The Knowledge Gap: Mentorship in Emergency Medicine Residency.

STUDY OBJECTIVE: Studies of mentorship in emergency medicine show that mentored residents are twice as likely to describe their career preparation as excellent as compared to nonmentored peers. There has been significant interest in the mentor-mentee relationship in medicine; however, there is minimal guidance and published literature specific to emergency medicine residents. METHODS: In this narrative review, we described the emergency medicine mentor-mentee relationship, discussed alternatives to the traditional dyadic model, and highlighted current barriers to effective mentorship. We conducted a structured literature review to identify relevant published articles regarding the mentoring of emergency medicine residents. Additional studies from general mentoring literature were included based on relevancy. RESULTS: We identified 39 studies in emergency medicine literature based on our search criteria. Additional studies from general medicine literature were included based on relevancy to this review. Based on the limited available literature, we recommend maximizing the resident mentoring relationship by developing formal mentoring programs, supporting the advancement of women and underrepresented minority mentors, and moving toward team mentoring, including peer, near-peer, and collaborative mentorship. The development of a mentoring network is a logical strategy for residents to work with a diverse group of individuals to maximize benefits in multiple areas. CONCLUSION: Alternative approaches to the traditional and hierarchal dyadic mentoring style (eg, team mentoring) are effective methods that residencies may promote to increase effective mentoring. Future efforts in mentoring emergency medicine residents emphasize these strategies, which are increasingly beneficial given the constraints and use of technology highlighted by the COVID-19 pandemic.

Decision support tool for hospital resource allocation during the COVID-19 pandemic.

The SARS-CoV-2 (COVID-19) pandemic has placed unprecedented demands on entire health systems and driven them to their capacity, so that health care professionals have been confronted with the difficult problem of ensuring appropriate staffing and resources to a high number of critically ill patients. In light of such high-demand circumstances, we describe an open web-accessible simulation-based decision support tool for a better use of finite hospital resources. The aim is to explore risk and reward under differing assumptions with a model that diverges from most existing models which focus on epidemic curves and related demand of ward and intensive care beds in general. While maintaining intuitive use, our tool allows randomized "what-if" scenarios which are key for real-time experimentation and analysis of current decisions' down-stream effects on required but finite resources over self-selected time horizons. While the implementation is for COVID-19, the approach generalizes to other diseases and high-demand circumstances.

Outcomes for in-hospital cardiac arrest for COVID-19 patients at a rural hospital in Southern California.

BACKGROUND: In-hospital cardiac arrest (IHCA) carries a high mortality and providing resuscitation to COVID-19 patients presents additional challenges for emergency physicians. Our objective was to describe outcomes of COVID-19 patients suffering IHCA at a rural hospital in Southern California. METHODS: Single-center retrospective observational study. A hospital registry of COVID-19 patients was queried for all patients who suffered IHCA and received cardiopulmonary resuscitation (CPR) between May 1st and July 31st, 2020. A manual chart review was performed to obtain patient demographics, oxygen requirement prior to cardiac arrest (CA), details of the resuscitation including presence of an emergency physician, and final disposition. RESULTS: Twenty-one patients were identified, most of whom were Hispanic, male, and aged 50-70. The most common medical comorbidities were diabetes and hypertension. Most patients suffered respiratory arrest, with an initial rhythm of pulseless electrical activity or asystole. Return of spontaneous circulation (ROSC) was achieved in 3/9 patients already receiving mechanical ventilation, but all 3 expired within the following 24 h. ROSC was achieved in 10/12 patients not already intubated, though most also expired within a few days. The only 2 patients who survived to discharge suffered respiratory arrest after their oxygen delivery device dislodged. CONCLUSION: At a small rural hospital with limited resources and a predominantly Hispanic population, cardiac arrest in a COVID-19 patient portends an extremely poor prognosis. A better appreciation of these outcomes should help inform emergency providers and patients when discussing code status and attempts at resuscitation, particularly in resource limited settings.

Development and Prospective Validation of a Deep Learning Algorithm for Predicting Need for Mechanical Ventilation.

BACKGROUND: Objective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation (MV) may aid in delivering timely treatment. RESEARCH QUESTION: Can a transparent deep learning (DL) model predict the need for MV in hospitalized patients and those with COVID-19 up to 24 h in advance? STUDY DESIGN AND METHODS: We trained and externally validated a transparent DL algorithm to predict the future need for MV in hospitalized patients, including those with COVID-19, using commonly available data in electronic health records. Additionally, commonly used clinical criteria (heart rate, oxygen saturation, respiratory rate, Fio2, and pH) were used to assess future need for MV. Performance of the algorithm was evaluated using the area under receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value. RESULTS: We obtained data from more than 30,000 ICU patients (including more than 700 patients with COVID-19) from two academic medical centers. The performance of the model with a 24-h prediction horizon at the development and validation sites was comparable (AUC, 0.895 vs 0.882, respectively), providing significant improvement over traditional clinical criteria (P < .001). Prospective validation of the algorithm among patients with COVID-19 yielded AUCs in the range of 0.918 to 0.943. INTERPRETATION: A transparent deep learning algorithm improves on traditional clinical criteria to predict the need for MV in hospitalized patients, including in those with COVID-19. Such an algorithm may help clinicians to optimize timing of tracheal intubation, to allocate resources and staff better, and to improve patient care.

Multisystem Inflammatory Syndrome in Children (MIS-C) in an Adolescent that Developed Coronary Aneurysms: A Case Report and Review of the Literature.

BACKGROUND: A small subset of pediatric patients develop a rare syndrome associated with Coronavirus Disease 2019 (COVID-19) infection called multisystem inflammatory syndrome in children (MIS-C). This syndrome shares characteristics with Kawasaki disease. CASE REPORT: A 15-year-old girl presented to our Emergency Department (ED) with fevers and malaise. She was diagnosed on her initial visit with an acute viral syndrome and discharged with a COVID polymerase chain reaction test pending, which was subsequently negative. She returned 3 days later with persistent fever, conjunctivitis, and a symmetric targetoid rash over her palms. She had no adenopathy, but her erythrocyte sedimentation rate and C-reactive protein were both significantly elevated at 90 mm/h and 19.61 mg/dL, respectively. The patient was then transferred to the regional children's hospital due to a clinical suspicion for MIS-C, and subsequent COVID-19 immunoglobulin G testing was positive. She had been empirically started on intravenous immunoglobulin in addition to 81 mg aspirin daily. Initial echocardiograms showed mild dilatation of the left main coronary artery, and on repeat echocardiogram, a right coronary artery aneurysm was also identified. Oral prednisone therapy (5 mg) was initiated and the patient was discharged on a continued prednisone taper. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: We present a case of a 15-year-old girl who presented to the ED with MIS-C who developed coronary aneurysms despite early therapy, to increase awareness among emergency physicians of this emerging condition.