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Who receives naloxone from emergency medical services? Characteristics of calls and recent trends.

ABSTRACT: Background: With the rapid rise in opioid overdose-related deaths, state policy makers have expanded policies to increase the use of naloxone by emergency medical services (EMS). However, little is known about changes in EMS naloxone administration in the context of continued worsening of the opioid crisis and efforts to increase use of naloxone. This study examines trends in patient demographics and EMS response characteristics over time and by county urbanicity. Methods: We used data from the 2013-2016 National EMS Information System to examine trends in patient demographics and EMS response characteristics for 911-initiated incidents that resulted in EMS naloxone administration. We also assessed temporal, regional, and urban-rural variation in per capita rates of EMS naloxone administrations compared with per capita rates of opioid-related overdose deaths. Results: From 2013 to 2016, naloxone administrations increasingly involved young adults and occurred in public settings. Particularly in urban counties, there were modest but significant increases in the percentage of individuals who refused subsequent treatment, were treated and released, and received multiple administrations of naloxone before and after arrival of EMS personnel. Over the 4-year period, EMS naloxone administrations per capita increased at a faster rate than opioid-related overdose deaths across urban, suburban, and rural counties. Although national rates of naloxone administration were consistently higher in suburban counties, these trends varied across U.S. Census Regions, with the highest rates of suburban administration occurring in the South. Conclusions: Naloxone administration rates increased more quickly than opioid deaths across all levels of county urbanicity, but increases in the percentage of individuals requiring multiple doses and refusing subsequent care require further attention.

SUBMITTER: Geiger C

PROVIDER: S-EPMC6989345 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Who receives naloxone from emergency medical services? Characteristics of calls and recent trends.

Geiger Caroline C Smart Rosanna R Stein Bradley D BD

Substance abuse 20190730 3

<i>Background:</i> With the rapid rise in opioid overdose-related deaths, state policy makers have expanded policies to increase the use of naloxone by emergency medical services (EMS). However, little is known about changes in EMS naloxone administration in the context of continued worsening of the opioid crisis and efforts to increase use of naloxone. This study examines trends in patient demographics and EMS response characteristics over time and by county urbanicity. <i>Methods:</i> We used ...[more]

PMID: 31361589

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Project description:BACKGROUND:Little is known regarding paediatric medical emergency calls to Danish Emergency Medical Dispatch Centres (EMDC). This study aimed to investigate these calls, specifically the medical issues leading to them and the pre-hospital units dispatched to the paediatric emergencies. METHODS:We performed a retrospective, observational study on paediatric medical emergency calls managed by the EMDC in the Region of Southern Denmark in February 2016. We reviewed audio recordings of emergency calls and ambulance records to identify calls concerning patients ≤ 15 years. We examined EMDC dispatch records to establish how the medical issues leading to these calls were classified and which pre-hospital units were dispatched to the paediatric emergencies. We analysed the data using descriptive statistics. RESULTS:Of a total of 7052 emergency calls in February 2016, 485 (6.9%) concerned patients ≤ 15 years. We excluded 19 and analysed the remaining 466. The reported medical issues were commonly classified as: "seizures" (22.1%), "sick child" (18.9%) and "unclear problem" (12.9%). The overall most common pre-hospital response was immediate dispatch of an ambulance with sirens and lights with a supporting physician-manned mobile emergency care unit (56.4%). The classification of medical issues and the dispatched pre-hospital units varied with patient age. DISCUSSION:We believe our results might help focus the paediatric training received by emergency medical dispatch staff on commonly encountered medical issues, such as the symptoms and conditions pertaining to the symptom categories "seizures" and "sick child". Furthermore, the results could prove useful in hypothesis generation for future studies examining paediatric medical emergency calls. CONCLUSION:Almost 7% of all calls concerned patients ≤ 15 years. Medical issues pertaining to the symptom categories "seizures", "sick child" and "unclear problem" were common and the calls commonly resulted in urgent pre-hospital responses.

Project description:BackgroundImmediate recognition of stroke symptoms is crucial to ensure timely access to revascularisation therapy. Medical dispatchers ensure fast admission to stroke facilities by prioritising the appropriate medical response. Data on medical dispatchers' ability to recognise symptoms of acute stroke are therefore critical in organising emergency stroke care. We aimed to describe the sensitivity and positive predictive value of medical dispatchers' ability to recognise acute stroke during emergency calls, and to identify factors associated with recognition.MethodsThis was an observational study of 2653 consecutive unselected patients with a final diagnosis of stroke or transient ischemic attack (TIA). All admitted through the Emergency Medical Services Copenhagen, during a 2-year study period (2012-2014). Final diagnoses were matched with dispatch codes from the Emergency Medical Dispatch Centre. Sensitivity and positive predictive value were calculated. The effect of age, gender, and time-of-day was analysed using multivariable logistic regression.ResultsThe sensitivity was 66.2 % (95 % CI: 64.4 %-68.0 %), and the positive predictive value was 30.2 % (95 % CI: 29.1 %-31.4 %). The multivariable logistic regression analyses showed that emergency calls during daytime and a final diagnosis of TIA vs. intracerebral haemorrhage (ICH), was positively associated with recognition of stroke (OR 2.70, 95 % CI: 2.04-3.57).DiscussionThis study reports a high rate of stroke recognition compared to other studies ranging from 31% to 74%. The high sensitivity is likely the result of a profound reorganisation of the Emergency Medical ServicesCopenhagen, including the introduction of EMDs with a medical profession, and a criteria-based dispatch tool. A recognition rate of 100 % is not obtainable without an inappropriate amount of false positive cases.ConclusionsWe report an overall high recognition of stroke by medical dispatchers. A final diagnosis of TIA, compared to ICH, was positively associated with recognition of acute stroke. Emergency medical dispatchers serve as the essential first step in ensuring fast-track stroke treatment, which would promote timely acute therapy.Trial registrationUnique identifier: NCT02191514.

Project description:ImportanceEmergency medical dispatchers fail to identify approximately 25% of cases of out-of-hospital cardiac arrest (OHCA), resulting in lost opportunities to save lives by initiating cardiopulmonary resuscitation.ObjectiveTo examine how a machine learning model trained to identify OHCA and alert dispatchers during emergency calls affected OHCA recognition and response.Design, setting, and participantsThis double-masked, 2-group, randomized clinical trial analyzed all calls to emergency number 112 (equivalent to 911) in Denmark. Calls were processed by a machine learning model using speech recognition software. The machine learning model assessed ongoing calls, and calls in which the model identified OHCA were randomized. The trial was performed at Copenhagen Emergency Medical Services, Denmark, between September 1, 2018, and December 31, 2019.InterventionDispatchers in the intervention group were alerted when the machine learning model identified out-of-hospital cardiac arrest, and those in the control group followed normal protocols without alert.Main outcomes and measuresThe primary end point was the rate of dispatcher recognition of subsequently confirmed OHCA.ResultsA total of 169?049 emergency calls were examined, of which the machine learning model identified 5242 as suspected OHCA. Calls were randomized to control (2661 [50.8%]) or intervention (2581 [49.2%]) groups. Of these, 336 (12.6%) and 318 (12.3%), respectively, had confirmed OHCA. The mean (SD) age among of these 654 patients was 70 (16.1) years, and 419 of 627 patients (67.8%) with known gender were men. Dispatchers in the intervention group recognized 296 confirmed OHCA cases (93.1%) with machine learning assistance compared with 304 confirmed OHCA cases (90.5%) using standard protocols without machine learning assistance (P?=?.15). Machine learning alerts alone had a significantly higher sensitivity than dispatchers without alerts for confirmed OHCA (85.0% vs 77.5%; P?<?.001) but lower specificity (97.4% vs 99.6%; P?<?.001) and positive predictive value (17.8% vs 55.8%; P?<?.001).Conclusions and relevanceThis randomized clinical trial did not find any significant improvement in dispatchers' ability to recognize cardiac arrest when supported by machine learning even though artificial intelligence did surpass human recognition.Trial registrationClinicalTrials.gov Identifier: NCT04219306.

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Who receives naloxone from emergency medical services? Characteristics of calls and recent trends.

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Who receives naloxone from emergency medical services? Characteristics of calls and recent trends.

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