Has the United States Reached a Plateau in Overdoses Caused by Synthetic Opioids After the Onset of the COVID-19 Pandemic? Examination of Centers for Disease Control and Prevention Data to November 2021

Impact of the COVID-19 Pandemic on Functions of Nursing Professionals in the Care of Opioid Use Disorder: Systematic Review

BACKGROUND

Nursing professionals are vitally involved in the cascade of care for opioid use disorders (OUDs). The global spread of COVID-19 has had complex effects on public health aspects of major diseases, including OUDs. There are limited data on the major ways in which the COVID-19 pandemic has affected the functions of nursing professionals in the care of OUDs.

METHOD

This systematic review followed Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines and examined published data for trends in OUD care during the first 2 years of the COVID-19 pandemic, focusing on nursing functions. The National Library of Medicine PubMed database and the EMBASE database were examined for peer-reviewed studies with primary data published between January 1, 2020, and December 31, 2021.

REVIEW FINDINGS AND CONCLUSIONS

Rapid changes were observed in numerous aspects of OUDs during the early pandemic stage, as well as its care by nursing and other health professionals. These changes include increased overdoses (primarily from synthetic opioids such as fentanyl) and emergency department visits. These trends varied considerably across U.S. jurisdictions, underscoring the importance of region-specific examinations for public health policy and intervention. Out of necessity, healthcare systems and nursing professionals adapted to the challenges of OUD care in the pandemic. These adaptations included increases in telehealth services, increases in take-home doses of methadone or buprenorphine/naloxone, and expansion of layperson training in the use of naloxone for overdose reversal. It is likely that some of these adaptations will result in long-term changes in standards of care practices for OUDs by nursing professionals.

Overdose mortality rates for opioids and stimulant drugs are substantially higher in men than in women: state-level analysis

Drug overdoses from opioids and stimulants are a major cause of mortality in the United States. It is unclear if there are stable sex differences in overdose mortality for these drugs across states, whether these differ across the lifespan, and if so, whether they can be accounted for by different levels of drug misuse. This was a state-level analysis of epidemiological data on overdose mortality, across 10-year age bins (age range: 15-74), using the CDC WONDER platform for decedents in the United States in 2020-1. The outcome measure was rate of overdose death (per 100,000) for: synthetic opioids (e.g., fentanyl), heroin, psychostimulants with potential for misuse (e.g., methamphetamine), and cocaine. Multiple linear regressions controlled for ethnic-cultural background, household net worth, and sex-specific rate of misuse (from NSDUH, 2018-9). For all these drug categories, males had greater overall overdose mortality than females, after controlling for rates of drug misuse. The mean male/female sex ratio of mortality rate was relatively stable across jurisdictions: synthetic opioids (2.5 [95% CI, 2.4-7]), heroin, (2.9 [95% CI, 2.7-3.1], psychostimulants (2.4 [95% CI, 2.3-5]), and cocaine (2.8 [95% CI, 2.6-9]). With data stratified in 10-year age bins, the sex difference generally survived adjustment (especially in the 25-64 age range). Results indicate that males are significantly more vulnerable than females to overdose deaths caused by opioid and stimulant drugs, taking into account differing state-level environmental conditions and drug misuse levels. These results call for research into diverse biological, behavioral, and social factors that underlie sex differences in human vulnerability to drug overdose.

Neuroimmune Mechanisms of Opioid Use Disorder and Recovery: Translatability to Human Studies, and Future Research Directions

Opioid use disorder (OUD) is a major current cause of morbidity and mortality. Long-term exposure to short-acting opioids (MOP-r agonists such as heroin or fentanyl) results in complex pathophysiological changes to neuroimmune and neuroinflammatory functions, affected in part by peripheral mechanisms (e.g., cytokines in blood), and by neuroendocrine systems such as the hypothalamic-pituitary-adrenal (HPA) stress axis. There are important findings from preclinical models, but their role in the trajectory and outcomes of OUD in humans is not well understood. The goal of this narrative review is to examine available data on immune and inflammatory functions in persons with OUD, and to identify major areas for future research. Peripheral blood biomarker studies revealed a pro-inflammatory state in persons with OUD in withdrawal or early abstinence, consistent with available postmortem brain studies (which show glial activation) and diffusion tensor imaging studies (indicating white matter disruptions), with gradual abstinence-associated recovery. The mechanistic roles of these neuroimmune and neuroinflammatory changes in the trajectory of OUD (including recovery and medication management) cannot be examined practically with postmortem data. Collection of longitudinal data in larger-scale human cohorts would allow examination of these mechanisms associated with OUD stage and progression. Given the heterogeneity in presentation of OUD, a precision medicine approach integrating multi-omic peripheral biomarkers and comprehensive phenotyping, including neuroimaging, can be beneficial in risk stratification, and individually optimized selection of interventions for individuals who will benefit, and assessments under refractory therapy.

Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults

BACKGROUND

The clinical narrative in electronic health records (EHRs) carries valuable information for predictive analytics; however, its free-text form is difficult to mine and analyze for clinical decision support (CDS). Large-scale clinical natural language processing (NLP) pipelines have focused on data warehouse applications for retrospective research efforts. There remains a paucity of evidence for implementing NLP pipelines at the bedside for health care delivery.

OBJECTIVE

We aimed to detail a hospital-wide, operational pipeline to implement a real-time NLP-driven CDS tool and describe a protocol for an implementation framework with a user-centered design of the CDS tool.

METHODS

The pipeline integrated a previously trained open-source convolutional neural network model for screening opioid misuse that leveraged EHR notes mapped to standardized medical vocabularies in the Unified Medical Language System. A sample of 100 adult encounters were reviewed by a physician informaticist for silent testing of the deep learning algorithm before deployment. An end user interview survey was developed to examine the user acceptability of a best practice alert (BPA) to provide the screening results with recommendations. The planned implementation also included a human-centered design with user feedback on the BPA, an implementation framework with cost-effectiveness, and a noninferiority patient outcome analysis plan.

RESULTS

The pipeline was a reproducible workflow with a shared pseudocode for a cloud service to ingest, process, and store clinical notes as Health Level 7 messages from a major EHR vendor in an elastic cloud computing environment. Feature engineering of the notes used an open-source NLP engine, and the features were fed into the deep learning algorithm, with the results returned as a BPA in the EHR. On-site silent testing of the deep learning algorithm demonstrated a sensitivity of 93% (95% CI 66%-99%) and specificity of 92% (95% CI 84%-96%), similar to published validation studies. Before deployment, approvals were received across hospital committees for inpatient operations. Five interviews were conducted; they informed the development of an educational flyer and further modified the BPA to exclude certain patients and allow the refusal of recommendations. The longest delay in pipeline development was because of cybersecurity approvals, especially because of the exchange of protected health information between the Microsoft (Microsoft Corp) and Epic (Epic Systems Corp) cloud vendors. In silent testing, the resultant pipeline provided a BPA to the bedside within minutes of a provider entering a note in the EHR.

CONCLUSIONS

The components of the real-time NLP pipeline were detailed with open-source tools and pseudocode for other health systems to benchmark. The deployment of medical artificial intelligence systems in routine clinical care presents an important yet unfulfilled opportunity, and our protocol aimed to close the gap in the implementation of artificial intelligence-driven CDS.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05745480; https://www.clinicaltrials.gov/ct2/show/NCT05745480.

Overdose mortality rates for opioids or stimulants are higher in males than females, controlling for rates of drug misuse: State-level data

Importance

Drug overdoses from opioids like fentanyl and heroin and stimulant drugs such as methamphetamine and cocaine are a major cause of mortality in the United States, with potential sex differences across the lifespan.

Objective

To determine overdose mortality for specific drug categories across the lifespan of males and females, using a nationally representative state-level sample.

Design

State-level analyses of nationally representative epidemiological data on overdose mortality for specific drug categories, across 10-year age bins (age range: 15-74).

Setting

Population-based study of Multiple Cause of Death 2020-2021 data from the Centers of Disease Control and Prevention (CDC WONDER platform).

Participants

Decedents in the United States in 2020-2021.

Main outcome measures

The main outcome measure was sex-specific rates of overdose death (per 100,000) for: synthetic opioids excluding methadone (ICD-10 code: T40.4; predominantly fentanyl), heroin (T40.1), psychostimulants with potential for misuse, excluding cocaine (T43.6, predominantly methamphetamine; labeled "psychostimulants" hereafter), and cocaine (T40.5). Multiple regression analyses were used to control for ethnic-cultural background, household net worth, and sex-specific rate of misuse of the relevant substances (from the National Survey on Drug Use and Health, 2018-2019).

Results

For each of the drug categories assessed, males had greater overall overdose mortality than females, after controlling for rates of drug misuse. The mean male/female sex ratio of mortality rate for the separate drug categories was relatively stable across jurisdictions: synthetic opioids (2.5 [95%CI, 2.4-2.7]), heroin, (2.9 [95%CI, 2.7-3.1], psychostimulants (2.4 [95%CI, 2.3-2.5]), and cocaine (2.8 [95%CI, 2.6-2.9]). With data stratified in 10-year age bins, the sex difference generally survived adjustment for state-level ethnic-cultural and economic variables, and for sex-specific misuse of each drug type (especially for bins in the 25-64 age range). For synthetic opioids, the sex difference survived adjustment across the lifespan (i.e., 10-year age bins ranging from 15-74), including adolescence, adulthood and late adulthood.

Conclusions and Relevance

The robustly greater overdose mortality in males versus females for synthetic opioids (predominantly fentanyl), heroin, and stimulant drugs including methamphetamine and cocaine indicate that males who misuse these drugs are significantly more vulnerable to overdose deaths. These results call for research into diverse biological, behavioral, and social factors that underlie sex differences in human vulnerability to drug overdose.

The Evaluation of a Clinical Decision Support Tool Using Natural Language Processing to Screen Hospitalized Adults for Unhealthy Substance Use: Protocol for a Quasi-Experimental Design

BACKGROUND

Automated and data-driven methods for screening using natural language processing (NLP) and machine learning may replace resource-intensive manual approaches in the usual care of patients hospitalized with conditions related to unhealthy substance use. The rigorous evaluation of tools that use artificial intelligence (AI) is necessary to demonstrate effectiveness before system-wide implementation. An NLP tool to use routinely collected data in the electronic health record was previously validated for diagnostic accuracy in a retrospective study for screening unhealthy substance use. Our next step is a noninferiority design incorporated into a research protocol for clinical implementation with prospective evaluation of clinical effectiveness in a large health system.

OBJECTIVE

This study aims to provide a study protocol to evaluate health outcomes and the costs and benefits of an AI-driven automated screener compared to manual human screening for unhealthy substance use.

METHODS

A pre-post design is proposed to evaluate 12 months of manual screening followed by 12 months of automated screening across surgical and medical wards at a single medical center. The preintervention period consists of usual care with manual screening by nurses and social workers and referrals to a multidisciplinary Substance Use Intervention Team (SUIT). Facilitated by a NLP pipeline in the postintervention period, clinical notes from the first 24 hours of hospitalization will be processed and scored by a machine learning model, and the SUIT will be similarly alerted to patients who flagged positive for substance misuse. Flowsheets within the electronic health record have been updated to capture rates of interventions for the primary outcome (brief intervention/motivational interviewing, medication-assisted treatment, naloxone dispensing, and referral to outpatient care). Effectiveness in terms of patient outcomes will be determined by noninferior rates of interventions (primary outcome), as well as rates of readmission within 6 months, average time to consult, and discharge rates against medical advice (secondary outcomes) in the postintervention period by a SUIT compared to the preintervention period. A separate analysis will be performed to assess the costs and benefits to the health system by using automated screening. Changes from the pre- to postintervention period will be assessed in covariate-adjusted generalized linear mixed-effects models.

RESULTS

The study will begin in September 2022. Monthly data monitoring and Data Safety Monitoring Board reporting are scheduled every 6 months throughout the study period. We anticipate reporting final results by June 2025.

CONCLUSIONS

The use of augmented intelligence for clinical decision support is growing with an increasing number of AI tools. We provide a research protocol for prospective evaluation of an automated NLP system for screening unhealthy substance use using a noninferiority design to demonstrate comprehensive screening that may be as effective as manual screening but less costly via automated solutions.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03833804; https://clinicaltrials.gov/ct2/show/NCT03833804.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/42971.