitates improved detection technologies. Manual opioid screening exams can exhibit excellent sensitivity for identifying the risk of opioid misuse but can be time-consuming. Algorithms can help doctors identify at-risk people. In the past, electronic health record (EHR)-based neural networks outperformed Drug Abuse Manual Screenings in sparse studies; however, recent data shows that it may perform as well or less than manual screenings. Herein, a discussion of several different manual screenings and recommendations is contained, along with suggestions for practice. A multi-algorithm approach using EHR yielded strong predictive values of opioid use disorder (OUD) over a large sample size. A POR (Proove Opiate Risk) algorithm provided a high sensitivity for categorizing the risk of opioid abuse within a small sample size. All established screening methods and algorithms reflected high sensitivity and positive predictive values. Neural networks based on EHR also showed significant effectiveness when corroborated with Drug Abuse Manual Screenings. This review highlights the potential of algorithms for reducing provider costs and improving the quality of care by identifying nonmedical opioid use (NMOU) and OUD. These tools can be combined with traditional clinical interviewing, and neural networks can be further refined while expanding EHR.

Development and testing of data infrastructure in the American College of Emergency Physicians' Clinical Emergency Data Registry for opioid-related research

Objective

Prior research has identified gaps in the capacity of electronic health records (EHRs) to capture the intricacies of opioid-related conditions. We sought to enhance the opioid data infrastructure within the American College of Emergency Physicians' Clinical Emergency Data Registry (CEDR), the largest national emergency medicine registry, through data mapping, validity testing, and feasibility assessment.

Methods

We compared the CEDR data dictionary to opioid common data elements identified through prior environmental scans of publicly available data systems and dictionaries used in national informatics and quality measurement of policy initiatives. Validity and feasibility assessments of CEDR opioid-related data were conducted through the following steps: (1) electronic extraction of CEDR data meeting criteria for an opioid-related emergency care visit, (2) manual chart review assessing the quality of the extracted data, (3) completion of feasibility scorecards, and (4) qualitative interviews with physician reviewers and informatics personnel.

Results

We identified several data gaps in the CEDR data dictionary when compared with prior environmental scans including urine drug testing, opioid medication, and social history data elements. Validity testing demonstrated correct or partially correct data for >90% of most extracted CEDR data elements. Factors affecting validity included lack of standardization, data incorrectness, and poor delimitation between emergency department (ED) versus hospital care. Feasibility testing highlighted low-to-moderate feasibility of date and social history data elements, significant EHR platform variation, and inconsistency in the extraction of common national data standards (eg, Logical Observation Identifiers Names and Codes, International Classification of Diseases, Tenth Revision codes).

Conclusions

We found that high-priority data elements needed for opioid-related research and clinical quality measurement, such as demographics, medications, and diagnoses, are both valid and can be feasibly captured in a national clinical quality registry. Future work should focus on implementing structured data collection tools, such as standardized documentation templates and adhering to data standards within the EHR that would better characterize ED-specific care for opioid use disorder and related research.

Considerations for observational study design: Comparing the evidence of opioid use between electronic health records and insurance claims

PURPOSE

Pharmacoepidemiology studies often use insurance claims and/or electronic health records (EHR) to capture information about medication exposure. The choice between these data sources has important implications.

METHODS

We linked EHR from a large academic health system (2015-2017) to Medicare insurance claims for patients undergoing surgery. Drug utilization was characterized based on medication order dates in the EHR, and prescription fill dates in Medicare claims. We compared opioid use documented in EHR orders to prescription claims in four time periods: 1) Baseline (182 days before surgery); 2) Perioperative period; 3) Discharge date; 4) Follow-up (90 days after surgery).

RESULTS

We identified 11 128 patients undergoing surgery. During baseline, 34.4% (EHR) versus 44.1% (claims) had evidence of opioid use, and 56.9% of all baseline use was reflected only in one data source. During the perioperative period, 78.8% (EHR) versus 47.6% (claims) had evidence of use. On the day of discharge, 59.6% (EHR) versus 45.5% (claims) had evidence of use, and 51.8% of all discharge use was reflected only in one data source. During follow-up, 4.3% (EHR) versus 10.4% (claims) were identified with prolonged opioid use following surgery with 81.4% of all prolonged use reflected only in one data source.

CONCLUSIONS

When characterizing opioid exposure, we found substantial discrepancies between EHR medication orders and prescription claims data. In all time periods assessed, most patients' use was reflected only in the EHR, or only in the claims, not both. The potential for misclassification of drug utilization must be evaluated carefully, and choice of data source may have large impacts on key study design elements.

Endogenous opiates and behavior: 2020

This paper is the forty-third consecutive installment of the annual anthological review of research concerning the endogenous opioid system, summarizing articles published during 2020 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides and receptors as well as effects of opioid/opiate agonists and antagonists. The review is subdivided into the following specific topics: molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors (1), the roles of these opioid peptides and receptors in pain and analgesia in animals (2) and humans (3), opioid-sensitive and opioid-insensitive effects of nonopioid analgesics (4), opioid peptide and receptor involvement in tolerance and dependence (5), stress and social status (6), learning and memory (7), eating and drinking (8), drug abuse and alcohol (9), sexual activity and hormones, pregnancy, development and endocrinology (10), mental illness and mood (11), seizures and neurologic disorders (12), electrical-related activity and neurophysiology (13), general activity and locomotion (14), gastrointestinal, renal and hepatic functions (15), cardiovascular responses (16), respiration and thermoregulation (17), and immunological responses (18).

JP. Feasibility of using real-world data in the evaluation of cardiac ablation catheters: a test-case of the National Evaluation System for Health Technology Coordinating Center

OBJECTIVES

To determine the feasibility of using real-world data to assess the safety and effectiveness of two cardiac ablation catheters for the treatment of persistent atrial fibrillation and ischaemic ventricular tachycardia.

DESIGN

Retrospective cohort.

SETTING

Three health systems in the USA.

PARTICIPANTS

Patients receiving ablation with the two ablation catheters of interest at any of the three health systems.

MAIN OUTCOME MEASURES

Feasibility of identifying the medical devices and participant populations of interest as well as the duration of follow-up and positive predictive values (PPVs) for serious safety (ischaemic stroke, acute heart failure and cardiac tamponade) and effectiveness (arrhythmia-related hospitalisation) clinical outcomes of interest compared with manual chart validation by clinicians.

RESULTS

Overall, the catheter of interest for treatment of persistent atrial fibrillation was used for 4280 ablations and the catheter of interest for ischaemic ventricular tachycardia was used 1516 times across the data available within the three health systems. The duration of patient follow-up in the three health systems ranged from 91% to 97% at ≥7 days, 89% to 96% at ≥30 days, 77% to 90% at ≥6 months and 66% to 84% at ≥1 year. PPVs were 63.4% for ischaemic stroke, 96.4% for acute heart failure, 100% at one health system for cardiac tamponade and 55.7% for arrhythmia-related hospitalisation.

CONCLUSIONS

It is feasible to use real-world health system data to evaluate the safety and effectiveness of cardiac ablation catheters, though evaluations must consider the implications of variation in follow-up and endpoint ascertainment among health systems.

Use of Medications for Treatment of Opioid Use Disorder Among US Medicaid Enrollees in 11 States, 2014-2018

Importance

There is limited information about trends in the treatment of opioid use disorder (OUD) among Medicaid enrollees.

Objective

To examine the use of medications for OUD and potential indicators of quality of care in multiple states.

Design, Setting, and Participants

Exploratory serial cross-sectional study of 1 024 301 Medicaid enrollees in 11 states aged 12 through 64 years (not eligible for Medicare) with International Classification of Diseases, Ninth Revision (ICD-9 or ICD-10) codes for OUD from 2014 through 2018. Each state used generalized estimating equations to estimate associations between enrollee characteristics and outcome measure prevalence, subsequently pooled to generate global estimates using random effects meta-analyses.

Exposures

Calendar year, demographic characteristics, eligibility groups, and comorbidities.

Main Outcomes and Measures

Use of medications for OUD (buprenorphine, methadone, or naltrexone); potential indicators of good quality (OUD medication continuity for 180 days, behavioral health counseling, urine drug tests); potential indicators of poor quality (prescribing of opioid analgesics and benzodiazepines).

Results

In 2018, 41.7% of Medicaid enrollees with OUD were aged 21 through 34 years, 51.2% were female, 76.1% were non-Hispanic White, 50.7% were eligible through Medicaid expansion, and 50.6% had other substance use disorders. Prevalence of OUD increased in these 11 states from 3.3% (290 628 of 8 737 082) in 2014 to 5.0% (527 983 of 10 585 790) in 2018. The pooled prevalence of enrollees with OUD receiving medication treatment increased from 47.8% in 2014 (range across states, 35.3% to 74.5%) to 57.1% in 2018 (range, 45.7% to 71.7%). The overall prevalence of enrollees receiving 180 days of continuous medications for OUD did not significantly change from the 2014-2015 to 2017-2018 periods (-0.01 prevalence difference, 95% CI, -0.03 to 0.02) with state variability in trend (90% prediction interval, -0.08 to 0.06). Non-Hispanic Black enrollees had lower OUD medication use than White enrollees (prevalence ratio [PR], 0.72; 95% CI, 0.64 to 0.81; P < .001; 90% prediction interval, 0.52 to 1.00). Pregnant women had higher use of OUD medications (PR, 1.18; 95% CI, 1.11-1.25; P < .001; 90% prediction interval, 1.01-1.38) and medication continuity (PR, 1.14; 95% CI, 1.10-1.17, P < .001; 90% prediction interval, 1.06-1.22) than did other eligibility groups.

Conclusions and Relevance

Among US Medicaid enrollees in 11 states, the prevalence of medication use for treatment of opioid use disorder increased from 2014 through 2018. The pattern in other states requires further research.

Improving visual communication of discriminative accuracy for predictive models: the probability threshold plot

Objectives

To propose a visual display-the probability threshold plot (PTP)-that transparently communicates a predictive models' measures of discriminative accuracy along the range of model-based predicted probabilities (Pt).

Materials and Methods

We illustrate the PTP by replicating a previously-published and validated machine learning-based model to predict antihyperglycemic medication cessation within 1-2 years following metabolic surgery. The visual characteristics of the PTPs for each model were compared to receiver operating characteristic (ROC) curves.

Results

A total of 18 887 patients were included for analysis. Whereas during testing each predictive model had nearly identical ROC curves and corresponding area under the curve values (0.672 and 0.673), the visual characteristics of the PTPs revealed substantive between-model differences in sensitivity, specificity, PPV, and NPV across the range of Pt.

Discussion and Conclusions

The PTP provides improved visual display of a predictive model's discriminative accuracy, which can enhance the practical application of predictive models for medical decision making.

Genome-wide association analysis of opioid use disorder: A novel approach using clinical data

BACKGROUND

Opioid use disorder (OUD) represents a large and pervasive global public health challenge. Previous genetic studies have demonstrated the significant heritability of OUD and identified several single-nucleotide polymorphisms (SNPs) associated with its prevalence.

METHODS

In this paper, we conducted a genome-wide association analysis on opioid use disorder that leveraged genetic and clinical data contained in a biobank of 21,310 patients of European ancestry. We identified 1039 cases of opioid use disorder based on diagnostic codes from nearly 16 million encounters in electronic health records (EHRs).

RESULTS

We discovered one novel OUD-associated locus on chromosome 4 that was significant at a genome-wide threshold (p = 2.40 × 10^-8). Heritability analysis suggested that common SNPs explained 0.06 (se 0.02, p = 0.0065) of the phenotypic variation in OUD. When we restricted controls to those with previous opioid prescriptions, we were able to further strengthen the original signal and discovered another significant locus on chromosome 16. Pair-wise genetic correlation analysis yielded strong positive correlations between OUD and two other major substance use disorders, alcohol and nicotine, with the strongest correlation between nicotine and opioid use disorder (genetic correlation 0.65, se = 0.19, p = 0.00048), suggesting a significant shared genetic component across different substance disorders.

CONCLUSIONS

This pragmatic, clinically-focused approach may supplement more traditional methods to facilitate identification of new genetic underpinnings of OUD and related disorders.