Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis.

BACKGROUND: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality. OBJECTIVE: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data. METHODS: In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips. RESULTS: Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance. CONCLUSIONS: Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems.

Artificial Intelligence to Determine Fetal Sex.

OBJECTIVE: This proof-of-concept study assessed how confidently an artificial intelligence (AI) model can determine the sex of a fetus from an ultrasound image. STUDY DESIGN: Analysis was performed using 19,212 ultrasound image slices from a high-volume fetal sex determination practice. This dataset was split into a training set (11,769) and test set (7,443). A computer vision model was trained using a transfer learning approach with EfficientNetB4 architecture as base. The performance of the computer vision model was evaluated on the hold out test set. Accuracy, Cohen's Kappa and Multiclass Receiver Operating Characteristic area under the curve (AUC) were used to evaluate the performance of the model. RESULTS: The AI model achieved an Accuracy of 88.27% on the holdout test set and a Cohen's Kappa score 0.843. The ROC AUC score for Male was calculated to be 0.896, for Female a score of 0.897, for Unable to Assess a score of 0.916, and for Text Added a score of 0.981 was achieved. CONCLUSION: This novel AI model proved to have a high rate of fetal sex capture that could be of significant use in areas where ultrasound expertise is not readily available. KEY POINTS: · This is the first proof-of-concept AI model to determine fetal sex.. · This study adds to the growing research in ultrasound AI.. · Our findings demonstrate AI integration into obstetric care..

Ultrasound-guided nerve blocks in emergency medicine practice: 2022 updates.

OBJECTIVES: In the Emergency Department (ED), ultrasound-guided nerve blocks (UGNBs) have become a cornerstone of multimodal pain regimens. We investigated current national practices of UGNBs across academic medical center EDs, and how these trends have changed over time. METHODS: We conducted a cross-sectional electronic survey of academic EDs with ultrasound fellowships across the United States. Twenty-item questionnaires exploring UGNB practice patterns, training, and complications were distributed between November 2021-June 2022. Data was manually curated, and descriptive statistics were performed. The survey results were then compared to results from Amini et al. 2016 UGNB survey to identify trends. RESULTS: The response rate was 80.5% (87 of 108 programs). One hundred percent of responding programs perform UGNB at their institutions, with 29% (95% confidence interval (CI), 20%-39%) performing at least 5 blocks monthly. Forearm UGNB are most commonly performed (96% of programs (95% CI, 93%-100%)). Pain control for fractures is the most common indication (84%; 95% CI, 76%-91%). Eighty-five percent (95% CI, 77%-92%) of programs report at least 80% of UGNB performed are effective. Eighty-five percent (95% CI, 66%-85%) of programs have had no reported complications from UGNB performed by emergency providers at their institution. The remaining 15% (95% CI, 8%-23%) report an average of 1 complication annually. CONCLUSIONS: All programs participating in our study report performing UGNB in their ED, which is a 16% increase over the last 5 years. UGNB's are currently performed safely and effectively in the ED, however practice improvements can still be made. Creating multi-disciplinary committees at local and national levels can standardize guidelines and practice policies to optimize patient safety and outcomes.

The prognostic value of improving congestion on lung ultrasound during treatment for acute heart failure differs based on patient characteristics at admission.

BACKGROUND: Lung ultrasound congestion scoring (LUS-CS) is a congestion severity biomarker. The BLUSHED-AHF trial demonstrated feasibility for LUS-CS-guided therapy in acute heart failure (AHF). We investigated two questions: 1) does change (∆) in LUS-CS from emergency department (ED) to hospital-discharge predict patient outcomes, and 2) is the relationship between in-hospital decongestion and adverse events moderated by baseline risk-factors at admission? METHODS: We performed a secondary analysis of 933 observations/128 patients from 5 hospitals in the BLUSHED-AHF trial receiving daily LUS. ∆LUS-CS from ED arrival to inpatient discharge (scale -160 to +160, where negative = improving congestion) was compared to a primary outcome of 30-day death/AHF-rehospitalization. Cox regression was used to adjust for mortality risk at admission [Get-With-The-Guidelines HF risk score (GWTG-RS)] and the discharge LUS-CS. An interaction between ∆LUS-CS and GWTG-RS was included, under the hypothesis that the association between decongestion intensity (by ∆LUS-CS) and adverse outcomes would be stronger in admitted patients with low-mortality risk but high baseline congestion. RESULTS: Median age was 65 years, GWTG-RS 36, left ventricular ejection fraction 36 %, and ∆LUS-CS -20. In the multivariable analysis ∆LUS-CS was associated with event-free survival (HR = 0.61; 95 % CI: 0.38-0.97), while discharge LUS-CS (HR = 1.00; 95%CI: 0.54-1.84) did not add incremental prognostic value to ∆LUS-CS alone. As GWTG-RS rose, benefits of LUS-CS reduction attenuated (interaction p < 0.05). ∆LUS-CS and event-free survival were most strongly correlated in patients without tachycardia, tachypnea, hypotension, hyponatremia, uremia, advanced age, or history of myocardial infarction at ED/baseline, and those with low daily loop diuretic requirements. CONCLUSIONS: Reduction in ∆LUS-CS during AHF treatment was most associated with improved readmission-free survival in heavily congested patients with otherwise reassuring features at admission. ∆LUS-CS may be most useful as a measure to ensure adequate decongestion prior to discharge, to prevent early readmission, rather than modify survival.

Clinical Features vs CT Findings to Estimate Need for Surgery in Small Bowel Obstruction.

This comparative effectiveness research compares the ability of clinical features vs clinical features with computed tomography (CT) to estimate the need for small bowel obstruction surgery.

Implementing ultrasound-guided nerve blocks in the emergency department: A low-cost, low-fidelity training approach.

Background: Managing acute pain is a common challenge in the emergency department (ED). Though widely used in perioperative settings, ED-based ultrasound-guided nerve blocks (UGNBs) have been slow to gain traction. Here, we develop a low-cost, low-fidelity, simulation-based training curriculum in UGNBs for emergency physicians to improve procedural competence and confidence. Methods: In this pre-/postintervention study, ED physicians were enrolled to participate in a 2-h, in-person simulation training session composed of a didactic session followed by rotation through stations using handmade pork-based UGNB models. Learner confidence with performing and supervising UGNBs as well as knowledge and procedural-based competence were assessed pre- and posttraining via electronic survey quizzes. One-way repeated-measures ANOVAs and pairwise comparisons were conducted. The numbers of nerve blocks performed clinically in the department pre- and postintervention were compared. Results: In total, 36 participants enrolled in training sessions, eight participants completed surveys at all three data collection time points. Of enrolled participants, 56% were trainees, 39% were faculty, 56% were female, and 53% self-identified as White. Knowledge and competency scores increased immediately postintervention (mean ± SD t0 score 66.9 ± 8.9 vs. t1 score 90.4 ± 11.7; p < 0.001), and decreased 3 months postintervention but remained elevated above baseline (t2 scores 77.2 ± 11.5, compared to t0; p = 0.03). Self-reported confidence in performing UGNBs increased posttraining (t0 5.0 ± 2.3 compared to t1 score 7.1 ± 1.5; p = 0.002) but decreased to baseline levels 3 months postintervention (t2 = 6.0 ± 1.9, compared to t0; p = 0.30). Conclusions: A low-cost, low-fidelity simulation curriculum can improve ED provider procedural-based competence and confidence in performing UGNBs in the short term, with a trend toward sustained improvement in knowledge and confidence. Curriculum adjustments to achieve sustained improvement in confidence performing and supervising UGNBs long term are key to increased ED-based UGNB use.

POCUS-first for nephrolithiasis: A Monte Carlo simulation illustrating cost savings, LOS reduction, and preventable radiation.

OBJECTIVES: Non-contrast computed tomography (NCCT) is the gold standard for nephrolithiasis evaluation in the emergency department (ED). However, Choosing Wisely guidelines recommend against ordering NCCT for patients with suspected nephrolithiasis who are <50 years old with a history of kidney stones. Our primary objective was to estimate the national annual cost savings from using a point-of-care ultrasound (POCUS)-first approach for patients with suspected nephrolithiasis meeting Choosing Wisely criteria. Our secondary objectives were to estimate reductions in ED length of stay (LOS) and preventable radiation exposure. METHODS: We created a Monte Carlo simulation using available estimates for the frequency of ED visits for nephrolithiasis and eligibility for a POCUS-first approach. The study population included all ED patients diagnosed with nephrolithiasis. Based on 1000 trials of our simulation, we estimated national cost savings in averted advanced imaging from this strategy. We applied the same model to estimate the reduction in ED LOS and preventable radiation exposure. RESULTS: Using this model, we estimate a POCUS-first approach for evaluating nephrolithiasis meeting Choosing Wisely guidelines to save a mean (±SD) of $16.5 million (±$2.1 million) by avoiding 159,000 (±18,000) NCCT scans annually. This resulted in a national cumulative decrease of 166,000 (±165,000) annual bed-hours in ED LOS. Additionally, this resulted in a national cumulative reduction in radiation exposure of 1.9 million person-mSv, which could potentially prevent 232 (±81) excess cancer cases and 118 (±43) excess cancer deaths annually. CONCLUSION: If adopted widely, a POCUS-first approach for suspected nephrolithiasis in patients meeting Choosing Wisely criteria could yield significant national cost savings and a reduction in ED LOS and preventable radiation exposure. Further research is needed to explore the barriers to widespread adoption of this clinical workflow as well as the benefits of a POCUS-first approach in other patient populations.

Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound.

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F 1-score of 0.65, performing on par with the inter-observer agreement. The dataset and developed methods can facilitate further biomedical research on automated interpretation of lung ultrasound with the potential to expand the clinical utility.

Comparison of pulmonary congestion severity using artificial intelligence-assisted scoring versus clinical experts: A secondary analysis of BLUSHED-AHF.

AIM: Acute decompensated heart failure (ADHF) is the leading cause of cardiovascular hospitalizations in the United States. Detecting B-lines through lung ultrasound (LUS) can enhance clinicians' prognostic and diagnostic capabilities. Artificial intelligence/machine learning (AI/ML)-based automated guidance systems may allow novice users to apply LUS to clinical care. We investigated whether an AI/ML automated LUS congestion score correlates with expert's interpretations of B-line quantification from an external patient dataset. METHODS AND RESULTS: This was a secondary analysis from the BLUSHED-AHF study which investigated the effect of LUS-guided therapy on patients with ADHF. In BLUSHED-AHF, LUS was performed and B-lines were quantified by ultrasound operators. Two experts then separately quantified the number of B-lines per ultrasound video clip recorded. Here, an AI/ML-based lung congestion score (LCS) was calculated for all LUS clips from BLUSHED-AHF. Spearman correlation was computed between LCS and counts from each of the original three raters. A total of 3858 LUS clips were analysed on 130 patients. The LCS demonstrated good agreement with the two experts' B-line quantification score (r = 0.894, 0.882). Both experts' B-line quantification scores had significantly better agreement with the LCS than they did with the ultrasound operator's score (p < 0.005, p < 0.001). CONCLUSION: Artificial intelligence/machine learning-based LCS correlated with expert-level B-line quantification. Future studies are needed to determine whether automated tools may assist novice users in LUS interpretation.

Diaphragmatic Excursion as a Novel Objective Measure of Serratus Anterior Plane Block Efficacy: A Case Series.

INTRODUCTION: Pain scales are often used in peripheral nerve block studies but are problematic due to their subjective nature. Ultrasound-measured diaphragmatic excursion is an easily learned technique that could provide a much-needed objective measure of pain control over time with serial measurements. CASE SERIES: We describe three cases where diaphragmatic excursion was used as an objective measure of decreased pain and improved respiratory function after serratus anterior plane block in emergency department patients with anterior or lateral rib fractures. CONCLUSION: Diaphragmatic excursion may be an ideal alternative to pain scores to evaluate serratus anterior plane block efficacy. More data will be needed to determine whether this technique can be applied to other ultrasound-guided nerve blocks.

Novice-performed point-of-care ultrasound for home-based imaging.

Patient-performed point-of-care ultrasound (POCUS) may be feasible for use in home-based healthcare. We investigated whether novice users can obtain lung ultrasound (LUS) images via self-scanning with similar interpretability and quality as experts. Adult participants with no prior medical or POCUS training, who were capable of viewing PowerPoint slides in their home and who could hold a probe to their chest were recruited. After training, volunteers self-performed 8-zone LUS and saved images using a hand-held POCUS device in their own home. Each 8-zone LUS scan was repeated by POCUS experts. Clips were independently viewed and scored by POCUS experts blinded to performing sonographers. Quality and interpretability scores of novice- and expert-obtained LUS images were compared. Thirty volunteers with average age of 42.8 years (Standard Deviation (SD) 15.8), and average body mass index of 23.7 (SD 3.1) were recruited. Quality of novice and expert scans did not differ (median score 2.6, interquartile range (IQR) 2.3-2.9 vs. 2.8, IQR 2.3-3.0, respectively p = 0.09). Individual zone quality also did not differ (P > 0.05). Interpretability of LUS was similar between expert and novice scanners (median 7 zones interpretable, IQR 6-8, for both groups, p = 0.42). Interpretability of novice-obtained scans did not differ from expert scans (median 7 out of 8 zones, IQR 6-8, p = 0.42). Novice-users can self-obtain interpretable, expert-quality LUS clips with minimal training. Patient-performed LUS may be feasible for outpatient home monitoring.

Optimizing Lung Ultrasound: The Effect of Depth, Gain and Focal Position on Sonographic B-Lines.

Ultrasonographic B-lines are artifacts present in alveolar-interstitial syndromes. We prospectively investigated optimal depth, gain, focal position and transducer type for B-line visualization and image quality. B-Lines were assessed at a single rib interspace with curvilinear and linear transducers. Video clips were acquired by changing parameters: depth (6, 12, 18 and 24 cm for curvilinear transducer, 4 and 8 cm for linear transducer), gain (10%, 50% and 90%) and focal position (at the pleural line or half the scanning depth). Clips were scored for B-lines and image quality. Five hundred sixteen clips were obtained and analyzed. The curvilinear transducer improved B-line visualization (63% vs. 37%, p < 0.0001), with higher image quality (3.52 ± 0.71 vs. 3.31 ± 0.86, p = 0.0047) compared with the linear transducer. B-Lines were better visualized at higher gains (curvilinear: gain of 50% vs. 10%, odds ratio = 7.04, 95% confidence interval: 4.03-12.3; gain of 90% vs. 10%, odds ratio = 9.48, 95% confidence interval: 5.28-17.0) and with the focal point at the pleural line (odds ratio = 1.64, 95% confidence interval: 1.02-2.63). Image quality was highest at 50% gain (p = 0.02) but decreased at 90% gain (p < 0.0001) and with the focal point at the pleural line (p < 0.0001). Image quality was highest at depths of 12-18 cm. B-Lines are best visualized using a curvilinear transducer with at least 50% gain and focal position at the pleural line. Gain less than 90% and image depth between 12 and 18 cm improve image quality.

The Effect of Noise-Masking Earbuds (SleepBuds) on Reported Sleep Quality and Tension in Health Care Shift Workers: Prospective Single-Subject Design Study.

BACKGROUND: Shift work is associated with sleep disorders, which impair alertness and increase risk of chronic physical and mental health disease. In health care workers, shift work and its associated sleep loss decrease provider wellness and can compromise patient care. Pharmacological sleep aids or substances such as alcohol are often used to improve sleep with variable effects on health and well-being. OBJECTIVE: We tested whether use of noise-masking earbuds can improve reported sleep quality, sleepiness, and stress level in health care shift workers, and increase alertness and reaction time post night shift. METHODS: Emergency medicine resident physicians were recruited for a prospective, single-subject design study. Entrance surveys on current sleep habits were completed. For 14 days, participants completed daily surveys reporting sleep aid use and self-rated perceived sleepiness, tension level, and last nights' sleep quality using an 8-point Likert scale. After overnight shifts, 3-minute psychomotor vigilance tests (PVT) measuring reaction time were completed. At the end of 14 days, participants were provided noise-masking earbuds, which they used in addition to their baseline sleep regimens as they were needed for sleep for the remainder of the study period. Daily sleep surveys, post-overnight shift PVT, and earbud use data were collected for an additional 14 days. A linear mixed effects regression model was used to assess changes in the pre- and postintervention outcomes with participants serving as their own controls. RESULTS: In total, 36 residents were recruited, of whom 26 participants who completed daily sleep surveys and used earbuds at least once during the study period were included in the final analysis. The median number of days of earbud use was 5 (IQR 2-9) days of the available 14 days. On days when residents reported earbud use, previous nights' sleep quality increased by 0.5 points (P<.001, 95% CI 0.23-0.80), daily sleepiness decreased by 0.6 points (P<.001, 95% CI -0.90 to -0.34), and total daily tension decreased by 0.6 points (P<.001, 95% CI -0.81 to -0.32). These effects were more pronounced in participants who reported worse-than-average preintervention sleep scores. CONCLUSIONS: Nonpharmacological noise-masking interventions such as earbuds may improve daily sleepiness, tension, and perceived sleep quality in health care shift workers. Larger-scale studies are needed to determine this interventions' effect on other populations of shift workers' post-night shift alertness, users' long-term physical and mental health, and patient outcomes.

Point-of-Care Lung Ultrasound for Differentiating COVID-19 From Influenza.

Background and objectives Patients infected with influenza and COVID-19 exhibit similar clinical presentations; thus, a point-of-care test to differentiate between the diseases is needed. Here, we sought to identify features of point-of-care lung ultrasound (LUS) that can discriminate between influenza and COVID-19. Methods In this prospective, cross-sectional study, LUS clips of patients presenting to the emergency department (ED) with viral-like symptoms were collected via a 10-zone scanning protocol. Deidentified clips were interpreted by emergency ultrasound fellows blinded to patients' clinical context and influenza or COVID-19 diagnosis. Modified Soldati scores were calculated for each lung zone. Logistic regression identified the association of pulmonary pathologies with each disease. Results Ultrasound fellows reviewed LUS clips from 165 patients, of which 30.9% (51/165) had confirmed influenza, 33.9% (56/165) had confirmed COVID-19, and 35.1% (58/165) had neither disease. Patients with COVID-19 were more likely to have irregular pleura and B-lines in all lung zones (p<0.01). The median-modified Soldati score for influenza patients was 0/20 (IQR 0-2), 9/20 (IQR 2.5-15.5) for COVID-19 patients, and 2/20 (IQR 0-8) for patients with neither disease (p<0.0001). In multivariate regression analysis adjusted for age, sex, and congestive heart failure (CHF), the presence of B-lines (OR = 1.29, 95% CI 1.09-1.53) was independently associated with COVID-19 diagnosis. The presence of pleural effusion was inversely associated with COVID-19 (OR = 0.09, 95% CI 0.01-0.65). Conclusions LUS may help providers preferentially identify the presence of influenza versus COVID-19 infection both visually and by calculating a modified Soldati score. Further studies assessing the utility of LUS in differentiating viral illnesses in patients with variable illness patterns and those with variable illness severity are warranted.

Perineural Dexamethasone as a Peripheral Nerve Block Adjuvant in the Emergency Department: A Case Series.

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.

Lung ultrasound in predicting COVID-19 clinical outcomes: A prospective observational study.

STUDY OBJECTIVE: We sought to determine the ability of lung point-of-care ultrasound (POCUS) to predict mechanical ventilation and in-hospital mortality in patients with coronavirus disease 2019 (COVID-19). METHODS: This was a prospective observational study of a convenience sample of patients with confirmed COVID-19 presenting to 2 tertiary hospital emergency departments (EDs) in Iran between March and April 2020. An emergency physician attending sonographer performed a 12-zone bilateral lung ultrasound in all patients. Research associates followed the patients on their clinical course. We determined the frequency of positive POCUS findings, the geographic distribution of lung involvement, and lung severity scores. We used multivariable logistic regression to associate lung POCUS findings with clinical outcomes. RESULTS: A total of 125 patients with COVID-like symptoms were included, including 109 with confirmed COVID-19. Among the included patients, 33 (30.3%) patients were intubated, and in-hospital mortality was reported in 19 (17.4%). Lung POCUS findings included pleural thickening 95.4%, B-lines 90.8%, subpleural consolidation 86.2%, consolidation 46.8%, effusions 19.3%, and atelectasis 18.3%. Multivariable logistic regression incorporating binary and scored POCUS findings were able to identify those at highest risk for need of mechanical ventilation (area under the curve 0.80) and in-hospital mortality (area under the curve 0.87). In the binary model ultrasound (US) findings in the anterior lung fields were significantly associated with a need for intubation and mechanical ventilation (odds ratio [OR] 3.67; 0.62-21.6). There was an inverse relationship between mortality and posterior lung field involvement (OR 0.05; 0.01-0.23; and scored OR of 0.57; 0.40-0.82). Anterior lung field involvement was not associated with mortality. CONCLUSIONS: In patients with COVID-19, the anatomic distribution of findings on lung ultrasound is associated with outcomes. Lung POCUS-based models may help clinicians to identify those patients with COVID-19 at risk for clinical deterioration.Key Words: COVID-19; Lung Ultrasound; Mechanical ventilation; Prediction; ICU admission; Mortality; Clinical outcome; Risk stratification; Diagnostic accuracy.

Ultrasound-Guided Transgluteal Sciatic Nerve Block for Gluteal Procedural Analgesia.

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.

Is novel coronavirus 2019 reinfection possible? Interpreting dynamic SARS-CoV-2 test results.

Since December 2019, COVID-19, the clinical syndrome associated with SARS-CoV-2 infection, has infected more than 6.2 million people and brought the function of the global community to a halt. As the number of patients recovered from COVID-19 rises and the world transitions toward reopening, the question of acquired immunity versus the possibility of reinfection are critical to anticipating future viral spread. Here, we present a case of a patient previously recovered from COVID-19 who re-presents with new respiratory, radiographical, laboratory, and real-time reverse transcriptase-polymerase chain reaction (RT-PCR) findings concerning for possible re-infection. We review this case in the context of the evolving discussion and theories surrounding dynamic RT-PCR results, prolonged viral shedding, and the possibility of developed immunity. Understanding how to interpret dynamic and late-positive SARS-CoV-2 RT-PCR results after primary infection will be critical for understanding disease prevalence and spread among communities worldwide.

Lung Ultrasound in Children with Respiratory Tract Infections: Viral, Bacterial or COVID-19? A Narrative Review.

Respiratory tract infections (RTIs) are common complaints among patients presenting to the pediatric emergency department. In the diagnostic assessment of children with RTIs, many patients ultimately undergo imaging studies for further evaluation. Point-of-care lung ultrasound (LUS) can be used safely and with a high degree of accuracy in differentiating etiologies of RTIs in pediatric patients. Ultrasonographical features such as an irregular pleural line, subpleural consolidations, focal and lobar consolidation and signs of interstitial involvement can be used to distinguish between several pathologies. This work offers a comprehensive overview of pediatric LUS in cases of the most common pediatric RTIs including bacterial and viral pneumonia, bronchiolitis, and COVID-19.