Development of a Medical Device in Response to a Fatal Self-Injection of Non-prescribed Opioids: A Case Report.

Patients who inject drugs (PWID) pose unique challenges in their medical care due to risks of increased infection and overdose. There are no known commercially available devices to prevent patients from self-injecting non-prescribed substances into vascular access devices (VADs). A patient in the emergency department (ED) of a midsized suburban hospital self-injected an opioid in the ED restroom after the placement of a vascular catheter by the nursing staff as part of her ED care. Despite precautions taken for a patient with a known opioid use disorder (OUD) and a history of self-injecting non-prescribed substances into VADs, the patient suffered a self-induced fatal overdose. PWID are at significant risk of self-injection when requiring intravenous medications as part of their medical care. This case highlighted the need for formal reporting for patients who self-inject non-prescribed substances into VADs. It revealed a lack of medical devices to help providers ensure that PWID cannot access their medical devices when intravenous therapy is indicated.

Evaluation of the Functionality in an Emergency Department Setting of an Intravenous Protection Device to Prevent Self-Injection.

Background: The United States has an opioid abuse crisis that has been increasing exponentially since 2013. In 2021, there were 220 deaths each day from opioid overdoses in the United States alone. Patients suffering from addiction often present to the emergency department (ED) anticipating that an intravenous (IV) catheter will be placed. This catheter is then accessible for patients to self-inject illicit drugs while under medical care or elope from the facility with the IV in place to self-inject. The misuse of medical IV access is a potential source of prolonged hospitalizations and fatal overdoses nationwide. On two separate occasions, patients were found dead in our ED bathroom after overdosing by accessing their IV site for self-injection. These events prompted the development of the IV SafeLock prototype. The IV SafeLock is designed to prevent intravenous access by the patient while allowing access by specified providers to administer medications. This study aims to investigate prototype usability and functionality by nursing staff in the ED. Methods: A prospective study was performed with twenty ED nurses in a clinical trial to use the IV SafeLock in the clinical setting. Each nurse was given two months to complete an evaluation of 20 patients requiring IV access. They used the IV SafeLock on infusion ports and Intermittent Needle Therapy (INT) access sites. A Likert scale was used to measure the ease of function and use of the IV SafeLock. Results and conclusion: The nurses felt that the IV SafeLock was easy to use and achieved its function of protecting the intravenous access site from self-injection. The IV SafeLock prototypes used in the trial were easy to use and functioned as intended most of the time. The IV SafeLock can be used by nursing staff in a clinical setting to help prevent self-injection. Clinical Trial Registration: NCT05695183 enrolled 01/12/2023.

Evaluating atrial fibrillation artificial intelligence for the ED: statistical and clinical implications.

OBJECTIVE: An artificial intelligence (AI) algorithm has been developed to detect the electrocardiographic signature of atrial fibrillation (AF) present on an electrocardiogram (ECG) obtained during normal sinus rhythm. We evaluated the ability of this algorithm to predict incident AF in an emergency department (ED) cohort of patients presenting with palpitations without concurrent AF. METHODS: This retrospective study included patients 18 years and older who presented with palpitations to one of 15 ED sites and had a 12­lead ECG performed. Patients with prior AF or newly diagnosed AF during the ED visit were excluded. Of the remaining patients, those with a follow up ECG or Holter monitor in the subsequent year were included. We evaluated the performance of the AI-ECG output to predict incident AF within one year of the index ECG by estimating an area under the receiver operating characteristics curve (AUC). Sensitivity, specificity, and positive and negative predictive values were determined at the optimum threshold (maximizing sensitivity and specificity), and thresholds by output decile for the sample. RESULTS: A total of 1403 patients were included. Forty-three (3.1%) patients were diagnosed with new AF during the following year. The AI-ECG algorithm predicted AF with an AUC of 0.74 (95% CI 0.68-0.80), and an optimum threshold with sensitivity 79.1% (95% Confidence Interval (CI) 66.9%-91.2%), and specificity 66.1% (95% CI 63.6%-68.6%). CONCLUSIONS: We found this AI-ECG AF algorithm to maintain statistical significance in predicting incident AF, with clinical utility for screening purposes limited in this ED population with a low incidence of AF.

Emergency Medical Services Provider Self-Reported Occupational Safety.

BACKGROUND: Emergency medical services (EMS) providers are at high risk for occupational violence, and some tactical EMS providers carry weapons. METHODS: Anonymous surveys were administered to tactical and nontactical prehospital providers at 180 prehospital agencies in northeast Ohio between September 2018 and March 2019. Demographics were collected, and survey questions asked about workplace violence and comfort level with tactical EMS carrying weapons. RESULTS: Of 432 respondents, 404 EMS providers (94%) reported a history of verbal or physical assault on scene, and 395 (91%) reported working in a setting with a direct active threat at least rarely. Of those reporting a history of assault on scene, 46.5% reported that it occurred at least sometimes. Higher rates of assault on scene were associated with being younger, white, or an emergency medical technician-paramedic, working in an urban environment, having more frequent direct active threats, and having more comfort with tactical EMS carrying firearms (p ≤ .03). Most respondents (306; 71%) reported that they were prepared to defend themselves from someone who originally called for help. Most (303; 70%) reported a comfort level of 8 or higher (from 1, not comfortable to 10, completely comfortable) with tactical EMS providers carrying weapons. Comfort with tactical EMS providers carrying weapons was associated with being white, not having a bachelor's degree, and feeling prepared to defend oneself from a patient (p ≤ .02). CONCLUSION: EMS providers in the survey report high rates of verbal and physical violence while on scene and are comfortable with tactical EMS providers carrying weapons.

A Delayed Case of Pericarditis Following Recovery From COVID-19 Infection.

Pericarditis is a rare cardiac complication of coronavirus 19 (COVID-19) infection. Recent case reports describe severe sequelae of pericarditis, including cardiac tamponade, developing within days of initial COVID-19 symptoms. We present a case of pericarditis with slower onset and milder symptoms, developing over a period of a few weeks in an immunocompetent male who recovered from COVID-19 several months earlier. A 65-year-old male presented to an emergency department several times for one week of worsening chest and neck symptoms, along with fever. He had been symptom-free after a three-day course of cough, myalgias, and fever with positive COVID-19 testing, approximately 70 days earlier. He was ultimately admitted for fever and pericarditis with an associated pericardial effusion and positive PCR testing for COVID-19. Pericarditis should be considered in the differential diagnosis for patients with COVID-19 and unexplained persistent chest symptoms. The possibility of recurrent or atypical latent infection should additionally be considered in the months following the initial COVID-19 infection. Bedside ultrasound may facilitate early diagnosis and management of COVID-19 associated pericarditis.

A Case of COVID-19 Re-Infection in a Liver Transplant Patient.

Coronavirus disease 2019 (COVID-19) is an ongoing worldwide pandemic infection. The exact incidence of disease re-infection or recurrence remains unknown. One particular at-risk population includes individuals with solid organ transplantation on immunosuppression. We present a case of COVID-19 re-infection in a chronically immunocompromised liver transplant patient. A 53-year-old female presented to the Emergency Department (ED) with nausea, vomiting, diarrhea, and myalgias. She was found to test positive for COVID-19. Her relevant medical history included liver transplantation on chronic immunosuppression. More recently, she had tested positive for COVID-19 approximately three months prior to this and was hospitalized at that time for encephalopathy and treated with remdesivir and convalescent plasma. She had subsequently recovered with negative COVID-19 testing in the interim. On the ED presentation with presumed re-infection, her disease was deemed to be mild with lack of severe symptoms or pulmonary involvement, and she was discharged with outpatient follow-up for monoclonal antibody infusion therapy. We describe a scenario of presumed COVID-19 re-infection in a liver transplant patient. To our knowledge, this is a rare event and has been reported internationally in only a handful of individuals. We surmise that immunosuppression could offer some protection from the inflammatory cascade of the initial disease process in COVID-19 given the relatively mild disease observed in our patient. On the other hand, a less robust immune response may decrease humoral immunity and leave patients at greater risk of re-infection. Further investigation is necessary to delineate COVID-19 disease re-infection versus relapse, especially in the setting of an immunocompromised state.

Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea.

BACKGROUND: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing can lack sensitivity and specificity. The objective of this study was to assess the accuracy of an artificial intelligence-enabled ECG to identify patients presenting with dyspnea who have left ventricular systolic dysfunction (LVSD). METHODS: We retrospectively applied a validated artificial intelligence-enabled ECG algorithm for the identification of LVSD (defined as LV ejection fraction ≤35%) to a cohort of patients aged ≥18 years who were evaluated in the ED at a Mayo Clinic site with dyspnea. Patients were included if they had at least one standard 12-lead ECG acquired on the date of the ED visit and an echocardiogram performed within 30 days of presentation. Patients with prior LVSD were excluded. We assessed the model performance using area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity. RESULTS: A total of 1606 patients were included. Median time from ECG to echocardiogram was 1 day (Q1: 1, Q3: 2). The artificial intelligence-enabled ECG algorithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI, 0.86-0.91) and accuracy of 85.9%. Sensitivity, specificity, negative predictive value, and positive predictive value were 74%, 87%, 97%, and 40%, respectively. To identify an ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.85 (95% CI, 0.83-0.88), 86%, 63%, and 91%, respectively. NT-proBNP (N-terminal pro-B-type natriuretic peptide) alone at a cutoff of >800 identified LVSD with an area under the receiver operating characteristic curve of 0.80 (95% CI, 0.76-0.84). CONCLUSIONS: The ECG is an inexpensive, ubiquitous, painless test which can be quickly obtained in the ED. It effectively identifies LVSD in selected patients presenting to the ED with dyspnea when analyzed with artificial intelligence and outperforms NT-proBNP. Graphic Abstract: A graphic abstract is available for this article.