The stability flexibility tradeoff and the dark side of detail.

Learning in dynamic environments requires integrating over stable fluctuations to minimize the impact of noise (stability) but rapidly responding in the face of fundamental changes (flexibility). Achieving one of these goals often requires sacrificing the other to some degree, producing a stability-flexibility tradeoff. Individuals navigate this tradeoff in different ways; some people learn rapidly (emphasizing flexibility) and others rely more heavily on historical information (emphasizing stability). Despite the prominence of such individual differences in learning tasks, the degree to which they relate to broader characteristics of real-world behavior or pathologies has not been well explored. We relate individual differences in learning behavior to self-report measures thought to capture collectively the characteristics of the Autism spectrum. We show that young adults who learn most slowly tend to integrate more effective samples into their beliefs about the world making them more robust to noise (more stability) but are more likely to integrate information from previous contexts (less flexibility). We show that individuals who report paying more attention to detail tend to use high flexibility and low stability information processing strategies. We demonstrate the robustness of this inverse relationship between attention to detail and formation of stable beliefs in a heterogeneous population of children that includes a high proportion of Autism diagnoses. Together, our results highlight that attention to detail reflects an information processing policy that comes with a substantial downside, namely the ability to integrate data to overcome environmental noise.

Co-Occurring Opioid Use and Depressive Disorders: Patient Characteristics and Co-Occurring Health Conditions.

OBJECTIVE: Among persons with opioid use disorder (OUD), co-occurring depression is linked to a greater risk of opioid misuse, overdose and suicide. Less is known about characteristics and other comorbid health conditions of persons with co-occurring opioid use and depressive disorders. METHODS: This study used electronic health record (EHR) encounters from the Geisinger Health System prior to the fall of 2019. Adult patients were recruited from a medication-based treatment clinic and had an OUD diagnosis (N = 692). Co-occurring depression was defined by a depression diagnosis in the EHR. Multivariable logistic regression was performed to assess differences in characteristics, behavioral health and medical diagnoses, as well as opioid overdose and suicide attempt or ideation between individuals with and without comorbid depression. RESULTS: Forty-seven percent of patients with OUD had a lifetime depression diagnosis. Individuals with co-occurring depression were more likely to be female and have comorbid chronic pain or other medical conditions. Co-occurring depression was associated with an increased likelihood of other mental health and substance use disorders, as well as opioid overdose and/or suicide attempt or ideation. CONCLUSIONS: While it is established that co-occurring depression is associated with increased risk of overdose and suicide, this study adds that other health conditions, including chronic pain and common medical conditions, are more prevalent among persons with co-occurring depressive disorders. Results highlight the need to consider these complex health needs when developing treatment plans and services.

The use of the orbitofrontal H-sulcus as a reference frame for value signals.

Understanding the factors that drive organization and function of the brain is an enduring question in neuroscience. Using functional magnetic resonance imaging (fMRI), structure and function have been mapped in primary sensory cortices based on knowledge of the organizational principles that likely drive a given region (e.g., aspects of visual form in primary visual cortex and sound frequency in primary auditory cortex) and knowledge of underlying cytoarchitecture. The organizing principles of higher-order brain areas that encode more complex signals, such as the orbitofrontal cortex (OFC), are less well understood. One fundamental component that underlies the many functions of the OFC is the ability to compute the reward or value of a given object. There is evidence of variability in the spatial location of responses to specific categories of objects (or value of said objects) within the OFC, and several reference frames have been proposed to explain this variability, including topographic spatial gradients that correspond to axes of primary versus secondary rewards and positive versus negative reinforcers. One potentially useful structural morphometric reference frame in the OFC is the "H-sulcus," a pattern formed by medial orbital, lateral orbital and transverse orbital sulci. In 48 human subjects, we use a structural morphometric tracing procedure to localize functional activation along the H-sulcus for face and food stimuli. We report the novel finding that food-selective responses are consistently found within the caudal portion of the medial orbital sulcus, but no consistency within the H-sulcus for response to face stimuli. These results suggest that sulcogyral anatomy of the H-sulcus may be an important morphological metric that contributes to the organizing principles of the OFC response to certain stimulus categories, including food.

Distinct and overlapping fusiform activation to faces and food.

The fusiform cortex is a part of the ventral visual stream and is typically associated with face processing. Indeed, a subregion of the fusiform has been named the "fusiform face area" or FFA, based on its robust response to faces relative to other objects. In a separate literature, appetizing food has also been shown to activate bilateral fusiform cortex, yet no study to date has directly compared face and food responses within the same paradigm. Here, we use functional magnetic resonance imaging (fMRI) to compare face and food responses in ventral visual cortex and other regions that are typically associated with face processing. We present evidence that a region of the left fusiform cortex (typically associated with face processing) actually responds equally to faces and food. We go on to describe the similarities and differences in location of face- and food-responses in the fusiform, the relationship of fusiform activation to body mass index (BMI), and resting state connectivity of face- and food-selective fusiform. Results are interpreted within a model in which motivational relevance or value influence fusiform response.

Anhedonia and individual differences in orbitofrontal cortex sulcogyral morphology.

Three types of orbitofrontal cortex (OFC) sulcogyral patterns that have been identified in the population, and the distribution of these three types in clinically diagnosed schizophrenic patients has been found to be distinct from the normal population. Schizophrenia is associated with increased levels of social and physical anhedonia. In this study, we asked whether variation in anhedonia in a neurologically normal population is associated with altered sulcogyral pattern frequency. OFC sulcogyral type was classified and anhedonia was measured in 58 normal young adults, and the relationship between OFC sulcogyral type and anhedonia was explored. In line with other studies conducted in chronic schizophrenia, individuals with higher levels of physical anhedonia demonstrated atypical sulcogyral patterns. Individuals with higher physical anhedonia showed a reduced incidence of Type I OFC and an increased incidence of Type II OFC in the left hemisphere compared to individuals with lower physical anhedonia. These findings support the notion that Type I OFC sulcogyral pattern is protective of anhedonia compared to Type II, even in individuals that are not schizophrenic. Overall, these results support the view that symptoms and neural indices typically associated with neuropsychiatric disorders actually reflect quantitative traits that are continuously distributed throughout the general population. Hum Brain Mapp 37:3873-3881, 2016. © 2016 Wiley Periodicals, Inc.

Multiple object properties drive scene-selective regions.

Neuroimaging studies have identified brain regions that respond preferentially to specific stimulus categories, including 3 areas that activate maximally during viewing of real-world scenes: The parahippocampal place area (PPA), retrosplenial complex (RSC), and transverse occipital sulcus (TOS). Although these findings suggest the existence of regions specialized for scene processing, this interpretation is challenged by recent reports that activity in scene-preferring regions is modulated by properties of isolated single objects. To understand the mechanisms underlying these object-related responses, we collected functional magnetic resonance imaging data while subjects viewed objects rated along 7 dimensions, shown both in isolation and on a scenic background. Consistent with previous reports, we find that scene-preferring regions are sensitive to multiple object properties; however, results of an item analysis suggested just 2 independent factors--visual size and the landmark suitability of the objects--sufficed to explain most of the response. This object-based modulation was found in PPA and RSC irrespective of the presence or absence of a scenic background, but was only observed in TOS for isolated objects. We hypothesize that scene-preferring regions might process both visual qualities unique to scenes and spatial qualities that can appertain to either scenes or objects.

Recruitment in Appalachian, Rural and Older Adult Populations in an Artificial Intelligence World: Study Using Human-Mediated Follow-Up.

BACKGROUND: Participant recruitment in rural and hard-to-reach (HTR) populations can present unique challenges. These challenges are further exacerbated by the need for low-cost recruiting, which often leads to use of web-based recruitment methods (eg, email, social media). Despite these challenges, recruitment strategy statistics that support effective enrollment strategies for underserved and HTR populations are underreported. This study highlights how a recruitment strategy that uses email in combination with follow-up, mostly phone calls and email reminders, produced a higher-than-expected enrollment rate that includes a diversity of participants from rural, Appalachian populations in older age brackets and reports recruitment and demographic statistics within a subset of HTR populations. OBJECTIVE: This study aims to provide evidence that a recruitment strategy that uses a combination of email, telephonic, and follow-up recruitment strategies increases recruitment rates in various HTR populations, specifically in rural, older, and Appalachian populations. METHODS: We evaluated the overall enrollment rate of 1 recruitment arm of a larger study that aims to understand the relationship between genetics and substance use disorders. We evaluated the enrolled population's characteristics to determine recruitment success of a combined email and follow-up recruitment strategy, and the enrollment rate of HTR populations. These characteristics included (1) enrollment rate before versus after follow-up; (2) zip code and county of enrollee to determine rural or urban and Appalachian status; (3) age to verify recruitment in all eligible age brackets; and (4) sex distribution among age brackets and rural or urban status. RESULTS: The email and follow-up arm of the study had a 17.4% enrollment rate. Of the enrolled participants, 76.3% (4602/6030) lived in rural counties and 23.7% (1428/6030) lived in urban counties in Pennsylvania. In addition, of patients enrolled, 98.7% (5956/6030) were from Appalachian counties and 1.3% (76/6030) were from non-Appalachian counties. Patients from rural Appalachia made up 76.2% (4603/6030) of the total rural population. Enrolled patients represented all eligible age brackets from ages 20 to 75 years, with the 60-70 years age bracket having the most enrollees. Females made up 72.5% (4371/6030) of the enrolled population and males made up 27.5% (1659/6030) of the population. CONCLUSIONS: Results indicate that a web-based recruitment method with participant follow-up, such as a phone call and email follow-up, increases enrollment numbers more than web-based methods alone for rural, Appalachian, and older populations. Adding a humanizing component, such as a live person phone call, may be a key element needed to establish trust and encourage patients from underserved and rural areas to enroll in studies via web-based recruitment methods. Supporting statistics on this recruitment strategy should help researchers identify whether this strategy may be useful in future studies and HTR populations.

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.

Medical Marijuana Documentation Practices in Patient Electronic Health Records: A Retrospective Observational Study Using Smart Data Elements and Chart Review.

BACKGROUND: Medical Marijuana (MMJ) is available in Pennsylvania (PA) and participation in the state-regulated program requires a patient to register and receive certification by an approved physician. There is currently no integration of MMJ certification data in PA into health records that would allow for clinicians to rapidly identify patients that are using MMJ, as there are with other scheduled drugs. This absence of a formal data sharing structure necessitates tools that aid in consistent documentation practices to enable comprehensive patient care. Customized smart data elements (SDEs) were made available to clinicians at an integrated health system, Geisinger, following MMJ legalization in PA. OBJECTIVE: The purpose of this project was to examine and contextualize the use of MMJ SDEs in the Geisinger population. We accomplished this goal by developing a systematic chart review protocol, with the goal of creating a tool that resulted in consistent human data extraction. METHODS: We developed a chart review protocol for extracting MMJ-related information. The protocol was developed between August to December of 2022 and focused on a patient group that received one of several MMJ SDEs between 1/25/2019 and 5/26/2022. Characteristics were first identified on a small pilot sample of patients (n=5), which were then iteratively reviewed to optimize for consistency. Following the pilot, two reviewers were assigned 200 patient charts, selected randomly from the larger cohort, with a third reviewer examining a subsample (n=30) to determine reliability. We then summarized the clinician-level and patient-level features from 156 charts with a table-format SDE that best captured MMJ information. RESULTS: We found the chart review protocol was feasible for those with minimal medical background to complete, with high inter-rater reliability (Kappa = .966 (P<.001), 95% CI (.954 - .978)). MMJ certification was largely documented by nurses and medical assistants (88.5%) and typically within primary care settings (68.6%). The SDE has six pre-set field prompts with heterogeneous documentation completion rates, including certifying conditions (93.6%), product (92.9%), authorized dispensary (87.8%), active ingredient (83.3%), certifying provider (61.5%), and dosage (30.8%). We found pre-set fields were overall well-recorded (76.6% across all fields). Primary diagnostic codes recorded at documentation encounters varied, with the most frequent being routine exams and testing (21.8%), musculoskeletal/nervous conditions (13.5%), and signs and symptoms not classified elsewhere (13.5%). CONCLUSIONS: This method of chart review yields high quality data extraction that can serve as a model for other health record inquiries. Our evaluation showed relatively high completeness of SDE fields, primarily by clinical staff responsible for rooming patients. Additional data captured presents an overview of the conditions under which MMJ is currently being documented. Improving adoption and fidelity of SDE data collection may present a valuable data source for future research on patient MMJ use, treatment efficacy, and outcomes.

An emerging multi-omic understanding of the genetics of opioid addiction.

Opioid misuse, addiction, and associated overdose deaths remain global public health crises. Despite the tremendous need for pharmacological treatments, current options are limited in number, use, and effectiveness. Fundamental leaps forward in our understanding of the biology driving opioid addiction are needed to guide development of more effective medication-assisted therapies. This Review focuses on the omics-identified biological features associated with opioid addiction. Recent GWAS have begun to identify robust genetic associations, including variants in OPRM1, FURIN, and the gene cluster SCAI/PPP6C/RABEPK. An increasing number of omics studies of postmortem human brain tissue examining biological features (e.g., histone modification and gene expression) across different brain regions have identified broad gene dysregulation associated with overdose death among opioid misusers. Drawn together by meta-analysis and multi-omic systems biology, and informed by model organism studies, key biological pathways enriched for opioid addiction-associated genes are emerging, which include specific receptors (e.g., GABAB receptors, GPCR, and Trk) linked to signaling pathways (e.g., Trk, ERK/MAPK, orexin) that are associated with synaptic plasticity and neuronal signaling. Studies leveraging the agnostic discovery power of omics and placing it within the context of functional neurobiology will propel us toward much-needed, field-changing breakthroughs, including identification of actionable targets for drug development to treat this devastating brain disease.

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.

Hearing loss in older adults affects neural systems supporting speech comprehension.

Hearing loss is one of the most common complaints in adults over the age of 60 and a major contributor to difficulties in speech comprehension. To examine the effects of hearing ability on the neural processes supporting spoken language processing in humans, we used functional magnetic resonance imaging to monitor brain activity while older adults with age-normal hearing listened to sentences that varied in their linguistic demands. Individual differences in hearing ability predicted the degree of language-driven neural recruitment during auditory sentence comprehension in bilateral superior temporal gyri (including primary auditory cortex), thalamus, and brainstem. In a second experiment, we examined the relationship of hearing ability to cortical structural integrity using voxel-based morphometry, demonstrating a significant linear relationship between hearing ability and gray matter volume in primary auditory cortex. Together, these results suggest that even moderate declines in peripheral auditory acuity lead to a systematic downregulation of neural activity during the processing of higher-level aspects of speech, and may also contribute to loss of gray matter volume in primary auditory cortex. More generally, these findings support a resource-allocation framework in which individual differences in sensory ability help define the degree to which brain regions are recruited in service of a particular task.

Variability and concordance of sulcal patterns in the orbitofrontal cortex: A twin study.

Sulcogyral patterns have been identified in the orbitofrontal cortex (OFC) based on the continuity of the medial and lateral orbital sulci. Pattern types are named according to their frequency in the population, with Type I present in ∼60%, Type II in ∼25%, Type III in ∼10%, and Type IV in ∼5%. Previous work has demonstrated that psychiatric conditions with high estimated heritability (e.g. schizophrenia, bipolar disorder) are associated with reduced frequency of Type I patterns, but the general heritability of the OFC sulcogyral patterns is unknown. We examined concordance of OFC patterns in 304 monozygotic (MZ) twins relative to 172 dizygotic (DZ) twins using structural magnetic resonance imaging data. We find that the frequency of pattern types within MZ and DZ twins are similar and bilateral concordance rates across all pattern types in DZ twins were 14% and 21% for MZ twins. Results from follow-up analyses confirm that continuity in the rostral-caudal direction is an important source of variability within the OFC, and subtype analyses indicate that variability is present in other sulci that are not represented by overall OFC pattern type. Overall, these results suggest that OFC sulcogyral patterns may reflect important variance that is not genetic in origin.

Classifying Characteristics of Opioid Use Disorder From Hospital Discharge Summaries Using Natural Language Processing.

Background: Opioid use disorder (OUD) is underdiagnosed in health system settings, limiting research on OUD using electronic health records (EHRs). Medical encounter notes can enrich structured EHR data with documented signs and symptoms of OUD and social risks and behaviors. To capture this information at scale, natural language processing (NLP) tools must be developed and evaluated. We developed and applied an annotation schema to deeply characterize OUD and related clinical, behavioral, and environmental factors, and automated the annotation schema using machine learning and deep learning-based approaches. Methods: Using the MIMIC-III Critical Care Database, we queried hospital discharge summaries of patients with International Classification of Diseases (ICD-9) OUD diagnostic codes. We developed an annotation schema to characterize problematic opioid use, identify individuals with potential OUD, and provide psychosocial context. Two annotators reviewed discharge summaries from 100 patients. We randomly sampled patients with their associated annotated sentences and divided them into training (66 patients; 2,127 annotated sentences) and testing (29 patients; 1,149 annotated sentences) sets. We used the training set to generate features, employing three NLP algorithms/knowledge sources. We trained and tested prediction models for classification with a traditional machine learner (logistic regression) and deep learning approach (Autogluon based on ELECTRA's replaced token detection model). We applied a five-fold cross-validation approach to reduce bias in performance estimates. Results: The resulting annotation schema contained 32 classes. We achieved moderate inter-annotator agreement, with F1-scores across all classes increasing from 48 to 66%. Five classes had a sufficient number of annotations for automation; of these, we observed consistently high performance (F1-scores) across training and testing sets for drug screening (training: 91-96; testing: 91-94) and opioid type (training: 86-96; testing: 86-99). Performance dropped from training and to testing sets for other drug use (training: 52-65; testing: 40-48), pain management (training: 72-78; testing: 61-78) and psychiatric (training: 73-80; testing: 72). Autogluon achieved the highest performance. Conclusion: This pilot study demonstrated that rich information regarding problematic opioid use can be manually identified by annotators. However, more training samples and features would improve our ability to reliably identify less common classes from clinical text, including text from outpatient settings.

Higher scores on autonomic symptom scales in pediatric patients with neurodevelopmental disorders of known genetic etiology.

INTRODUCTION: Features of underlying autonomic dysfunction, including sleep disturbances, gastrointestinal problems, and atypical heart rate, have been reported in neurodevelopmental conditions, including autism spectrum disorder (ASD). The current cross-sectional, between-groups study aimed to quantify symptoms of autonomic dysfunction in a neurodevelopmental pediatric cohort characterized by clinical diagnoses as well as genetic etiology. METHOD: The Pediatric Autonomic Symptom Scales (PASS) questionnaire was used to assess autonomic features across a group of patients with clinical neurodevelopmental diagnoses (NPD; N = 90) and genetic etiologies. Patients were subdivided based on either having a clinical ASD diagnosis (NPD-ASD; n = 37) or other non-ASD neurodevelopmental diagnoses, such as intellectual disability without ASD, speech and language disorders, and/or attention deficit hyperactivity disorder (NPD-OTHER; n = 53). Analyses focused on characterizing differences between the NPD group compared to previously published reference samples, as well as differences between the two NPD subgroups (NPD-ASD and NPD-OTHER). RESULTS: Our results indicate higher PASS scores in our NPD cohort relative to children with and without ASD from a previously published cohort. However, we did not identify significant group differences between our NPD-ASD and NPD-OTHER subgroups. Furthermore, we find a significant relationship between quantitative ASD traits and symptoms of autonomic function. CONCLUSION: This work demonstrates the utility of capturing quantitative estimates of autonomic trait dimensions that may be significantly linked with psychosocial impairments and other core clinical features of ASD.

Positive associations between cannabis and alcohol use polygenic risk scores and phenotypic opioid misuse among African-Americans.

BACKGROUND: This study examined whether polygenic risk scores (PRS) for lifetime cannabis and alcohol use were associated with misusing opioids, and whether sex differences existed in these relations in an urban, African-American sample. METHODS: Data were drawn from three cohorts of participants (N = 1,103; 45% male) who were recruited in first grade as part of a series of elementary school-based, universal preventive intervention trials conducted in a Mid-Atlantic region of the U.S. In young adulthood, participants provided a DNA sample and reported on whether they had used heroin or misused prescription opioids in their lifetime. Three substance use PRS were computed based on prior GWAS: lifetime cannabis use from Pasman et al. (2018), heavy drinking indexed via maximum number of drinks from Gelernter et al. (2019), and alcohol consumption from Kranzler et al. (2019). RESULTS: Higher PRS for lifetime cannabis use, greater heavy drinking, and greater alcohol consumption were associated with heightened risk for misusing opioids among the whole sample. Significant sex by PRS interactions were also observed such that higher PRS for heavy drinking and alcohol consumption were associated with a greater likelihood of opioid misuse among males, but not females. CONCLUSION: Our findings further elucidate the genetic contributions to misusing opioids by showing that the genetics of cannabis and alcohol consumption are associated with lifetime opioid misuse among young adults, though replication of our findings is needed.

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

Introduction: Opioid use disorders (OUDs) constitute a major public health issue, and we urgently need alternative methods for characterizing risk for OUD. Electronic health records (EHRs) 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.