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in category emergency medicine

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81: Performance of prediction models for short term outcome in COVID-19 patients in the emergency department: a retrospective study
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Posted 29 Nov 2020

Performance of prediction models for short term outcome in COVID-19 patients in the emergency department: a retrospective study
343 downloads medRxiv emergency medicine

Paul M.E.L. van Dam, Noortje Zelis, Sander M.J. van Kuijk, Aimee E.M.J.H. Linkens, Renee R.A.G. Bruggemann, Bart Spaetgens, Iwan C.C. van der Horst, Patricia M. Stassen

Introduction: Coronavirus disease 2019 (COVID-19) has a high burden on the healthcare system and demands information on the outcome early after admission to the emergency department (ED). Previously developed prediction models may assist in triaging patients when allocating healthcare resources. We aimed to assess the value of several prediction models when applied to COVID-19 patients in the ED. Methods: All consecutive COVID-19 patients who visited the ED of a combined secondary/tertiary care center were included. Prediction models were selected based on their feasibility. The primary outcome was 30-day mortality, secondary outcomes were 14-day mortality, and a composite outcome of 30-day mortality and admission to the medium care unit (MCU) or the intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC). Results: A total of 403 ED patients were diagnosed with COVID-19. Within 30 days, 95 patients died (23.6%), 14-day mortality was 19.1%. Forty-eight patients (11.9%) were admitted to the MCU, 66 patients (16.4%) to the ICU and 152 patients (37.7%) met the composite endpoint. Eleven models were included: RISE UP score, 4C mortality score, CURB-65, MEWS, REMS, abbMEDS, SOFA, APACHE II, CALL score, ACP index and Host risk factor score. The RISE UP score and 4C mortality score showed a very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84 respectively, 95% CI 0.79-0.88 for both), for 14-day mortality (AUC 0.83, 95% CI: 0.79-0.88, for both) and for the composite outcome (AUC 0.79 and 0.77 respectively, 95% CI 0.75-0.84). The discriminatory performance of the RISE UP score and 4C mortality score was significantly higher compared to that of the other models. Conclusion: The RISE UP score and 4C mortality score have good discriminatory performance in predicting adverse outcome in ED patients with COVID-19. These prediction models can be used to recognize patients at high risk for short-term poor outcome and may assist in guiding clinical decision-making and allocating healthcare resources.

82: Segmental Signs and Spontaneous Pain in Acute Visceral Disease - Lateralization and Bodily Patterns
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Posted 24 Jul 2020

Segmental Signs and Spontaneous Pain in Acute Visceral Disease - Lateralization and Bodily Patterns
340 downloads medRxiv emergency medicine

Nour Shaballout, Anas Aloumar, Jorge Manuel, Marcus May, Florian Beissner

The differential diagnosis of acute visceral diseases is a challenging clinical problem. The older literature suggests that patients with acute visceral problems show segmental signs, such as hyperalgesia, skin resistance, or muscular defence, whose lateralization and segmental distribution may be used for differential diagnosis. This study aimed to investigate the lateralization and segmental distribution of spontaneous pain and segmental signs in acute visceral diseases. We recruited 208 emergency room patients that were presenting for acute medical problems. All patients underwent a structured 10-minute bodily examination to test for various segmental signs and were asked for spontaneous pain and segmental symptoms, such as nausea, meteorism, and urinary retention. We collected all findings as digital drawings on a tablet-PC. After the final diagnosis, patients were divided into groups according to the organ affected. Using statistical image analysis, we calculated average distributions of pain and segmental signs for the heart, lungs, stomach, liver/gallbladder, and kidneys/ureters analyzing their segmental distribution and lateralization. 85 of 110 patients with a single-organ problem reported pain, while 81 had at least one segmental sign, the most frequent being hyperalgesia (n=46), and muscle resistance (n=39). While the pain was distributed along the body midline, segmental signs for the heart, stomach and liver/gallbladder appeared mostly ipsilateral to the affected organ. An unexpectedly high number of patients (n=37) further showed ipsilateral mydriasis. The present study underlines the usefulness of including segmental signs in the bodily examination of patients with acute medical problems.

83: Mobile robotic systems in patient-facing functions: national acceptability survey, single site feasibility study and cost-effectiveness analysis
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Posted 30 Sep 2020

Mobile robotic systems in patient-facing functions: national acceptability survey, single site feasibility study and cost-effectiveness analysis
334 downloads medRxiv emergency medicine

Peter R. Chai, Farah Z Dadabhoy, Hen-Wei Huang, Jacqueline N Chu, Annie Feng, Hien M Le, Joy Collins, Marco Da Silva, Marc Raibert, Chin Hur, Edward W. Boyer, Giovanni Traverso

Objective: To understand the acceptability of patient-facing mobile robotic systems on a national scale, conduct a pilot feasibility study to deploy and measure satisfaction associated with clinical evaluation using a mobile telehealth robot in the emergency department (ED) and to build a decision analytic model to gauge the potential of a robotic system to prevent COVID-19 infections and conserve personal protective equipment in the ED. Design: Mixed study comprising an online sampling-based survey, single-site observational clinical trial and development of a decision analytic model. Setting: A quaternary care, urban, academic, emergency department in Boston, Massachusetts, USA. Participants: For the acceptability survey, we recruited N=1000 individuals living in the United States participating in an online sampling from the survey provider YouGov. In the ED study, we enrolled 40 individuals over 18 years old presenting to the ED for evaluation. Interventions: In the pilot ED study, consenting participants were exposed to a mobile robotic system facilitated triage interview controlled by an emergency medicine clinician. Afterwards, participants completed a survey to measure their satisfaction with the robotic system. Main outcome measures: Acceptability of mobile robot facilitated tasks in healthcare (national survey), satisfaction with interaction of a robotic system (ED study), number of potential SARS-CoV-2 infections avoided and cost savings (US dollars) per year per ED (decision analytic model). Results: In the national survey, participants rated the use of robotics for a variety of patient-facing healthcare functions useful or very useful. The perceived usefulness increased when asked to consider these functions in the context of the COVID-19 pandemic. In the ED, 40 patients completed study procedures; 92.5% (N=37) reported satisfaction with the robotic system. Most participants (82.5%, N=33) reported their experience being evaluated by a robotic system was as good as an in-person encounter. Our decision analytic model estimated that robotic evaluations could prevent 2.68 infections per ED yearly and save $1 million annually per ED by decreasing PPE and additional staffing in a triage space. Conclusions: Robotic systems were broadly acceptable across the US and their acceptance increased in the setting of COVID-19. Mobile robotic-enabled teleheath facilitated contactless evaluation of ED patients and was highly acceptable and equivalent to an in-person history. Robotic platforms may prevent healthcare-associated COVID-19 transmission to healthcare workers and have a significant cost savings if widely implemented among healthcare systems.

84: New Zealand Emergency Department COVID-19 Preparedness Survey
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Posted 07 Apr 2021

New Zealand Emergency Department COVID-19 Preparedness Survey
327 downloads medRxiv emergency medicine

Michael J Howard, Charlotte Chambers, Nicholas M Mohr

ObjectiveThis research sought to assess the level of COVID-19 preparedness of Emergency Departments (EDs) in Aotearoa New Zealand (NZ) through the views of Emergency Medicine specialists working in District Health Boards around the country. Given the limited experience NZ hospitals have had with SARS-CoV-2, a comparison of current local practice with recent literature from other countries identifying known weaknesses may help prevent future healthcare worker infections in NZ. MethodsA cross-sectional survey by convenience sample of New Zealand Emergency Specialists in November 2020 to evaluate preparedness of engineering, administrative policy, and PPE use. ResultsA total of 137 surveys were completed (32% response rate), revealing heterogeneity in NZ ED clinical work environments in November 2020. More than 10% of emergency specialists surveyed reported no access to negative pressure rooms. N95 fit testing was not done on 15 (11%). Most specialists (86%) work in EDs that cohort patients, about one-third (34%) do not use spotters during PPE doffing, few have policy regarding breaches in PPE, and most do not have required space for physical distancing in non-clinical areas. Initial PPE training, simulations and segregating patients were widespread but appear to be waning with persistent low SARS-CoV-2 prevalence. PPE shortages were not identified in NZ EDs, yet 13% of consultants did not indicate they would use respirators during aerosol generating procedures on COVID-19 patients. Treatment interventions including non-invasive ventilation and high flow nasal cannula were common. Many respondents reported high levels of stress attributable to predicted inadequate staffing and the state of overall preparedness in event of a second wave. ConclusionsNew Zealand emergency specialists identified significant gaps in COVID-19 preparedness, and they have a unique opportunity to translate lessons from other locations into local action. Proactive identification of weaknesses in hospital engineering, policy, and PPE practice in advance of future SARS-CoV-2 endemic transmission would be prudent. What is already known?Aotearoa New Zealand has eliminated COVID-19 community transmission. Recently, trans-Tasman neighbour Australia has controlled SARS-CoV-2 surges which were complicated by significant nosocomial spread and healthcare worker infections. Several recent publications as well as expert recommendations from the Australian Department of Health and Human Services have listed improvements to the Hierarchy of Controls necessary to prevent future outbreaks in hospitals and long-term care facilities. What are the new findings?Survey responses specifically identified breakdowns in engineering, administrative policy and PPE in New Zealand emergency departments (EDs), potentially increasing healthcare worker nosocomial infection risk. As of November 2020, equitable access of all NZ emergency specialists to recommended negative flow rooms, fit testing of N95 masks, and other evidence based policy upgrades to COVID-19 infection prevention and control (IPC) standards are not universal. What do the new findings imply?The experience of local emergency specialists in a rapidly evolving pandemic can identify weaknesses in emergency preparedness previously reported to have increased nosocomial infection risk in similar healthcare environments. The aim of this research was to identify those weaknesses in local NZ emergency department policy, protocols and PPE and further efforts to provide proactive recommendations for system improvement. Finally, the research sought to understand how safe NZ emergency specialists felt during the initial lockdown and provides insight as to the psychological experiences of this vital group of front-line staff.

85: A comparison of Emergency Department presentations for Medically Unexplained Symptoms in Frequent Attenders during COVID-19
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Posted 31 Aug 2020

A comparison of Emergency Department presentations for Medically Unexplained Symptoms in Frequent Attenders during COVID-19
327 downloads medRxiv emergency medicine

Natasha Faye Daniels, Raiiq Ridwan, Edward BG Barnard, Talha Muneer Amanullah, Catherine Hayhurst

Background Medically Unexplained Symptoms (MUS) refer to symptoms with no identified organic aetiology, and are amongst the most challenging for patients and Emergency Department (ED) staff. Providers working in our ED perceived an increase in severity and frequency of these types of presentations during the COVID-19 pandemic. Methods A retrospective list of frequent attenders (FA) presenting five or more times to the ED between two 122-day periods were examined: 01 Mar to 30 Jun 2019 (Control) and 2020 (COVID-19). The FA group were then examined to identify patients presenting with MUS (FA-MUS). Results The total number of ED attendances during the control period was n=42,785 which reduced to n=28,806 in the COVID-19 period, a decrease of 32.7%. The control FA cohort had n=44 FA-MUS patients with 149 ED visits. This increased to n=65 FA-MUS patients with 267 visits during COVID-19, p=0.44. There was a significant increase in the proportion of all ED visits that were FA-MUS: 0.3% (control) compared to 0.9% (COVID-19); OR 2.7, p<0.001. There was a significant increase in shortness of breath amongst MUS during the COVID-19 pandemic relative to the control period (p<0.01), with no significant difference in any other MUS category. Conclusion Whilst the total number of ED attendances reduced by almost one third during COVID-19, the actual number of all visits by frequent attenders with MUS increased and the proportion of attendances by these tripled during the same period. This presents an increasing challenge to ED clinicians who may feel underprepared to manage these patients effectively.

86: SYSTEMATIC REVIEW ON EPIDEMIOLOGY, INTERVENTIONS AND MANAGEMENT OF NONCOMMUNICABLE DISEASES IN ACUTE AND EMERGENCY CARE SETTINGS IN KENYA
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Posted 29 Jun 2020

SYSTEMATIC REVIEW ON EPIDEMIOLOGY, INTERVENTIONS AND MANAGEMENT OF NONCOMMUNICABLE DISEASES IN ACUTE AND EMERGENCY CARE SETTINGS IN KENYA
326 downloads medRxiv emergency medicine

Christine Ngaruiya, Annrita Kawira, MBChB, Florence Mali, MBChB, Faith Kambua, BPharm, Beatrice Mwangi, MBChB, Mbatha Wambua, MBChB, Denise Hersey, MLS, Laventa Obare, MBChB, Rebecca Leff, Benjamin Wachira, MBChB

Introduction: Mortality and morbidity from Non-Communicable Diseases (NCDs) in Africa are expected to worsen if the status quo is maintained. Emergency care settings act as a primary point of entry into the health system for a spectrum of NCD-related illnesses, however, there is a dearth of literature on this population. We conducted a systematic review assessing available evidence on epidemiology, interventions and management of NCDs in acute and emergency care settings in Kenya, the largest economy in East Africa and a medical hub for the continent. Methods: All searches were run on July 15, 2015 capturing concepts of NCDs, and acute and emergency care. The study is registered at PROSPERO (CRD42018088621). Results: We retrieved a total of 461 references, and an additional 23 articles in grey literature. 391 studies were excluded by title or abstract, and 93 articles read in full. We included 10 articles in final thematic analysis. The majority of studies were conducted in tertiary referral or private/mission hospitals. Cancer, diabetes, cardiovascular disease and renal disease were addressed. Majority of the studies were retrospective, cross-sectional in design; no interventions or clinical trials were identified. There was a lack of access to basic diagnostic tools, and management of NCDs and their complications was limited. Conclusion: There is a paucity of literature on NCDs in Kenyan emergency care settings, with particular gaps on interventions and management. Opportunities include nationally representative, longitudinal research such as surveillance and registries, as well as clinical trials and implementation science to advance evidence-based, context-specific care.

87: Revisiting Role of Bilateral Ligation of Internal Iliac Arteries and Preperitoneal Pelvic Packing for Haemorrhage Control in Patients with Pelvic Injuries in Resource Constraint Settings.
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Posted 14 Jul 2020

Revisiting Role of Bilateral Ligation of Internal Iliac Arteries and Preperitoneal Pelvic Packing for Haemorrhage Control in Patients with Pelvic Injuries in Resource Constraint Settings.
326 downloads medRxiv emergency medicine

Dinesh Bagaria, Majid Anwer, Narendra Choudhary, Abhinav Kumar, Pratyusha Priyadarshini, Niladri Banerjee, Junaid Alam, Amit Gupta, Biplab Mishra, Sushma Sagar, Subodh Kumar

Background Since the description of bilateral ligation of internal iliac arteries (BLIIA) and preperitoneal pelvic packing (PPP) for haemorrhage control in pelvic injury patients, multiple reports have been published advocating its use with acceptable outcomes. We analyzed our experience with this technique in a setting where the facility of hybrid Operating room for simultaneous angioembolisation is not available. Material and Methods We prospectively analysed data of sixty-six patients who presented in a state of unresponsive shock with pelvic fracture between January 2014 and September 2019. After initial resuscitation, they all underwent BLIIA with PPP as part of damage control surgery. Results Out of 66 patients, 55 were male. The mean age was 36.12 years. All patients sustained blunt trauma, with road traffic injuries being the most common mechanism involving 65 % of the patients followed by fall from height. The mean systolic blood pressure at the time of surgery was 77 + -34.46mm Hg. Median packed red blood cell transfusion in the first 24 hours was 8.5 units with IQR of 6-12. The hemorrhage related mortality was 48%. Conclusion BLIIA with PPP may be considered as a viable treatment option in hemodynamically unstable patients with pelvic injuries in resource constraint facilities

88: Direct access physiotherapy to help manage patients with musculoskeletal disorders in an emergency department: results of a randomized controlled trial
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Posted 29 Oct 2020

Direct access physiotherapy to help manage patients with musculoskeletal disorders in an emergency department: results of a randomized controlled trial
322 downloads medRxiv emergency medicine

Rose Gagnon, Kadija Perreault, Simon Berthelot, Eveline Matifat, François Desmeules, Bertrand Achou, Marie-Christine Laroche, Catherine Van Neste, Stéphane Tremblay, Jean Leblond, Luc J. Hébert

ContextIn several countries, physiotherapists (PT) have been integrated within emergency departments (EDs) to help manage patients with musculoskeletal disorders (MSKDs). Still, research on the effects of such initiatives is scarce. ObjectivesTo evaluate the effects of direct access PT on MSKD patients consulting the ED in terms of clinical outcomes and use of health care resources. Design, Setting, ParticipantsRandomized controlled trial, academic ED in Quebec City (Canada), participants 18-80 years presenting with a minor MSKD. InterventionDirect access PT at the ED ControlEmergency Physicians lead management (EP). Main Outcome MeasureClinical outcomes (pain, interference of pain on function) and use of resources (ED return visit, interventions, diagnostic tests, consultations) were compared between groups at ED discharge and after 1 and 3 months using two-way ANOVAs, log-linear analysis and {chi}2 tests. ResultsSeventy-eight patients suffering from MSKDs were included (40.2 {+/-} 17.6 years old; 44% women). Participants in the PT group (n=40) had statistically lower levels of pain and pain interference at 1- and 3-months. They were recommended fewer imaging tests (38% vs. 78%; p<.0001) and prescription medication (43% vs. 67%; p=.030) at ED discharge, had used less prescription medication (32% vs. 72%; p=.002) and had revisited significantly less often the ED (0% vs. 21%; p=.007) at 1-month than those in the EP group (n=38). At 3 months, the PT group had used less over-the-counter medication (19% vs. 43%; p=.034). ConclusionPatients presenting with a MSKD to the ED with direct access to a PT had better clinical outcomes and used less services and resources than those in the usual care group after ED discharge and up to 3 months after discharge. The results of this study support the implementation of such models of care for the management of this population. Trial RegistrationThis trial is registered at the US National Institutes of Health (ClinicalTrials.gov) #NCT04009369 Ethical approvalThis trial was approved by the Research Ethics Committee of the CHU de Quebec - Universite Laval #MP-20-2019-4307

89: Using explainable machine learning to identifypatients at risk of reattendance at discharge from emergency departments
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Posted 04 Dec 2020

Using explainable machine learning to identifypatients at risk of reattendance at discharge from emergency departments
320 downloads medRxiv emergency medicine

Francis P Chmiel, Dan K Burns, Martin Azor, Florina Borca, Michael J Boniface, Zlatko D Zlatev, Neil M White, Thomas W V Daniels, Michael Kiuber

Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance could help reduce the number of missed critical illnesses and could reduce avoidable utilization of emergency departments by enabling targeted post-discharge intervention. In this manuscript we present a retrospective, single-centre study where we created and evaluated an extreme gradient boosted decision tree model trained to identify pa- tients at risk of reattendance within 72 hours of discharge from an emergency department (University Hospitals Southampton Foundation Trust, UK). Our model was trained using 35,447 attendances by 28,945 patients and evaluated on a hold-out test set featuring 8,847 attendances by 7,237 patients. The set of attendances from a given patient appeared exclusively in either the training or the test set. Our model was trained using both visit level variables (e.g., vital signs, arrival mode, and chief complaint) and a set of variables available in a patients electronic patient record, such as age and any recorded medical conditions. On the hold-out test set, our highest performing model obtained an AUROC of 0.747 (95% CI : 0.722-0.773) and an average precision of 0.233 (95% CI : 0.194-0.277). These results demonstrate that machine-learning models can be used to classify patients, with moderate performance, into low and high-risk groups for reattendance. We explained our models predictions using SHAP values, a concept developed from coalitional game theory, capable of explaining predictions at an attendance level. We demonstrated how clustering techniques can be used to investigate the different sub-groups of explanations present in our patient cohort.

90: Score for Emergency Risk Prediction (SERP): An Interpretable Machine Learning AutoScore-Derived Triage Tool for Predicting Mortality after Emergency Admissions
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Posted 15 Feb 2021

Score for Emergency Risk Prediction (SERP): An Interpretable Machine Learning AutoScore-Derived Triage Tool for Predicting Mortality after Emergency Admissions
320 downloads medRxiv emergency medicine

Feng Xie, Marcus Eng Hock Ong, Johannes Nathaniel Min Hui Liew, Kenneth Boon Kiat Tan, Andrew Fu Wah Ho, Gayathri Devi Nadarajan, Lian Leng Low, Yu Heng Kwan, Benjamin Alan Goldstein, David Bruce Matchar, Bibhas Chakraborty, Nan Liu

ImportanceTriage in the emergency department (ED) for admission and appropriate level of hospital care is a complex clinical judgment based on the tacit understanding of the patients likely acute course, availability of medical resources, and local practices. While a scoring tool could be valuable in triage, currently available tools have demonstrated limitations. ObjectiveTo develop a tool based on a parsimonious list of predictors available early at ED triage, to provide a simple, early, and accurate estimate of short-term mortality risk, the Score for Emergency Risk Prediction (SERP), and evaluate its predictive accuracy relative to published tools. Design, Setting, and ParticipantsWe performed a single-site, retrospective study for all emergency department (ED) patients between January 2009 and December 2016 admitted in a tertiary hospital in Singapore. SERP was derived using the machine learning framework for developing predictive models, AutoScore, based on six variables easily available early in the ED care process. Using internal validation, the SERP was compared to the current triage system, Patient Acuity Category Scale (PACS), Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), Cardiac Arrest Risk Triage (CART), and Charlson Comorbidity Index (CCI) in predicting both primary and secondary outcomes in the study. Main Outcomes and MeasuresThe primary outcome of interest was 30-day mortality. Secondary outcomes include 2-day mortality, inpatient mortality, 30-day post-discharge mortality, and 1-year mortality. The SERPs predictive power was measured using the area under the curve (AUC) in the receiver operating characteristic (ROC) analysis. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated under the optimal threshold, defined as the point nearest to the upper-left corner of the ROC curve. ResultsWe included 224,666 ED episodes in the model training cohort, 56,167 episodes in the validation cohort, and 42,676 episodes in the testing cohort. 18,797 (5.8%) of them died in 30 days after their ED visits. Evaluated on the testing set, SERP outperformed several benchmark scores in predicting 30-day mortality and other mortality-related outcomes. Under cut-off score of 27, SERP achieved a sensitivity of 72.6% (95% confidence interval [CI]: 70.7-74.3%), a specificity of 77.8% (95% CI: 77.5-78.2), a positive predictive value of 15.8% (15.4-16.2%) and a negative predictive value of 98% (97.9-98.1%). ConclusionsSERP showed better prediction performance than existing triage scores while maintaining easy implementation and ease of ascertainment at the ED. It has the potential to be widely applied and validated in different circumstances and healthcare settings. Key pointsO_ST_ABSQuestionC_ST_ABSHow does a tool for predicting hospital outcomes based on a machine learning-based automatic clinical score generator, AutoScore, perform in a cohort of individuals admitted to hospital from the emergency department (ED) compared to other published clinical tools? FindingsThe new tool, the Score for Emergency Risk Prediction (SERP), is parsimonious and point-based. SERP was more accurate in identifying patients who died during short or long-term care, compared with other point-based clinical tools. MeaningSERP, a tool based on AutoScore is promising for triaging patients admitted from the ED according to mortality risk.

91: Epidemiologial Analysis of Patients Presenting to a West London District General Hospital Requiring Admission with Covid-19
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Posted 14 Oct 2020

Epidemiologial Analysis of Patients Presenting to a West London District General Hospital Requiring Admission with Covid-19
319 downloads medRxiv emergency medicine

E Heald, N Ring, D Vatvani, D Shackleton

BackgroundCoronavirus has lead to significant morbidity and mortality both within the UK and worldwide. We hypothesise there are local clusters of coronavirus which would therefore be amenable to targeted public health measures. MethodsThis is a retrospective, observational case series conducted in a West London District General Hospital. All patients admitted to hospital with a radiological or microbiological diagnosis of Covid-19 were included (children under 16 years were excluded). Consecutive sampling was used and baseline characteristics including age, sex, postcode and final patient outcome were collected from the electronic health records. Patient origin postcode was plotted to a map of the local area and an online cloud based mapping analysis system was used to generate heat maps and case density maps which were compared to living base layers. The primary outcome was identification of local clusters of cases of coronavirus. Secondary outcome was identification of population characteristics that may provide evidence for more targetted public health intervention in a second wave. ResultsLocal clusters of infection were identified within the target population. These appeared to correlate with higher indices of deprivation, poorer overall health and high household occupancy suggesting a role for public health measures to target these areas. ConclusionThere is a role for targeted public health measures in tackling the spread of coronavirus, paying particular attention to those who live in more deprived areas.

92: Development and Validation of a Deep Learning Model for Automated View Classification of Pediatric Focused Assessment with Sonography for Trauma (FAST)
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Posted 16 Oct 2020

Development and Validation of a Deep Learning Model for Automated View Classification of Pediatric Focused Assessment with Sonography for Trauma (FAST)
312 downloads medRxiv emergency medicine

Aaron E Kornblith, Newton Addo, Ruolei Dong, Robert Rogers, Jacqueline Grupp-Phelan, Atul J Butte, Pavan Gupta, Rachael A Callcut, Rima Arnaout

The pediatric Focused Assessment with Sonography for Trauma (FAST) is a sequence of ultrasound views rapidly performed by the clinician to diagnose hemorrhage. One limitation of FAST is inconsistent acquisition of required views. We sought to develop a deep learning model and classify FAST views using a heterogeneous dataset of pediatric FAST. This study of diagnostic test developed and tested a deep learning model for view classification of archived real-world pediatric FAST studies collected from two pediatric emergency departments. FAST frames were randomly distributed to training, validation, and test datasets in a 70:20:10 ratio; each patient was represented in only one dataset to maintain sample independence. The outcome was the prediction accuracy of the model in classifying FAST frames and video clips. FAST studies performed by 30 different clinicians from 699 injured children included 4,925 videos representing 1,062,612 frames from children who were a median of 9 years old. On test dataset, the overall view classification accuracy for the model was 93.4% (95% CI: 93.3-93.6) for frames and 97.8% (95% CI: 96.0-99.0) for video clips. Frames were correctly classified with an accuracy of 96.0% (95% CI: 95.9-96.1) for cardiac, 99.8% (95% CI: 99.8-99.8) for thoracic, 95.2% (95% CI: 95.0-95.3) for abdominal upper quadrants, and 95.9% (95% CI: 95.8-96.0) for suprapubic. A deep learning model can be developed to accurately classify pediatric FAST views. Accurate view classification is the important first step to support developing a consistent and accurate multi-stage deep learning model for pediatric FAST interpretation.

93: Assessment of The Relationship of REMS and MEWS Scores with Prognosis in Patients Diagnosed with Covid-19 Admitted to the Emergency Department
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Posted 26 Jan 2021

Assessment of The Relationship of REMS and MEWS Scores with Prognosis in Patients Diagnosed with Covid-19 Admitted to the Emergency Department
302 downloads medRxiv emergency medicine

Behlul Bas, Mucahit Senturk, Tugce Nur Burnaz, Kubilay Timur, Asim Kalkan

Aim: With the rapid and global increase in COVID-19 cases, it is becoming important to identify patients with a risk of mortality and patients that need hospitalization. The aim of this study is to try to predict the mortality rate of COVID patients admitted to the emergency department with rapid scoring systems such as REMS and MEWS and their clinical termination in the emergency department at the end of the first month. Method: We have designed this study to be a single-centered, prospective and an observational study. A total of 392 patients diagnosed with COVID-19, who were admitted to the emergency department in a 1-month period, were included in the study. REMS and MEWS scores were calculated for each case. Demographic data of patients, clinical outcomes such as discharge, service hospitalization, ICU hospitalization, and first-month mortality were analysed based on these scores. ROC curves were analysed to determine the cut-off value with the help of which REMS and MEWS scores can predict 1-month mortality and hospitalization. Results: Out of the 392 patients included in the study, the 43.4% (n=170) were female and 56.6% (n=222) were male. The average age of our patients was 48.98{+/-}19.49 years. The 1-month mortality rate of our patients was 4.3% (n=17). At the end of the first month, the mortality of patients with a comorbid disease was higher than those who did not (p<0.01). The average of the REMS score was higher in patients with an average mortality of (7.24{+/-}3.77) than in patients without it (2.87{+/-}3.09), and there was a statistically significant difference between them (p<0.01). Similarly, the average of the MEWS score was higher in patients with an average mortality of (2.76{+/-}1.86) than in patients without it (1.65{+/-}1.35), and there was a statistically significant difference (p<0.01). The REMS score of patients admitted to the service was higher than that of patients discharged (p<0.01). When the REMS score was determined as 3 cut-off value in ROC analysis, service hospitalization was 5 times higher in patients with a REMS score of 3 and above than in those who were discharged (OR: 1:5.022 95% CI: 3.088-8.168)). REMS and MEWS scores were also higher in ICU patients than in discharged patients (p<0.01). Conclusion: In predicting the 1-month mortality of ER patients diagnosed with COVID-19, REMS and MEWS scoring systems can be useful and guiding in determining the patients who need hospitalization for emergency physicians. The use of these scoring systems in emergency departments can help predict the clinical outcomes of patients at the time of the initial evaluation, and can also be a practical method of predicting the prognosis of the patients.

94: An Initial Psychometric Evaluation of the APS-POQ-R in Acute Pain Presenting to the Emergency Department
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Posted 18 Sep 2020

An Initial Psychometric Evaluation of the APS-POQ-R in Acute Pain Presenting to the Emergency Department
298 downloads medRxiv emergency medicine

James A Hughes, Lee Jones, Joseph Potter, Alixandra Wong, Nathan J Brown, Kevin Chu

Background: Pain is a common presenting complaint to the emergency department (ED), yet is often undertreated. When assessing the outcomes of pain care in the ED, process measures are commonly reported. Attempts to measure patient reported outcomes existing in current ED literature. However, they are frequently unvalidated and lack standardization. The American Pain Societies, Patient Outcome Questionnaire, Revised edition (APS POQ R) has been identified as the most likely, preexisting tool to be useful in the acute pain in the ED. However, this requires feasibility and construct validation before use. Objective: To assess the feasibility and construct validity of the APS POQ R in patients presenting to the adult emergency department with acute pain. Methods: This study is an initial psychometric evaluation of the constructs contained within the APS POQ R in adult patients presenting with moderate to severe acute pain to a large urban ED. The study is guided by the methods described in the initial development of the instrument. Results: Two hundred adult patients were recruited and completed the APS POQ R. The APS POQ R demonstrated content validity in patients presenting with acute pain. Exploratory factor analysis demonstrated five subgroups. The tool demonstrated discriminatory ability based on patient urgency, and subscale measurement was associated with patient satisfaction with care. Conclusions: The APS POQ R has demonstrable construct validity in adult patients presenting with acute pain to the ED. Further psychometric analysis across multiple EDs is required before the APS POQ R can be recommended as a validated PROM for ED patients in pain.

95: A phase I/II trial to treat massive Africanized honeybee (Apis mellifera) stings using the new apilic antivenom
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Posted 03 Jan 2021

A phase I/II trial to treat massive Africanized honeybee (Apis mellifera) stings using the new apilic antivenom
298 downloads medRxiv emergency medicine

Alexandre Naime Barbosa, Rui Seabra Ferreira, Francilene Capel Tavares de Carvalho, Fabiana Schuelter-Trevisol, Mônica Bannwart Mendes, Bruna Cavecci Mendonça, José Nixon Batista, Daisson José Trevisol, Leslie Boyer, Jean-Philippe Chippaux, Natália Bronzatto Medolago, Claudia Vilalva Cassaro, Márcia Tonin Rigotto Carneiro, Ana Paola Piloto de Oliveira, Daniel Carvalho Pimenta, Luís Eduardo Ribeiro da Cunha, Lucilene Delazari dos Santos, Benedito Barraviera

Safety, optimal minimum dose, and, preliminary effectiveness of a new generation Africanized honeybees (Apis mellifera) antivenom (AAV) were evaluated. A phase I/II, multicenter, non- randomized, single-arm clinical trial involving 20 participants showing multiple stings were studied. Participants have received either 2 to 10 vials of AAV based on the stings number together with a predefined adjuvant, symptomatic, and complementary treatment schedule. The primary safety endpoint was the presence of early adverse reactions within the first 24 hours after treatment. Preliminary efficacy through clinical evolution, including laboratory tests, was assessed at baseline and over the following four weeks. ELISA assays and mass spectrometry estimated the venom pharmacokinetics before, during, and after treatment. Twenty adult participants, 13 (65%) males, and 7 (35%) females, with a median age of 44 years and a mean body surface of 1.92 m2 (median = 1.93 m2) were recruited. The median number of stings was 52.5 ranging from 7 to more than 2,000. Envenoming severity was classified as 80% mild, 15% moderate, and 5% severe. According to the protocol, 16 (80%) participants received two AAV vials, 3 (15%) six vials, and one (5%) 10 vials. There was no discontinuation of the treatment due to acute adverse events and there were no late adverse reactions. Two patients showed mild adverse events with only transient itchy skin and erythroderma. All participants completed the infusion within two hours and there was no loss of follow-up after discharge. ELISA assays showed venom concentrations varying between 0.25 ng/mL and 1.479 ng/mL prior to treatment. Venom levels decreased in all cases during the hospitalization period. Surprisingly, in nine cases (45%), despite clinical recovery and without symptoms, the venom levels increased again during outpatient care 10 days after discharge. Mass spectrometry showed melittin in eight participants 30 days after the treatment. Considering the promising safety results of the investigational product for the treatment of massive Africanized honeybee attacks, added to efficacy in clinical improvement and immediate decrease in blood venom level, the AAV has shown to be safe for human use. Trial registrationUniversal Trial Number (UTN): U1111-1160-7011, Register Number: RBR-3fthf8 (http://www.ensaiosclinicos.gov.br/rg/RBR-3fthf8/).

96: How the COVID-19 pandemic has adversely affected the economics of U.S. emergency care
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Posted 14 Dec 2020

How the COVID-19 pandemic has adversely affected the economics of U.S. emergency care
296 downloads medRxiv emergency medicine

Jesse M Pines, Mark S Zocchi, Bernard S. Black, Rebecca Kornas, Pablo Celedon, Ali Moghtaderi, Arvind Venkat

Objective: We describe how the coronavirus (COVID-19) pandemic impacted emergency department (ED) economics, acuity, and staffing. Methods: We conducted an observational study of visits during January to September 2020 compared to 2019 in 136 EDs staffed by a national emergency medicine group. We created ratios of three-week moving averages for 2020 visits, acuity, costs divided by 2019 moving averages, by age and ED size. We tabulated reductions in clinician hours and FTEs compared to early 2020 staffing. Results: 2020-2019 ED visit ratios declined in March nadiring mid-April for both adults (to 0.60) and children (to 0.30) and rose thereafter but remained below 2019 levels through September 2020. The ratio of adult RVUs/visit rose to 1.1 for adults and 1.2 for children in the early pandemic, falling to 1.04 and 1.1 through September. The ratio of direct salary expenses in freestanding (FSED) and small EDs declined less dramatically than in medium and large EDs. Clinical revenues in medium and large EDs declined more sharply and recovered slowly but plateaued well below 2019 levels. By September 2020, expenses were still higher than revenues for small EDs, similar for FSEDs, and somewhat higher for medium and large EDs. During the pandemic, physician hours fell 15% and APP hours 27% during COVID-19 translating to 174 lost physician and 193 lost APP FTEs. Conclusion: The COVID-19 pandemic reduced ED visits and increased acuity in the first 7 months of the pandemic, leading to a contraction of the ED workforce, and threatening ED economics, more so in small and FSEDs.

97: Validating the RISE UP score for predicting prognosis in patients with COVID-19 in the emergency department, a retrospective study
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Posted 24 Nov 2020

Validating the RISE UP score for predicting prognosis in patients with COVID-19 in the emergency department, a retrospective study
295 downloads medRxiv emergency medicine

Paul M.E.L. van Dam, Noortje Zelis, Patricia M. Stassen, Daan J.L. van Twist, Peter W. de Leeuw, Jacqueline Buijs

Objective: To mitigate the burden of COVID-19 on the healthcare system, information on the prognosis of the disease is needed. The recently developed RISE UP score has very good discriminatory value with respect to short-term mortality in older patients in the emergency department (ED). It consists of six items: age, abnormal vital signs, albumin, blood urea nitrogen (BUN), lactate dehydrogenase (LDH), and bilirubin. We hypothesized that the RISE UP score could have discriminatory value with regard to 30-day mortality in ED patients with COVID-19. Design: Retrospective analysis. Setting: Two EDs of the Zuyderland Medical Centre (MC), secondary care hospital in the Netherlands. Participants: The study sample consisted of 642 adult ED patients diagnosed with COVID-19 between March 3rd until May 25th 2020. Inclusion criteria were: 1) admission to the hospital with symptoms suggestive of COVID-19, and 2) positive result of the polymerase chain reaction (PCR), or (very) high suspicion of COVID-19 according to the chest computed tomography (CT) scan. Outcome: Primary outcome was 30-day mortality, secondary outcome was a composite of 30-day mortality and admission to intensive care unit (ICU). Results: Within 30 days after presentation, 167 patients (26.0%) died and 102 patients (15.9%) were admitted to ICU. The RISE UP score showed good discriminatory value with respect to 30-day mortality (AUC 0.77, 95% CI 0.73-0.81), and to the composite outcome (AUC 0.72, 95% CI 0.68-0.76). Patients with RISE UP scores below 10% (121 patients) had favourable outcome (0% mortality and 5% ICU admissions). Patients with a RISE UP score above 30% (221 patients) were at high risk of adverse outcome (46.6% mortality and 19% ICU admissions). Conclusion: The RISE UP score is an accurate prognostic model for adverse outcome in ED patients with COVID-19. It can be used to identify patients at risk of short-term adverse outcome, and may help guiding decision-making and allocating healthcare resources.

98: Study of pre-hospital care of Out of Hospital Cardiac Arrest victims and their outcome in a tertiary care hospital in India: Pre-hospital Cardiac Arrest REsuscitation (Pre-CARE) study
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Posted 16 Jun 2020

Study of pre-hospital care of Out of Hospital Cardiac Arrest victims and their outcome in a tertiary care hospital in India: Pre-hospital Cardiac Arrest REsuscitation (Pre-CARE) study
292 downloads medRxiv emergency medicine

RACHANA BHAT, Prithvishree Ravindra, Ankit Kumar Sahu, ROSHAN MATHEW, WILLIAM WILSON

BACKGROUND: India does not have a formal cardiac arrest registry and a centralized emergency medical system. In this study, we aimed to assess the prehospital care received by the patients with OHCA and to predict the factors that could influence their outcome. METHODS: We performed a prospective observational study, including OHCA patients presenting to the emergency department (ED) between February 2019 and January 2020. A structured proforma was used to capture information like basic demography, prehospital details like bystander cardiopulmonary resuscitation (CPR), use of an automated external defibrillator (AED), clinical profile, and outcome. RESULTS: Among the included 205 patients, the majority were male (71.2%) and belonged to older age (49.3%). The nature of arrest was predominantly non-traumatic (82.4%). The initial rhythm at presentation was non-shockable (96.5%). Return of spontaneous circulation (ROSC) was achieved in 17 (8.3%) patients, of which only 3 (1.4%) patients survived till discharge. The home was the most common location of OHCA (116, 56.6%). Among the OHCA patients, witnessed arrests were seen only in 64 (31.2%), of which 15 (7.8%) received bystander CPR, and AED was used in 1% of the patients. The initial shockable rhythm was a significant predictor of ROSC (OR 18.97 95%CI 3.83-93.89; p<0.001) and survival to discharge (OR 42.67; 95%CI 7.69-234.32; p<0.001). CONCLUSION: In a developing country like India, this study underlines the poor status of the prehospital system like lower bystander CPR, AED and ambulance usage. Moreover, ROSC was seen only in less than 10% of patients, and only 1.3% got discharged from the hospital.

99: Patient Preferences to Undergo Low-Value CT Coronary Angiography in the Emergency Department
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Posted 18 Oct 2019

Patient Preferences to Undergo Low-Value CT Coronary Angiography in the Emergency Department
285 downloads medRxiv emergency medicine

Jessica L. Winkels, Chelsea Morrow Smith, Rahul Iyengar, Arjun P. Meka, Jonathan D. Porath, William Meurer

BackgroundLow-value diagnostic testing adds billions of dollars to the cost of health care in the US annually. Addressing patient preference for these tests is one possible strategy to limit overuse. In previous work, we showed that patient preference for testing can be influenced by test benefit, risk, and financial measures. Our objective was to examine the effect of these variables in another clinical scenario involving chest pain. MethodsIn this cross-sectional survey of patients at the University of Michigan Emergency Department (ED), participants were given a hypothetical scenario involving an ED visit for chest pain, along with information regarding potential benefit (detecting a life-threatening condition; 0.1 or 1%) and risk (developing cancer; 0.1 or 1%) of CTCA, as well as an incentive of $0 or $100 to forego testing. Values for risk, benefit, and financial incentive varied across participants. Our primary outcome was patient preference to undergo testing. We also obtained demographic and numeracy information. Then, we used logistic regression to estimate odds ratios, adjusting for multiple potential confounders. Our sample size was designed to find at least 300 events (test acceptance) to allow for up to 30 covariates in fully adjusted models. We had 85-90% power to detect a 10% absolute difference in testing rate across groups, assuming a 95% significance level. Results913 patients were surveyed. A $100 financial incentive (adjusted OR [AOR] 0.57; 95% Confidence Interval [CI] 0.42-0.78) and increased test risk (AOR 0.61; 95% CI 0.44-0.84) both significantly decreased test acceptance in fully adjusted models, whereas increased test benefit significantly increased test acceptance (AOR 2.45; 95% CI 1.79-3.36). ConclusionsOffering a financial incentive deterred patients from accepting testing despite varying levels of risk and benefit. In the context of previous work, we provide preliminary evidence supporting that financial interventions may impact patient preference more than test risk and benefit.

100: Deep-Learning Approaches to Identify Critically Ill Patients at Emergency Department Triage Using Limited Information
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Posted 06 May 2020

Deep-Learning Approaches to Identify Critically Ill Patients at Emergency Department Triage Using Limited Information
284 downloads medRxiv emergency medicine

Joshua W Joseph, Evan L Leventhal, Anne V Grossestreuer, Matthew L Wong, Loren J Joseph, Larry A Nathanson, Michael W Donnino, Noémie Elhadad, Leon D Sanchez

Importance Triage quickly identifies critically ill patients, helping to facilitate timely interventions. Many emergency departments use the emergency severity index (ESI) or abnormal vital sign thresholds to identify critically ill patients. However, both rely on fixed thresholds, and false activations detract from efficient care. Prior research suggests that machine-learning approaches may improve triage accuracy, but have relied on information that is often unavailable during the triage process. Objective We examined whether deep-learning approaches could identify critically ill patients using data immediately available at triage with greater discriminative power than ESI or abnormal vital sign thresholds. Design Retrospective, cross-sectional study. Setting An urban tertiary care hospital in the Northeastern United States. Participants Adult patients presenting to the emergency department from 1/1/2012 - 1/1/2020 were included. Deidentified triage information included structured data (age, sex, initial vital signs, ESI score, and clinical trigger activation due to abnormal vital signs), and textual data (chief complaint) with critical illness (defined as mortality or ICU admission within 24 hours) as the outcome. Interventions Three progressively complex deep-learning models were trained (logistic regression on structured data, neural network on structured data, and neural network on structured and textual data), and applied to triage information from all patients. Main Outcomes and Measures The primary outcome was the accuracy of the model for predicting whether patients were critically ill using area under the receiver-operator curve (AUC), as compared to ESI, utilizing a 10-fold cross-validation. Results 445,925 patients were included, with 60,901 (13.7%) critically ill. Vital sign thresholds identified critically ill patients with AUC 0.521 (95% CI 0.519 -- 0.522), and ESI less than 3 demonstrated AUC 0.672 (95% CI 0.671 -- 0.674), logistic regression classified patients with AUC 0.803 (95% CI 0.802 -- 0.804), neural network with structured data with 0.811 (95% CI 0.807 - 0.815), and the neural network model with textual data with AUC 0.851 (95% CI 0.849 -- 0.852). Conclusions and Relevance Deep-learning techniques represent a promising method of enhancing the triage process, even when working from limited information. Further research is needed to determine if improved predictions can be translated into meaningful clinical and operational benefits.

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