Naloxone administration for suspected opioid overdose: An expanded scope of practice by a basic life support collegiate-based emergency medical services agency

Identification of Naloxone in Emergency Medical Services Data Substantially Improves by Processing Unstructured Patient Care Narratives

OBJECTIVES

Structured data fields, including medication fields involving naloxone, are routinely used to identify opioid overdoses in emergency medical services (EMS) data; between January 2021 and March 2024, there were approximately 1.2 million instances of naloxone administration in the United States. To improve the accuracy of naloxone reporting, we developed methodology for identifying naloxone administration using both structured fields and unstructured patient care narratives for events documented by EMS.

METHODS

We randomly sampled 30,000 records from Kentucky's state-wide EMS database during 2019. We applied regular expressions (RegEx) capable of recognizing naloxone-related text patterns in each EMS patient's case narrative. Additionally, we applied natural language processing (NLP) techniques to extract important contextual factors such as route and dosage from these narratives. We manually reviewed cases where the structured data and unstructured data disagreed and developed an aggregate indicator for naloxone administration using either structured or unstructured data for each patient case.

RESULTS

There were 437 (1.45%) records with structured documentation of naloxone. Our RegEx method identified 547 naloxone administrations in the narratives; after manual review, we determined RegEx yielded acceptable false positives (N = 31, 5.6%), false negatives (N = 23, 4.2%) and performance (precision = 0.94, recall = 0.93). In total, 552 patients had naloxone administered after combining indicators from both structured fields and verified results from unstructured narratives. The NLP approach also identified 246 (47.4%) records that specified route of administration and 358 (69.0%) records with dosage delivered.

CONCLUSIONS

An additional 115 (26.3%) patients receiving naloxone were identified by using unstructured case narratives compared to structured data. New surveillance methods that incorporate unstructured EMS narratives are critically needed to avoid substantial underestimation of naloxone utilization and enumeration of opioid overdoses.

Characteristics and outcomes of cardiac arrests reported in the national collegiate emergency medical services foundation data registry

OBJECTIVE

The objective of this study was to identify out-of-hospital cardiac arrest characteristics for patients treated by collegiate-based emergency medical services (CBEMS) organizations.

PARTICIPANTS

CBEMS organizations provided data via the National Collegiate EMS Foundation Cardiac Arrest Data Registry.

METHODS

CBEMS organization details, patient demographics, cardiac arrest characteristics and treatments, and prehospital outcomes for cases spanning October 2007 to May 2020 were analyzed with descriptive statistics.

RESULTS

There were 65 OHCA entries. The majority were for male patients (82%) and a notable number of cases occurred in patients 45 years of age or younger (41%). Cases were frequently witnessed (71%) with high rates of bystander cardiopulmonary resuscitation (57%) and defibrillation (29%) prior to EMS arrival. Almost half of the patients (48%) had achieved return of spontaneous circulation until care was transferred to a provider of equal/higher level.

CONCLUSIONS

CBEMS organizations may be well situated to respond rapidly to on-campus OHCAs.

Interventions to increase naloxone access for undergraduate students: A systematic review of the literature

OBJECTIVE

To identify and describe interventions that increase access to naloxone for undergraduate students.

METHODS

A systematic review across 4 databases identified interventions that expand access to naloxone at colleges in the United States from 2015-2023. Three reviewers extracted the following data to create a narrative synthesis and summary of program elements: setting, rationale for intervention, timeline, intervention components, study size, collaboration, sustainability, outcomes and results.

RESULTS

Seven articles met inclusion criteria. Institutions' implemented naloxone interventions due to concerns for student safety and/or student overdose fatalities. Three universities collaborated with their School of Pharmacy for program design and/or dissemination, while two partnered with state-based naloxone distribution programs. Most programs combined opioid-overdose/naloxone training; four distributed naloxone kits. Three studies included pre/post-outcomes, and all reported increases in participant knowledge, attitudes, and/or ability to respond to an overdose.

CONCLUSIONS

Our results indicates an opportunity for wide-scale implementation of undergraduate naloxone programs within US colleges. However, more rigorous implementation research is needed to identify barriers and facilitators to program feasibility, acceptability, and participation.

A promising and highly sensitive electrochemical platform for the detection of fentanyl and alfentanil in human serum

A novel electrochemical method has been developed, based on a covalent organic framework (COF) and reduced graphene oxide (rGO), to detect fentanyl and alfentanil. COF nanomaterials with chrysanthemum morphology obtained by solvothermal reaction contain rich active sites for electrochemical catalytic reaction, thus improving the detection performance of the designed sensor. Reduced graphene oxide improves the sensor's sensitivity due to enhanced electron transfer. Under optimized experimental conditions, the fabricated electrode presents a linear range of 0.02 to 7.26 μM for alfentanil and 0.1 to 6.54 μM for fentanyl, with detection limits of 6.7 nM and 33 nM, respectively. In addition, the sensor possesses excellent selectivity, outstanding reproducibility, and acceptable stability. The proposed sensor is feasible for the reliable monitoring of fentanyl and alfentanil in human serum samples, with acceptable reliability and high potential in real-world applications. Finally, the electrochemical characteristic fingerprint of fentanyl is investigated by studying the electrochemical behavior of alfentanil and fentanyl on the electrode surface.

Deep Neural Networks for Fine-Grained Surveillance of Overdose Mortality

Surveillance of drug overdose deaths relies on death certificates for identification of the substances that caused death. Drugs and drug classes can be identified through the International Classification of Diseases, Tenth Revision (ICD-10), codes present on death certificates. However, ICD-10 codes do not always provide high levels of specificity in drug identification. To achieve more fine-grained identification of substances on death certificate, the free-text cause-of-death section, completed by the medical certifier, must be analyzed. Current methods for analyzing free-text death certificates rely solely on lookup tables for identifying specific substances, which must be frequently updated and maintained. To improve identification of drugs on death certificates, a deep-learning named-entity recognition model was developed, utilizing data from the Kentucky Drug Overdose Fatality Surveillance System (2014-2019), which achieved an F1-score of 99.13%. This model can identify new drug misspellings and novel substances that are not present on current surveillance lookup tables, enhancing the surveillance of drug overdose deaths.

The influence of race, sexual orientation and gender identity on mental health, substance use, and academic persistence during the COVID-19 pandemic: A cross-sectional study from a national sample of college students in the healthy minds study

Purpose

This study seeks to characterize substance use, mental health, and academic persistence in college students during the COVID-19 pandemic, emphasizing variations by race, sexual orientation, and gender identity.

Methods

National samples (n = 146,810) of undergraduate students from the Healthy Minds Study were compared in two time periods: Fall 2017-Winter 2020 (pre-COVID) and March-December 2020. Descriptive statistics were conducted including t-tests/chi square tests comparing differences between time periods. Logistic regressions were estimated for main outcomes: substance use (licit, illicit, none), anxiety and/or depression symptoms, and academic persistence (student confidence that they will finish their degree). Marginal effects of race, sexual orientation, and gender identity were reported for all logistic regressions.

Results

In March-December 2020, students had 1.70 higher odds of screening positive for anxiety and/or depression compared to pre-COVID semesters. Latinx, Black, and "other" race/ethnicity had significantly higher probabilities of screening positive, as did transgender and gender non-conforming (TGNC) and lesbian, gay, bisexual, and queer (LGBQ) students.;Students had 0.43 times lower odds of reporting substance use in March-December 2020. Asian, Black, and Latinx students had significantly lower probabilities of reporting substance use, as did TGNC and LGBQ students.; During the pandemic, most students (86.7%) report at least 1 day of academic impairment (emotional/mental difficulties that hurt academic performance) in the last month due to mental health (up from 79.9% pre-pandemic).

Conclusions

Using the most comprehensive mental health data in college student populations, this is the first study to describe the impact of the pandemic on undergraduate students' substance use, mental health, and academic persistence/impairment.

Encountering Overdose: Examining the Contexts and Correlates of US College Students' Overdose Experiences

BACKGROUND

As overdose rates increase, it is critical to better understand the causes and contexts of overdose, particularly for college students who exhibit high rates of alcohol and drug use. The purpose of this study was to examine the social contexts of U.S. college students' overdose experiences (their own, witnessed, and family'/friends'), and to assess the correlates of personal overdose.

METHODS

A cross-sectional survey containing open- and closed-ended questions about overdose encounters was completed by undergraduate students at a southeastern American university (n = 1,236). Descriptive frequencies assessed prevalence, substance involvement, and fatalities associated with different encounter types. A content analysis of open-ended responses examined the social contexts of encounters. Multivariate logistic regression was used to assess the correlates of personal overdose.

RESULTS

Forty-one percent of respondents reported at least one type of overdose encounter and witnessed overdose was most common. Substances involved varied across encounter type and 20-40% of respondents reported overdose-related fatalities. Students who encountered overdose often reported multiple experiences and many attributed overdoses to mixing substances. Respondents commonly encountered overdose as intervening bystanders and overdose events were often perceived to be intentional or the result of using substances to cope with stress/mental health concerns. Personal overdose was significantly associated with having ever mixed alcohol with prescription drugs, been diagnosed with a mental disorder, witnessed an overdose, and had a family member/friend overdose.

CONCLUSION

Findings suggest a need for future research into the contexts and consequences of students' overdose encounters to more effectively tailor overdose prevention/response initiatives within college communities.

An Opioid Education Program for Baccalaureate Nursing Students

BACKGROUND

Unintentional drug overdose is the leading cause of accidental death in the United States. Previous research identified training health caregivers in the rescue medication naloxone as a strategy to prevent death from opioid overdose. Existing research on naloxone training with nursing students is limited. This educational research project investigated whether training on opioids, opioid toxicity, and overdose response could impact student knowledge, skills, and confidence responding to overdoses.

METHOD

Data were collected from baccalaureate nursing students using three sources: the Brief Opioid Overdose Knowledge questionnaire, a rubric based on the Substance Abuse and Mental Health Services Administration opioid overdose prevention toolkit, and a researcher-developed evaluation.

RESULTS

Planned repeated-measures analyses of variance conducted on data from 284 baccalaureate students indicated high uptake of knowledge and skills for opioid toxicity and naloxone administration. Results were maintained with slight enhancement at 1 week, and training was highly rated. Cohort analysis suggests efficacy across semesters.

CONCLUSION

Education improved student opioid knowledge, skills, and confidence and was relevant across undergraduate nursing curricula.

Implementation of a Mental Health Task Force in a collegiate-based emergency medical services organization

Objective: A Mental Health Task Force (MHTF) was implemented in 2016 by a collegiate-based emergency medical services (CBEMS) organization to (1) improve mental health emergency response and to (2) address concerns for the mental health of CBEMS providers. Participants: Skidmore College EMS is a Basic Life Support First Response service staffed by volunteer undergraduate students. Methods: In coordination with faculty and staff, students in the MHTF developed trainings, peer support structures, community events, policies, and informational resources. Results: Sixteen students joined the MHTF within 1 year. Over 35 Skidmore College EMS members received training on mental health emergency response, peer-support, and self-care. Debriefing programs, mindfulness-based events, shift-length limitations, and access to informational resources promoted the mental health of Skidmore College EMS members. Conclusions: Implementing an MHTF is an innovative, student-led approach to coupling education on emergency response with programming that supports the mental health of CBEMS providers.

Implementation of a collaborative model for opioid overdose prevention on campus

Drug overdose is the leading cause of death for Americans under the age of 50, a crisis that is driven by an increasingly potent supply of illicit opioids. College-aged adults are more likely than any other age group to engage in opioid misuse. Naloxone, the antidote for an opioid overdose, can save the life of an opioid overdose victim if it is readily available and administered quickly. The University of Texas at Austin implemented a collaborative model for proactive opioid overdose prevention in 2016. This model includes stocking naloxone in residence halls and providing it to police officers, training resident advisors and police officers to respond to suspected overdoses, and engaging student pharmacists in a service learning program to increase naloxone access and awareness among university students. Programmatic experiences and key recommendations for U.S. campuses are shared by faculty, student, and community leaders.

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.

Pharmacokinetics after a single dose of naloxone administered as a nasal spray in healthy volunteers

BACKGROUND

There is increasing interest in the use of intranasal naloxone to reverse adverse opioid effects during management of procedural pain in children and in adults after overdose. There are limited data on the pharmacokinetics of intranasal naloxone so in this study we aimed to detail the pharmacokinetic profile of the commercially marketed injectable solution of naloxone 0.4 mg/ml when administered as an intranasal spray.

METHODS

Twenty healthy volunteers received naloxone as an intranasal spray at a dose of 10 μg/kg. Venous blood sampling was carried out for 90 min after administration to determine the time profile of the plasma concentrations of using tandem mass spectrometry. Pharmacokinetic parameters were calculated using a one-compartment model.

RESULTS

Median time to maximum naloxone concentration (Tmax) was 14.5 (95% CI: 9.0-16.5) min, mean maximum naloxone concentration (Cmax) was 1.09 ± 0.56 ng/ml and mean AUC_{0-90 min} was 37.1 ± 15.0 ng*min/ml. Elimination half-life estimated from the median concentration data was 28.2 min.

CONCLUSION

Our results show a faster uptake of intranasal naloxone to maximum concentration compared with previous studies although with a marked variation in maximum concentration. The findings are consistent with our clinical experience of the time profile for reversing the effects of sufentanil sedation in children.