Rapid opioid overdose response system technologies

Purpose of review  Opioid overdose events are a time sensitive medical emergency, which is often reversible with naloxone administration if detected in time. Many countries are facing rising opioid overdose deaths and have been implementing rapid opioid overdose response Systems (ROORS). We describe how technology is increasingly being used in ROORS design, implementation and delivery. Recent findings  Technology can contribute in significant ways to ROORS design, implementation, and delivery. Artificial intelligence-based modelling and simulations alongside wastewater-based epidemiology can be used to inform policy decisions around naloxone access laws and effective naloxone distribution strategies. Data linkage and machine learning projects can support service delivery organizations to mobilize and distribute community resources in support of ROORS. Digital phenotyping is an advancement in data linkage and machine learning projects, potentially leading to precision overdose responses. At the coalface, opioid overdose detection devices through fixed location or wearable sensors, improved connectivity, smartphone applications and drone-based emergency naloxone delivery all have a role in improving outcomes from opioid overdose. Data driven technologies also have an important role in empowering community responses to opioid overdose. Summary  This review highlights the importance of technology applied to every aspect of ROORS. Key areas of development include the need to protect marginalized groups from algorithmic bias, a better understanding of individual overdose trajectories and new reversal agents and improved drug delivery methods.PostprintPeer reviewe

Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data

Background Accurate identification of opioid overdose (OOD) cases in electronic healthcare record (EHR) data is an important element in surveillance, empirical research, and clinical intervention. We sought to improve existing OOD electronic phenotypes by incorporating new data types beyond diagnostic codes and by applying several statistical and machine learning methods. Materials and Methods We developed an EHR dataset of emergency department visits involving OOD cases or patients considered at risk for an OOD and ascertained true OOD status through manual chart reviews. We developed and validated prediction models using Random Forest, Extreme Gradient Boost, and Elastic Net models that incorporated 717 features involving primary and second diagnoses, chief complaints, medications prescribed, vital signs, laboratory results, and procedural codes. We also developed models limited to single data types. Results A total of 1718 records involving 1485 patients were manually reviewed; 541 (36.4%) patients had one or more OOD. Prediction performance was similar for all models; sensitivity varied from 94% to 97%; and area under the receiver operating characteristic curve (AUC) was 98% for all methods. The primary diagnosis and chief complaint were the most important contributors to AUC performance; primary diagnoses and medication class contributed most to sensitivity; chief complaint, primary diagnosis, and vital signs were most important for specificity. Models limited to decision support data types available in real time demonstrated robust prediction performance. Conclusions Substantial prediction performance improvements were demonstrated for identifying OODs in EHR data. Our e-phenotypes could be applied in surveillance, retrospective empirical applications, or clinical decision support systems

Opioid Use Disorder Prediction Using Machine Learning of fMRI Data

According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical analysis of functional magnetic resonance imaging (fMRI) methods to analyze the neurobiology of Opioid addictions in humans. In this work, for the first time in the literature, we propose a machine learning (ML) framework to predict OUD users utilizing clinical fMRI-BOLD (Blood oxygen level dependent) signal from OUD users and healthy controls (HC). We first obtain the features and validate these with those extracted from selected brain subcortical areas identified in our previous statistical analysis of the fMRI-BOLD signal discriminating OUD subjects from that of the HC. The selected features from three representative brain areas such as default mode network (DMN), salience network (SN), and executive control network (ECN) for both OUD participants and HC subjects are then processed for OUD and HC subjects’ prediction. Our leave one out cross validated results with sixty-nine OUD and HC cases show 88.40% prediction accuracies. These results suggest that the proposed techniques may be utilized to gain a greater understanding of the neurobiology of OUD leading to novel therapeutic development

The Feasibility and Utility of Harnessing Digital Health to Understand Clinical Trajectories in Medication Treatment for Opioid Use Disorder: D-TECT Study Design and Methodological Considerations

Introduction: Across the U.S., the prevalence of opioid use disorder (OUD) and the rates of opioid overdoses have risen precipitously in recent years. Several effective medications for OUD (MOUD) exist and have been shown to be life-saving. A large volume of research has identified a confluence of factors that predict attrition and continued substance use during substance use disorder treatment. However, much of this literature has examined a small set of potential moderators or mediators of outcomes in MOUD treatment and may lead to over-simplified accounts of treatment non-adherence. Digital health methodologies offer great promise for capturing intensive, longitudinal ecologically-valid data from individuals in MOUD treatment to extend our understanding of factors that impact treatment engagement and outcomes. Methods: This paper describes the protocol (including the study design and methodological considerations) from a novel study supported by the National Drug Abuse Treatment Clinical Trials Network at the National Institute on Drug Abuse (NIDA). This study (D-TECT) primarily seeks to evaluate the feasibility of collecting ecological momentary assessment (EMA), smartphone and smartwatch sensor data, and social media data among patients in outpatient MOUD treatment. It secondarily seeks to examine the utility of EMA, digital sensing, and social media data (separately and compared to one another) in predicting MOUD treatment retention, opioid use events, and medication adherence [as captured in electronic health records (EHR) and EMA data]. To our knowledge, this is the first project to include all three sources of digitally derived data (EMA, digital sensing, and social media) in understanding the clinical trajectories of patients in MOUD treatment. These multiple data streams will allow us to understand the relative and combined utility of collecting digital data from these diverse data sources. The inclusion of EHR data allows us to focus on the utility of digital health data in predicting objectively measured clinical outcomes. Discussion: Results may be useful in elucidating novel relations between digital data sources and OUD treatment outcomes. It may also inform approaches to enhancing outcomes measurement in clinical trials by allowing for the assessment of dynamic interactions between individuals\u27 daily lives and their MOUD treatment response. Clinical Trial Registration: Identifier: NCT04535583

Genetic and environmental prediction of opioid cessation using machine learning, GWAS, and a mouse model

The United States is currently experiencing an epidemic of opioid use, use disorder, and overdose-related deaths. While studies have identified several loci that are associated with opioid use disorder (OUD) risk, the genetic basis for the ability to discontinue opioid use has not been investigated. Furthermore, very few studies have investigated the non-genetic factors that are predictive of opioid cessation or their predictive ability. In this thesis, I studied a novel phenotype–opioid cessation, defined as the time since last use of illicit opioids (1 year ago as cease) among persons meeting lifetime DSM-5 criteria for opioid use disorder (OUD). In chapter two, I identified novel genetic variants and biological pathways that potentially regulate opioid cessation success through a genome wide study, as well as genetic overlap between opioid cessation and other substance cessation traits. In chapter three, I identified multiple non-genetic risk factors specific to each racial group that are predictive of opioid cessation from the same individuals analyzed in chapter two by applying several linear and non-linear machine learning techniques to a set of more than 3,000 variables assessed by a structured psychiatric interview. Factors identified from this atheoretical approach can be grouped into opioid use activities, other drug use, health conditions, and demographics, while the predictive accuracy as high as nearly 80% was achieved. The findings from this research generated more hypotheses for future studies to reference. In chapter four, I performed differential gene expression and network analysis on mice with different oxycodone (an opioid receptor agonist)-induced behaviors and compared the significantly associated genes and network modules with top-ranked genes identified in humans. The pathway cross-talks and gene homologs identified from both species illuminate the potential molecular mechanism of opioid behaviors. In summary, this thesis utilized statistical genetics, machine learning, and a computational biology framework to address factors that are associative with opioid cessation in humans, and cross-referenced the genetic findings in a mouse model. These findings serve as references for future studies and provide a framework for personalizing the treatment of OUD

A Spatial Risk Prediction Model for Drug Overdose

Drug overdose is a leading cause of unintentional death in the United States and has contributed significantly to a decline in life expectancy from 2015 to 2018. Overdose deaths, especially from opioids, have also been recognized in recent years as a significant public health issue. To address this public health problem, this study sought to identify neighborhood-level (e.g., block group) factors associated with drug overdose and develop a spatial model using machine learning (ML) algorithms to predict the likelihood or risk of drug overdoses across South Carolina. This study included block group level socio-demographic factors and drug use variables which may influence the incidence of drug overdose. In particular, this study developed a new index of access to measure spatial access to treatment facilities and incorporated these variables to assess the relationship between drug overdose and accessibility to the treatment centers. We explored different ML algorithms (e.g., XGBoost, Random Forest) to identify optimum predictors in each category. The categories were combined into a final ensemble predictive model that addressed spatial dependency. An evaluation was conducted to validate that the final model generalized well across the different datasets and geographical areas. Results of the study identified strong neighborhood-level predictors of a drug overdose, pinpointing the most critical neighborhood-level factor(s) that place a community at risk and protect communities from developing such problems. These factors included proportion of households receiving food stamps, households with income less than $35,000, high opioid prescription rates, smoking accessories expenditures, and low accessibility to opioid treatment programs and hospitals. The generalized error of spatial models did not increase considerably in spatial cross-validation compared to the error estimated from normal cross-validation. Our model also outperformed the geographic weighted regression method. Our Results show that variables regarding socio-demographic factors, drug use variables, and protective resources can assist in spatial drug overdose prediction. Our finding highlights several specific pathways toward community-level intervention targeted to a vulnerable population facing potentially high burdens of drug abuse and overdose

Pharmacogenetics and Tramadol-Related Fatalities

Tramadol (TR) is a widely prescribed pain killer because of its relatively safe profile among opioids. Nevertheless, intoxication can occur and overdose can lead to fatal outcomes. Surprisingly, in some fatalities for which death is attributable to TR alone, postmortem blood concentration levels overlap with the therapeutic concentration range. These fatal cases might be explained by pharmacokinetic and pharmacodynamic properties of TR that are known to be both enantioselective and influenced by genes. Indeed pharmacogenetics (PG) is of great importance in this issue as it has the ability to elucidate the genetic variation contributing to drug absorption, distribution, metabolism, excretion, and response so that adverse drug reactions, toxicity, and even death can be avoided. The aim of this chapter is to present this issue

Addressing Ascertainment Bias in the Study of Cardiovascular Disease Burden in Opioid Use Disorders - Application of Natural Language Processing of Electronic Health Records

In the United States, the prevalence of long-term exposure to opioid drugs, for both medically and nonmedically indicated purposes, has increased considerably since the mid-1990’s. Concerns have emerged about the potential health effects of opioid use. There is also growing interest in other possible connections with opioid use including cardiovascular disease. Electronic health records (EHR) contain information about patient care in the form of structured codes and unstructured notes. Natural language processing (NLP) provides a tool for processing unstructured textual data in EHR clinical notes and extracts useful information for research with structured formats. The purpose of this dissertation was to 1) to summarize peer-reviewed literature on the association between non-acute opioid and cardiovascular disease (CVD) and identify the gap of this research topic; 2) to apply NLP algorithm to estimate the extent of opioid use disorder (OUD) among hospital inpatients that cannot be identified using ICD-10-CM codes; and 3) to determine the extent to which estimates of the association between OUD and CVD may be biased by misclassification of OUD cases that are not identifiable using ICD-10-CM codes. First, we conducted a scoping review of the epidemiological literature on nonacute opioid use and CVD. We summarized the current evidence about the association between NOU and CVD, and identified some open questions on this topic. Then, we developed a Natural Language Processing algorithm to identify cases of OUD in electronic healthcare records that were not assigned an ICD-10-CM code for OUD by medical records coders, but for which strong evidence of OUD exists in the unstructured clinical notes. Lastly, we estimated the association between OUD and six types of CVD, arrhythmia, myocardial infarction, stroke, heart failure, ischemic heart disease, and infective endocarditis, classifying OUD in two ways: defining OUD cases by ICD-10-CM codes alone, and using a combination of cases identified by ICD-10-CM codes and cases identified using NLP algorithm. We assessed the effect of misclassification of OUD status when using ICD-10-CM codes alone