Publications

BACKGROUND: Adequate analgesia is difficult to achieve in patients with an abscess requiring incision and drainage (I&D). There has been a recent increase in regional anesthesia use in the emergency department (ED) to aid in acute musculoskeletal pain relief. Specifically, transgluteal sciatic nerve (TGSN) block has been used as an adjunct treatment for certain chronic lumbar and lower extremity pain syndromes in the ED.

CASE REPORT: A 21-year-old woman presented to the ED with a painful gluteal abscess. The pain was so severe that the patient barely tolerated light palpation to the abscess area. Using dynamic ultrasound guidance, a TGSN block was performed with significant pain reduction. Ultrasonographic confirmation of abscess was obtained followed by definitive I&D. She was discharged from the ED and her incision site was healing well at the time of follow-up. WHY SHOULD AN EMERGENCY PHYSICIAN BE AWARE OF THIS?: Abscess I&D is a common procedure in the ED. Procedural analgesia for I&D can be difficult to obtain. We describe the TGSN block as an additional analgesic option to be used for procedural analgesia. The use of regional anesthesia has the potential to decrease unwanted and at times dangerous side effects of opiate use and resource utilization of procedural sedation while optimizing patient comfort.

Shoulder pain is a common and painful patient condition. Unfortunately, diagnostic imaging of shoulder pain in the emergency department (ED) is often limited to radiography. While diagnostic for fractures and dislocations, drawbacks of radiography include time delays and non-diagnostic imaging in the case of rotator cuff pathology. While bedside ultrasound has been incorporated into many procedural and diagnostic applications in the ED, its use for musculoskeletal complaints and specifically shoulder pain is infrequent among ED clinicians. The incorporation of shoulder ultrasound in the ED may improve diagnostic certainty while decreasing time to diagnosis and treatment, yielding patient and health system benefits. Herein, we present the ABSIS (Acromio-clavicular joint, Biceps, Subscapularis, Impingement, Supraspinatus) Protocol for performing bedside ultrasound of the shoulder including the rotator cuff and bony anatomy.

BACKGROUND: Acute pain is one of the most common complaints encountered in the emergency department (ED). Single-injection peripheral nerve blocks are a safe and effective pain management tool when performed in the ED. Dexamethasone has been explored as an adjuvant to prolong duration of analgesia from peripheral nerve blocks in peri- and postoperative settings; however, data surrounding the use of dexamethasone for ED-performed nerve blocks are lacking.

CASE SERIES: In this case series we discuss our experience with adjunctive perineural dexamethasone in ED-performed regional anesthesia. Why Should an Emergency Physician be Aware of This?: Nerve blocks performed with adjuvant perineural dexamethasone may be a safe additive to provide analgesia beyond the expected half-life of local anesthetic alone. Prospective studies exploring the role of adjuvant perineural dexamethasone in ED-performed nerve blocks are needed. © 2021 Elsevier Inc.

The practice of ultrasound-guided regional anesthesia (UGRA) by emergency medicine physicians in the emergency department (ED) is increasing. The need for effective alternatives to opioid analgesia in the acute care setting likely exceeds the current capacity of UGRA-trained anesthesia teams. In this daring discourse, we outline several matters of relevance to be considered as protocols are put into place to facilitate the practice of UGRA by emergency medicine physicians in the ED. There are opportunities for collaboration between anesthesiology and emergency medicine societies in guideline development as well as educational resources. The sustained interest in UGRA shown by many emergency medicine physicians should be viewed open-mindedly by anesthesiologists. Failure to collaborate on local and national scales could lead to delays in the development and implementation of patient-centered, safe procedural care, and limit patient access to the benefits of regional anesthesia.

In patients with influenza, cardiac and lung ultrasound may help determine the severity of illness and predict clinical outcomes. To determine the ultrasound characteristics of influenza and define the spectrum of lung and cardiac findings in patients with suspected influenza A or B, we conducted a prospective observational study in patients presenting to the emergency department at a tertiary care academic institution. An ultrasound protocol consisting of cardiac, lung and inferior vena cava scans was performed within 6 h of admission. We compared the ultrasound findings in cases with positive and negative influenza polymerase chain reaction, while controlling for comorbidities. We enrolled 117 patients, 41.9% of whom (49/117) tested positive for influenza. In those with influenza, ultrasound confirmed preserved left ventricular and right ventricular (RV) function in 81.3% of patients. The most common cardiac pathology was RV dilation (10.4%), followed by left ventricular systolic dysfunction (8.3%). Patients with negative influenza polymerase chain reaction with RV dysfunction demonstrated higher hospital admission than those those with normal RV function (45.1%, 23/51, vs. 17.9%, 5/28; p = 0.016). B-lines were prevalent in both influenza and non-influenza groups (40.8% and 69.1%, respectively; p = 0.013). Lung consolidation was identified in only 8.25% of patients with influenza. In conclusion, in patients with influenza we were unable to define distinct ultrasound features specific to influenza A or B, suggesting that ultrasound may not be beneficial in diagnosing influenza nor in evaluating its severity.

OBJECTIVE: Linking emergency medical services (EMS) electronic patient care reports (ePCRs) to emergency department (ED) records can provide clinicians access to vital information that can alter management. It can also create rich databases for research and quality improvement. Unfortunately, previous attempts at ePCR and ED record linkage have had limited success. In this study, we use supervised machine learning to derive and validate an automated record linkage algorithm between EMS ePCRs and ED records.

MATERIALS AND METHODS: All consecutive ePCRs from a single EMS provider between June 2013 and June 2015 were included. A primary reviewer matched ePCRs to a list of ED patients to create a gold standard. Age, gender, last name, first name, social security number, and date of birth were extracted. Data were randomly split into 80% training and 20% test datasets. We derived missing indicators, identical indicators, edit distances, and percent differences. A multivariate logistic regression model was trained using 5-fold cross-validation, using label k-fold, L2 regularization, and class reweighting.

RESULTS: A total of 14 032 ePCRs were included in the study. Interrater reliability between the primary and secondary reviewer had a kappa of 0.9. The algorithm had a sensitivity of 99.4%, a positive predictive value of 99.9%, and an area under the receiver-operating characteristic curve of 0.99 in both the training and test datasets. Date-of-birth match had the highest odds ratio of 16.9, followed by last name match (10.6). Social security number match had an odds ratio of 3.8.

CONCLUSIONS: We were able to successfully derive and validate a record linkage algorithm from a single EMS ePCR provider to our hospital EMR.

INTRODUCTION: Transfers of skilled nursing facility (SNF) residents to emergency departments (ED) are linked to morbidity, mortality and significant cost, especially when transfers result in hospital admissions. This study investigated an alternative approach for emergency care delivery comprised of SNF-based telemedicine services provided by emergency physicians (EP). We compared this on-site emergency care option to traditional ED-based care, evaluating hospital admission rates following care by an EP.

METHODS: We conducted a retrospective, observational study of SNF residents who underwent emergency evaluation between January 1, 2017-January 1, 2018. The intervention group was comprised of residents at six urban SNFs in the Northeastern United States, who received an on-demand telemedicine service provided by an EP. The comparison group consisted of residents of SNFs that did not offer on-demand services and were transferred via ambulance to the ED. Using electronic health record data from both the telemedicine and ambulance transfers, our primary outcome was the odds ratio (OR) of a hospital admission. We also conducted a subanalysis examining the same OR for the three most common chronic disease-related presentations found among the telemedicine study population.

RESULTS: A total of 4,606 patients were evaluated in both the SNF-based intervention and ED-based comparison groups (n=2,311 for SNF based group and 2,295 controls). Patients who received the SNF-based acute care were less likely to be admitted to the hospital compared to patients who were transferred to the ED in our primary and subgroup analyses. Overall, only 27% of the intervention group was transported to the ED for additional care and presumed admission, whereas 71% of the comparison group was admitted (OR for admission = 0.15 [9% confidence interval, 0.13-0.17]).

CONCLUSION: The use of an EP-staffed telemedicine service provided to SNF residents was associated with a significantly lower rate of hospital admissions compared to the usual ED-based care for a similarly aged population of SNF residents. Providing SNF-based care by EPs could decrease costs associated with hospital-based care and risks associated with hospitalization, including cognitive and functional decline, nosocomial infections, and falls.

STUDY OBJECTIVE: Triage quickly identifies critically ill patients, facilitating timely interventions. Many emergency departments (EDs) use emergency severity index (ESI) or abnormal vital sign triggers to guide triage. However, both use fixed thresholds, and false activations are costly. Prior approaches using machinelearning have relied on information that is often unavailable during the triage process. We examined whether deep-learning approaches could identify critically ill patients only using data immediately available at triage.

METHODS: We conducted a retrospective, cross-sectional study at an urban tertiary care center, from January 1, 2012-January 1, 2020. De-identified triage information included structured (age, sex, initial vital signs) and textual (chief complaint) data, with critical illness (mortality or ICU admission within 24 hours) as the outcome. Four progressively complex deep-learning models were trained and applied to triage information from all patients. We compared the accuracy of the models against ESI as the standard diagnostic test, using area under the receiver-operator curve (AUC).

RESULTS: A total of 445,925 patients were included, with 60,901 (13.7%) critically ill. Vital sign thresholds identified critically ill patients with AUC 0.521 (95% confidence interval [CI] = 0.519-0.522), and ESI <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), 2-layer neural network with structured data with AUC 0.811 (95% CI = 0.807-0.815), gradient tree boosting with AUC 0.820 (95% CI = 0.818-0.821), and the neural network model with textual data with AUC 0.851 (95% CI = 0.849-0.852). All successive increases in AUC were statistically significant.

CONCLUSION: Deep-learning techniques represent a promising method of augmenting triage, even with limited information. Further research is needed to determine if improved predictions yield clinical and operational benefits.

Medical documentation is one of the primary methods by which physicians share clinical information and impressions over time with one another. As the adage says, "a picture is worth a thousand words," and physicians have started leveraging consumer mobile technology to share images with one another. However, image sharing often uses short message service texting and similar methods, which can be noncompliant with privacy regulations and can also limit the ability to communicate information longitudinally and across specialties. Sharing of such information is increasingly important, however, as smaller practices are joining to create large geographically spread out health care networks. To this end, we developed an application to acquire and store images via smartphone and seamlessly transfer into the patient's electronic medical record (EMR) to enable digital consults and longitudinal evaluation in a private and compliant method.

BACKGROUND: Studies have shown the value of CT brain imaging in adults with first-time seizures, but there are no recommendations regarding emergent brain CTs in persons with an established seizure disorders. Our study aimed to derive a clinical decision instrument (CDI) to determine which patients with status epilepticus (SE) require emergent brain imaging.

METHODS: This was a retrospective chart review of patients who presented to our emergency department with SE between 2010 and 2018. Patients with first-time seizures were excluded. A priori, we defined high risk criteria for emergent imaging as well as positive findings on brain CT. High risk criteria included known malignancy, trauma, and immunosuppression. Positive CT scans included findings such as intracranial hemorrhage (ICH) and mass.

RESULTS: We identified 214 patients who met inclusion criteria Of the 181 patients without high risk criteria, 3% had positive CT scans. Of the 33 patients with high risk criteria, 10% had positive CT scans. The sensitivity, specificity, PPV, and NPV for our initial CDI were 38%, 85%, 9%, and 97%. Adding the criterion of prior ICH would have lowered our miss rate to 0.6%. Modifying our CDI to 1) History of ICH, 2) Malignancy, 3) Immunosuppression, and 4) Trauma would result in a CDI with sensitivity, specificity, PPV, and NPV of 87.5%, 87.4%, 21.2%, and 99.5%.

CONCLUSIONS: By using four criteria to identify high risk patients, we can defer CT scanning in the vast majority of patients with SE and known seizure disorders. This CDI should be prospectively validated before adoption.