Acute stressors and clinical characteristics differentiate death by suicide, accident, or natural causes among illicit and prescription opiate users.

BACKGROUND: Opiate misuse has reached epidemic levels. Prevention efforts depend on distinguishing opiate users from abusers. The current study compared opioid users who died by natural cases, accidents, and suicide using psychological autopsy methods. Groups were compared on substance use characteristics, treatment history, experiences of negative life events, and circumstances at the time of death. METHODS: Substance use and suicide risk were evaluated using psychological autopsy methods in 63 decedents with positive toxicology for opiates at death divided into three groups: adults dying by suicide (n = 19), accident (n = 19), and natural causes (n = 25). Groups were compared on several dependent measures, using chi-square analyses to examine categorical variables and one-way analyses of variance (ANOVA) to examine continuous variables. RESULTS: Individuals who died by suicide were similar in many ways to adults who died by an accidental overdose. However, suicide completers were more likely to have struggled with severe depression, and previously attempted suicide, whereas the accidental overdose sample was more likely to display a chronic pattern of severe drug abuse. CONCLUSIONS: The current study helps to distinguish between opiate users who are at risk for death by an accidental or intentional overdose. In the ongoing opiate crisis, clinicians must understand the risk of overdose and the nuances of accidental behaviors compared to purposeful ones. Signs of suicidal planning, relevant psychopathology, and ongoing life stress may be useful points of intervention for stopping the increasing number of deaths among opiate users.

Understanding Opioid Use Disorder (OUD) using tree-based classifiers.

BACKGROUND: Opioid Use Disorder (OUD), defined as a physical or psychological reliance on opioids, is a public health epidemic. Identifying adults likely to develop OUD can help public health officials in planning effective intervention strategies. The aim of this paper is to develop a machine learning approach to predict adults at risk for OUD and to identify interactions between various characteristics that increase this risk. METHODS: In this approach, a data set was curated using the responses from the 2016 edition of the National Survey on Drug Use and Health (NSDUH). Using this data set, tree-based classifiers (decision tree and random forest) were trained, while employing downsampling to handle class imbalance. Predictions from the tree-based classifiers were also compared to the results from a logistic regression model. The results from the three classifiers were then interpreted synergistically to highlight individual characteristics and their interplay that pose a risk for OUD. RESULTS: Random forest predicted adults at risk for OUD with remarkable accuracy, with the average area under the Receiver-Operating-Characteristics curve (AUC) over 0.89, even though the prevalence of OUD was only about 1 %. It showed a slight improvement over logistic regression. Logistic regression identified statistically significant characteristics, while random forest ranked the predictors in order of their contribution to OUD prediction. Early initiation of marijuana (before 18 years) emerged as the dominant predictor. Decision trees revealed that early marijuana initiation especially increased the risk if individuals: (i) were between 18-34 years of age, or (ii) had incomes less than $49,000, or (iii) were of Hispanic and White heritage, or (iv) were on probation, or (v) lived in neighborhoods with easy access to drugs. CONCLUSIONS: Machine learning can accurately predict adults at risk for OUD, and identify interactions among the factors that pronounce this risk. Curbing early initiation of marijuana may be an effective prevention strategy against opioid addiction, especially in high risk groups.

The Substance Use Intervention Team: A Preliminary Analysis of a Population-level Strategy to Address the Opioid Crisis at an Academic Health Center.

OBJECTIVE: In 2017, an academic health center in Chicago launched the multidisciplinary Substance Use Intervention Team (SUIT) to address opioid misuse across 18 inpatient units and in a new outpatient addiction medicine clinic. This report assesses the first 5 months of implementation and associations with patient health and healthcare utilization. METHODS: Patient demographic and screening data were extracted from the administrative data warehouse of the electronic health record (EHR) infrastructure. Distribution of sample characteristics for positive initial screens for opioid misuse was tested against those of all patients screened using a 2-tailed test of proportions (P < 0.05). A second analysis compared length of stay and 30-day readmissions within a cohort of patients with a secondary diagnosis of substance use disorder. RESULTS: Between November, 2017 and March, 2018, 76% of 15,054 unique patients were screened, 578 had positive scores on the Alcohol Use Disorders Identification Test and Drug Abuse Screening Test, 131 had positive scores for opioid misuse, and 52 patients initiated medication treatment. Patients with a secondary diagnosis of substance use disorder who received a SUIT consult (n = 161), compared with those who did not (n = 612), had a shorter average length of stay (5.91 vs 6.73 days) and lower 30-day readmission rate (13.6% vs 15.7%). CONCLUSION: Leveraging the EHR to conduct standardized screenings and treatment has helped identify an at-risk population-disproportionately younger, black, and male-and treat new cases of opioid and substance misuse. The intervention indicates trends toward a shortened length of stay, reduced 30-day readmissions, and has linked patients to outpatient care.

Subtypes in patients with opioid misuse: A prognostic enrichment strategy using electronic health record data in hospitalized patients.

BACKGROUND: Approaches are needed to better delineate the continuum of opioid misuse that occurs in hospitalized patients. A prognostic enrichment strategy with latent class analysis (LCA) may facilitate treatment strategies in subtypes of opioid misuse. We aim to identify subtypes of patients with opioid misuse and examine the distinctions between the subtypes by examining patient characteristics, topic models from clinical notes, and clinical outcomes. METHODS: This was an observational study of inpatient hospitalizations at a tertiary care center between 2007 and 2017. Patients with opioid misuse were identified using an operational definition applied to all inpatient encounters. LCA with eight class-defining variables from the electronic health record (EHR) was applied to identify subtypes in the cohort of patients with opioid misuse. Comparisons between subtypes were made using the following approaches: (1) descriptive statistics on patient characteristics and healthcare utilization using EHR data and census-level data; (2) topic models with natural language processing (NLP) from clinical notes; (3) association with hospital outcomes. FINDINGS: The analysis cohort was 6,224 (2.7% of all hospitalizations) patient encounters with opioid misuse with a data corpus of 422,147 clinical notes. LCA identified four subtypes with differing patient characteristics, topics from the clinical notes, and hospital outcomes. Class 1 was categorized by high hospital utilization with known opioid-related conditions (36.5%); Class 2 included patients with illicit use, low socioeconomic status, and psychoses (12.8%); Class 3 contained patients with alcohol use disorders with complications (39.2%); and class 4 consisted of those with low hospital utilization and incidental opioid misuse (11.5%). The following hospital outcomes were the highest for each subtype when compared against the other subtypes: readmission for class 1 (13.9% vs. 10.5%, p<0.01); discharge against medical advice for class 2 (12.3% vs. 5.3%, p<0.01); and in-hospital death for classes 3 and 4 (3.2% vs. 1.9%, p<0.01). CONCLUSIONS: A 4-class latent model was the most parsimonious model that defined clinically interpretable and relevant subtypes for opioid misuse. Distinct subtypes were delineated after examining multiple domains of EHR data and applying methods in artificial intelligence. The approach with LCA and readily available class-defining substance use variables from the EHR may be applied as a prognostic enrichment strategy for targeted interventions.

Comorbidity Patterns Among Patients With Opioid Use Disorder and Problem Gambling: ADHD Status Predicts Class Membership.

Objective: Psychiatric comorbidities are highly prevalent among individuals affected by substance use disorders and those with non-substance-related addictive disorders such as gambling disorder. More recently, the frequent co-occurrence of substance use disorders and attention-deficit hyperactivity disorder (ADHD) has received particular attention. The aim of our study was to identify patterns of psychiatric comorbidity and to examine associations between patient group and ADHD status with class membership. Methods: Participants were patients with opioid use disorder enrolled in opioid maintenance treatment (OMT), either recruited from the community (n = 142; M age = 35.8 years; 38.7% female) or prison (n = 133; M age = 35.7 years; 21.8% female), and patients undergoing treatment for problem gambling (PrG; n = 80; M age = 43.1 years; 20% female). To enable direct comparisons, the following instruments were applied: Mini International Neuropsychiatric Interview, Adult ADHD self-report scale, Wender Utah Rating Scale, and European Addiction Severity Index. We used a latent class analysis (LCA) to identify psychiatric comorbidity patterns and a multinomial logistic regression to examine associations between patient group, ADHD status, age, and gender with class membership. Results: The LCA resulted in a three-class solution: (1) a class of individuals with a relatively low probability of current psychiatric comorbidities, except for a high probability of substance use disorders; (2) a class with markedly increased probabilities of current and recurrent psychiatric comorbidities, especially for major depression; and (3) a class with very low probabilities of psychiatric comorbidities, except for moderate probabilities of substance use disorders and antisocial personality disorder. Both OMT patients recruited from the community and those in prison were less likely than PrG patients to be assigned to the most burdened class with respect to psychiatric comorbidity (class 2). Further, both individuals with ADHD in childhood and those with adult ADHD were more likely members of class 2. Conclusions: PrG patients seem to be at an even higher risk for psychiatric comorbidities compared to OMT patients. Raising awareness among practitioners for the high prevalence of psychiatric comorbidities among patients with gambling disorder and individuals with ADHD is crucial to initiate adequate treatment and to improve response.

Deep Learning Solutions for Classifying Patients on Opioid Use.

Opioid analgesics, as commonly prescribed medications used for relieving pain in patients, are especially prevalent in US these years. However, an increasing amount of opioid misuse and abuse have caused lots of consequences. Researchers and clinicians have attempted to discover the factors leading to opioid long-term use, dependence, and abuse, but only limited incidents are understood from previous works. Motivated by recent successes of deep learning and the abundant amount of electronic health records, we apply state-of-the-art deep and recurrent neural network models on a dataset of more than one hundred thousand opioid users. Our models are shown to achieve robust and superior results on classifying opioid users, and are able to extract key factors for different opioid user groups. This work is also a good demonstration on adopting novel deep learning methods for real-world health care problems.

Ranking the harm of non-medically used prescription opioids in the UK.

A panel of nine experts applied multi-criteria decision analysis (MCDA) to determine the relative overall harm to users and harms to others of street heroin (injected and smoked) and eleven non-medically used prescription opioids. The experts assessed harm scores for each of the 13 opioids on each of 20 harm criteria, weighted the criteria and explored the resulting weighted harm scores for each opioid. Both forms of heroin scored very high: overall harm score of 99 for injected heroin and 72 for smoked heroin on a scale of 0-100. The main feature that distinguishes both forms of street heroin use is that their harm to others is more than five times that of the other eleven opioids. The overall harm score of fentanyl (including injection of fentanyl extracted from patches) and diamorphine (medically prescribed form of heroin) was 54 and 51, respectively, whereas that of orally used opioids ranged from 32 (pethidine) to 11 (codeine-containing pharmaceuticals). Injected street heroin, fentanyl and diamorphine emerged as most harmful to users, with the latter two very low in harm to others. Pethidine, methadone, morphine and oxycodone are also low in harm to others, while moderate in harm to users. We conclude that the overall harms of non-medically used prescription opioids are less than half that of injected street heroin. These data may give a basis for precautionary regulatory measures that should be considered if the rising trend in non-medical use of prescription opioids were to become evident in the UK.

Latent class analysis of DSM-5 criteria for opioid use disorders: results from the Iranian National Survey on Mental Health.

BACKGROUND: Assessments of DSM-IV and DSM-5 criteria with sample populations of opioid users are limited. This study aimed to determine the number of latent classes in opioid users and assessment of the proposed revisions to the DSM-5 opioid use disorder (OUD) criteria. METHODS: Data came from the 2011 Iranian National Mental Health Survey (IranMHS) on 7,886 participants aged 15-64 years living in Iran. We used the Composite International Diagnostic Interview (CIDI) version 2.1 in all respondents who indicated using opioids at least 5 times in the previous 12 months (n = 236). RESULTS: A three-class model provided the best fit of all the models tested. Classes showed a spectrum of severity that was compatible with the DSM-5 classification. 'Legal problems' and 'desire to cut down' showed poor discrimination between classes. The weighted prevalence of OUD using DSM-5 was 20.7% higher than with DSM-IV. CONCLUSIONS: RESULTS support the grouping based on severity of symptoms, combining abuse and dependence into a single diagnosis, omitting legal problems, and addition of craving as a new criterion.

Uniform standards and case definitions for classifying opioid-related deaths: recommendations by a SAMHSA consensus panel.

Deaths involving prescription and illicit opioids are on the rise, which is an issue of increasing concern to health care professionals, policymakers, and the public. However, because medical examiners, coroners, and other practitioners do not use uniform standards and case definitions in classifying such drug-related deaths, the incidence and prevalence data are challenging to analyze and difficult to interpret, and thus form a poor basis for crafting effective responses. To address this situation, the Substance Abuse and Mental Health Services Administration convened a Consensus Panel and charged it with devising uniform standards and case definitions that can assist medical examiners, coroners, public health officials, and others in consistently distinguishing between deaths that were caused by a certain opioids and deaths in which such a drug was detected but was not a major cause of or contributor to the death. The consensus statement presented here incorporates the panel's recommendations in four key areas.

Classification and definition of misuse, abuse, and related events in clinical trials: ACTTION systematic review and recommendations.

As the nontherapeutic use of prescription medications escalates, serious associated consequences have also increased. This makes it essential to estimate misuse, abuse, and related events (MAREs) in the development and postmarketing adverse event surveillance and monitoring of prescription drugs accurately. However, classifications and definitions to describe prescription drug MAREs differ depending on the purpose of the classification system, may apply to single events or ongoing patterns of inappropriate use, and are not standardized or systematically employed, thereby complicating the ability to assess MARE occurrence adequately. In a systematic review of existing prescription drug MARE terminology and definitions from consensus efforts, review articles, and major institutions and agencies, MARE terms were often defined inconsistently or idiosyncratically, or had definitions that overlapped with other MARE terms. The Analgesic, Anesthetic, and Addiction Clinical Trials, Translations, Innovations, Opportunities, and Networks (ACTTION) public-private partnership convened an expert panel to develop mutually exclusive and exhaustive consensus classifications and definitions of MAREs occurring in clinical trials of analgesic medications to increase accuracy and consistency in characterizing their occurrence and prevalence in clinical trials. The proposed ACTTION classifications and definitions are designed as a first step in a system to adjudicate MAREs that occur in analgesic clinical trials and postmarketing adverse event surveillance and monitoring, which can be used in conjunction with other methods of assessing a treatment's abuse potential.

Nonmedical use of prescription opioids among adolescents: subtypes based on motivation for use.

The purpose of this study was to advance our understanding of nonmedical use of prescription medications by identifying the distinguishing characteristics of 2 subtypes of adolescent nonmedical users of prescription opioids that have been previously described. A Web-based, self-administered survey was completed by 2,597 7th-12th grade students. Sensation-seeking nonmedical users were best characterized by rule breaking and aggressive behaviors and possible substance dependence. Medical users and nonmedical self-treating users were best characterized by somatic complaints, anxiety and depressive symptoms, and history of sexual victimization.

[Opiate dependence type II or antisocial: Cloninger's Psychobiological Model and its usefullness in addictions]. / Dependencia de Opiáceos tipo II o antisocial: utilidad del Modelo Psicobiológico de Cloninger en adicciones.

The aim was to analyze the relationship between Cloninger's dimensions and Personality Disorders (PD) (with DSM-IV criteria) in opiate dependents. The study was Cross-sectional. The sampling of 196 patients with opiate dependence was consecutive. All were receiving treatment in an inpatient detoxification unit. Cloninger's Temperament and Character Inventory (TCI), International Personality Disorders Examination (IPDE) and a Substance Use Questionnaire were used. Character's dimensions as Self-directness and Cooperation were related with PD when scored low. Opposite to Cloninger descriptions, high scores of Self-transcendence were related with presence of PD. Related to temperamental dimensions, cluster A was related with low scores of Reward Dependence (RD) and cluster C with high scores of Harm Avoidance (HA). Otherwise, in cluster B, while Borderline PD had high scores of Novelty Seeking (as high HA), the Antisocial PD only were related to low scores of RD. RD dimension seems useful to differ from presence or absence of Antisocial PD, also when alcohol consumption is considered. Cloninger's Model of Personality is useful in drug dependents for the definition of the different PD, as well as for probable PD's aggregation. This model also helps to create subtypes in opiate dependents as the antisocial or type II.

Empirically derived subtypes of opioid use and related behaviors.

AIMS: To identify and validate homogeneous subtypes of opioid use and related behaviors. DESIGN: Family-based and case-control genetic studies of opioid and/or cocaine dependence. SETTINGS: Clinical and general community samples from Connecticut, Massachusetts, Pennsylvania and South Carolina. PARTICIPANTS: A total of 4061 individuals (2003 individuals from 835 families and 2058 unrelated individuals) recruited to participate in genetic studies. MEASUREMENTS: The computer-assisted Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA) was used to assess participants' demographics, medical history, substance use behaviors and disorders and other psychiatric disorders. FINDINGS: Five homogeneous subtypes were identified, which differed on opioid-related measures, demographics and prevalence rates of substance use and psychiatric disorders. Heritability estimates for the two most severely affected subtypes exceeded 0.60. CONCLUSIONS: An empirical approach based on opioid use and related behaviors can yield homogeneous subtypes that could be of value in gene finding for opioid dependence.

Abuse and dependence on prescription opioids in adults: a mixture categorical and dimensional approach to diagnostic classification.

BACKGROUND: For the emerging DSM-V, it has been recommended that dimensional and categorical methods be used simultaneously in diagnostic classification; however, little is known about this combined approach for abuse and dependence. METHOD: Using data (n=37 708) from the 2007 National Survey on Drug Use and Health (NSDUH), DSM-IV criteria for prescription opioid abuse and dependence among non-prescribed opioid users (n=3037) were examined using factor analysis (FA), latent class analysis (LCA, categorical), item response theory (IRT, dimensional), and factor mixture (hybrid) approaches. RESULTS: A two-class factor mixture model (FMM) combining features of categorical latent classes and dimensional IRT estimates empirically fitted more parsimoniously to abuse and dependence criteria data than models from FA, LCA and IRT procedures respectively. This mixture model included a severely affected group (7%) with a comparatively moderate to high probability (0.32-0.88) of endorsing all abuse and dependence criteria items, and a less severely affected group (93%) with a low probability (0.003-0.16) of endorsing all criteria. The two empirically defined groups differed significantly in the pattern of non-prescribed opioid use, co-morbid major depression, and substance abuse treatment use. CONCLUSIONS: A factor mixture model integrating categorical and dimensional features of classification fits better to DSM-IV criteria for prescription opioid abuse and dependence in adults than a categorical or dimensional approach. Research is needed to examine the utility of this mixture classification for substance use disorders and treatment response.

Opioid dependence latent structure: two classes with differing severity?

AIMS: To examine the structure of illicit opioid abuse and dependence within an opioid dependent sample and its relationship to other clinical variables. DESIGN, SETTING AND PARTICIPANTS: A cross-sectional study of 1511 opioid dependent individuals recruited through opioid pharmacotherapy clinics in the Sydney area, Australia. MEASUREMENTS: A face-to-face structured interview covering substance use and dependence, psychiatric history, child maltreatment, family background, adult violence and criminal history. Dimensional, latent class and factor mixture models were fit to the opioid abuse and dependence data. Classes were then compared on a range of demographic and clinical covariates. FINDINGS: A two-class, one-factor model provided the best fit of all the models tested. The two classes differed with respect to endorsement probabilities on a range of abuse and dependence criteria, and also with respect to the odds of other drug dependence diagnoses, antisocial personality disorder and non-fatal opioid overdose. Within-class severity was associated with similar variables: other drug dependence, borderline personality disorder and opioid overdose. CONCLUSION: In an in-treatment, opioid-dependent sample, there appears to be two classes of individuals exhibiting distinct patterns of abuse and dependence criteria endorsement and to differ on externalizing but not internalizing disorders. This study provides preliminary evidence that the proposed DSM-V opioid use disorder distinction between moderate and severely dependent people is valid. Class one participants were not only more severely dependent, but had greater odds for opioid overdoses, other drug dependence and antisocial personality disorder.

Approaches to improve pain relief while minimizing opioid abuse liability.

UNLABELLED: Two strategies should greatly improve pain management while minimizing opioid abuse. The first strategy involves the systematic implementation in every clinical practice of "universal precautions," a set of procedures that help physicians implement opioid therapy in a safe and controlled manner. These procedures include: 1) carefully assessing the patient's risk for opioid abuse; 2) selecting the most appropriate opioid therapy; 3) regularly monitoring the patient to evaluate the efficacy and tolerability of the treatment and to detect possible aberrant behaviors; and 4) mapping out solutions if abuse and/or addiction is detected, or in case of treatment failure. The second strategy involves the use of opioid formulations designed to deter or prevent product tampering and abuse. Results of clinical trials of new formulations of existing opioids (including oxycodone, morphine, and hydromorphone) suggest the potential for reduced abuse liability and, if approved, will be evaluated after launch for reduced real-world abuse. Integration of these formulations in clinical practices based on universal precautions should help further minimize the risk of opioid abuse while fostering appropriate prescribing to patients with indications for opioid therapy. PERSPECTIVE: Undertreated pain and prescription opioid abuse remain important public health problems. In the absence of strong empirical evidence, common sense dictates that a universal-precautions approach-a systematic and easily adopted process that clinicians can quickly put into practice-is advised to promote safe opioid prescribing. Abuse- and tamper-resistant opioid formulations are emerging tools that may enhance safe opioid prescribing; further research and postmarketing analysis will clarify their utility and role in clinical practice.

Classification and identification of opioid addiction in chronic pain patients.

Addiction is a feared consequence of long-term opioid treatment of chronic pain patients. The ICD-10 and DSM-IV diagnostic addiction criteria may not be appropriate in these patients. Therefore Portenoy's criteria (PC) were launched. The aim was to estimate the prevalence of addiction, to investigate whether PC were applicable and to compare these criteria with the ICD-10 criteria. The study was cross-sectional and included 253 patients with chronic pain at a tertiary pain centre. Patients were screened for addiction by a physician and a nurse. The addiction prevalence was 14.4% according to ICD-10 and 19.3% according to PC. A significant difference between the prevalence of addiction according to ICD-10 and to PC was found. The inter-rater reliability was 0.95 for ICD-10 and 0.93 for PC. The sensitivity of PC was 0.85 and the specificity was 0.96. According to PC patients classified as addicted were treated with significantly higher opioid doses, drank more alcohol, smoked more tobacco, used benzodiazepines and had higher levels of depression. According to ICD-10 patients classified as addicted used higher doses of opioids, drank more alcohol and had higher scores of anxiety and depression. High opioid doses, concomitant use of alcohol and younger age were risk factors. The risk profile for PC was different to ICD-10 by adding risk factors as concomitant use of benzodiazepines, having depression and low educational level. PC seems to be appropriate for diagnosing addiction in opioid treated pain patients and seems to be more sensitive and specific than ICD-10 criteria.