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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.

Introduction: Tricuspid annular plane systolic excursion (TAPSE) is an established echocardiographic marker of right ventricular (RV) systolic function. The objective of this study was to evaluate whether emergency clinicians can visually estimate RV function using TAPSE in a set of video clips compared to a reference standard M-mode measurement.Methods: Emergency clinicians were shown a five-minute educational video on TAPSE. Participants then viewed 20 apical four-chamber point-of-care ultrasound (POCUS) echocardiography clips and recorded their estimate of TAPSE distance in centimeters (cm), as well as whether TAPSE was normal (>1.9 cm), borderline (1.5-1.9 cm), or abnormal (1.9 cm was 91.4% (95% CI, 88.4-94.3%) and 90.8% (95% CI, 87.7-93.9%), respectively. There was no significant difference in sensitivity (p = 0.27) or specificity (p = 0.55) between resident and attending physicians or between physicians and PAs (p = 0.17 and p = 0.81). Median self-reported comfort with TAPSE assessment increased from 1 (interquartile range [IQR] 1-2) to 3 (IQR 3-4) points after participation in the study.Conclusion: A wide range of emergency clinicians demonstrated fair accuracy for visual estimation of TAPSE on previously recorded POCUS echocardiography video clips. These findings should be considered hypothesis generating and warrant validation in larger, prospective studies.

Introduction: First detected in December 2019, the severe acute respiratory syndrome coronavirus 2 pandemic upended the global community in a few short months. Diagnostic testing is currently limited in availability, accuracy, and efficiency. Imaging modalities such as chest radiograph (CXR), computed tomography, and lung ultrasound each demonstrate characteristic findings of coronavirus disease 2019 (COVID-19). Lung ultrasound offers benefits over other imaging modalities including portability, cost, reduced exposure of healthcare workers as well as decreased contamination of equipment such as computed tomography scanners.Case Series: Here we present a case series describing consistent lung ultrasound findings in patients with confirmed COVID-19 despite variable clinical presentations and CXR findings. We discuss a triage algorithm for clinical applicability and utility of lung point-of-care ultrasound in the setting of COVID-19 and advocate for judicious and targeted use of this tool.Conclusion: Lung point-of-care ultrasound can provide valuable data supporting diagnostic and triage decisions surrounding suspected cases of COVID-19. Prospective studies validating our proposed triage algorithm are warranted.

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. [ABSTRACT FROM AUTHOR]

Background: Point‐of‐care ultrasound (US) has been suggested as the primary imaging in evaluating patients with suspected diverticulitis. Discrimination between simple and complicated diverticulitis may help to expedite emergent surgical consults and determine the risk of complications. This study aimed to: (1) determine the accuracy of an US protocol (TICS) for diagnosing diverticulitis in the emergency department (ED) setting and (2) assess the ability of TICS to distinguish between simple and complicated diverticulitis. Methods: Patients with clinically suspected diverticulitis who underwent a diagnostic computed tomography (CT) scan were identified prospectively in the ED. Emergency US faculty and fellows blinded to the CT results performed and interpreted US scans. The presence of simple or complicated diverticulitis was recorded after each US evaluation. The diagnostic ability of the US was compared to CT as the criterion standard. Modified Hinchey classification was used to distinguish between simple and complicated diverticulitis. Results: A total of 149 patients (55% female, mean ± SD age 58 ± 16 years) were enrolled and included in the final analyses. Diverticulitis was the final diagnosis in 75 of 149 patients (50.3%), of whom 53 had simple diverticulitis and 22 had perforated diverticulitis (29.4%). TICS protocol's test characteristics for simple diverticulitis include a sensitivity of 95% (95% confidence interval [CI] 87%–99%), specificity of 76% (95% CI 65%–86%), positive predictive value of 80% (95% CI 71%–88%), and negative predictive value of 93% (95% CI 84%–98%). TICS protocol correctly identified 12 of 22 patients with complicated diverticulitis (sensitivity 55% [95% CI 32%–76%]) and specificity was 96% (95% CI 91%–99%). Eight of 10 missed diagnoses of complicated diverticulitis were identified as simple diverticulitis, and two were recorded as negative. Conclusions: In ED patients with suspected diverticulitis, US demonstrated high accuracy in ruling out or diagnosing diverticulitis, but its reliability in differentiating complicated from simple diverticulitis is unsatisfactory. [ABSTRACT FROM AUTHOR]

Introduction: Point-of-care ultrasound (POCUS) is an essential tool in the timely evaluation of an undifferentiated patient in the emergency department (ED). Our primary objective in this study was to determine the perceived impact of POCUS in high-risk cases presented at emergency medicine (EM) morbidity and mortality (M&M) conferences. Additionally, we sought to identify in which types of patients POCUS might be most useful, and which POCUS applications were considered to be highest yield. Methods: This was a retrospective survey of cases submitted to M&M at an EM residency program that spans two academic EDs, over one academic year. Postgraduate year 4 (PGY) residents who presented M&M cases at departmental sessions were surveyed on perceived impacts of POCUS on individual patient outcomes. We evaluated POCUS use and indications while the POCUS was used. Results: Over the 12-month period, we reviewed 667 cases from 18 M&M sessions by 15 PGY-4 residents and a supervising EM attending physician who chairs the M&M committee. Of these cases, 75 were selected by the M&M committee for review and presentation. POCUS was used in 27% (20/75) of the cases and not used in 73% (55/75). In cases where POCUS was not used, retrospective review determined that if POCUS had been used it would have “likely prevented the M&M” in 45% (25/55). Of these 25 cases, the majority of POCUS applications that could have helped were cardiac (32%, 8/25) and lung (32%, 8/25) ultrasound. POCUS was felt to have greatest potential in identifying missed diagnoses (92%, 23/25), and decreasing the time to diagnosis (92%, 23/25). Patients with cardiopulmonary chief complaints and abnormal vital signs were most likely to benefit. There were seven cases (35%, 7/20, 95% CI 15-59%) in which POCUS was performed and thought to have possibly adversely affected the outcome of the M&M. Conclusion: POCUS was felt to have the potential to reduce or prevent M&M in 45% of cases in which it was not used. Cardiac and lung POCUS were among the most useful applications, especially in patients with cardiopulmonary complaints and in those with abnormal vital signs. [ABSTRACT FROM AUTHOR]

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., (©Nicole M Duggan, Mike Jin, Maria Alejandra Duran Mendicuti, Stephen Hallisey, Denie Bernier, Lauren A Selame, Ameneh Asgari-Targhi, Chanel E Fischetti, Ruben Lucassen, Anthony E Samir, Erik Duhaime, Tina Kapur, Andrew J Goldsmith. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.07.2024.)

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.

Acute pain is one of the most frequent, and yet one of the most challenging, complaints physicians encounter in the emergency department (ED). Currently, opioids are one of several pain medications given for acute pain, but given the long-term side effects and potential for abuse, alternative pain regimens are sought. Ultrasound-guided nerve blocks (UGNB) can provide quick and sufficient pain control and therefore can be considered a component of a physician's multimodal pain plan in the ED. As UGNB are more widely implemented at the point of care, guidelines are needed to assist emergency providers to acquire the skill necessary to incorporate them into their acute pain management., Competing Interests: Andrew Goldsmith holds a grant from Massachusetts Life Sciences, has received consulting fees from Phillips, Exo, and Bain Capital, and has received honoraria from Indian Health Services. Arun Nagdev holds stock options in Exo Inc., and is a salaried employee of Exo Inc., (Copyright (c) 2022 Joseph Brown, Andrew Goldsmith, Alexis LaPietra, Jose Zeballos, Kamen Vlassakov, Alexander Stone, R. Starr Knight, Jennifer Carnell, Arun Nagdev.)

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., (©Nicole M Duggan, M Adrian Hasdianda, Olesya Baker, Guruprasad Jambaulikar, Andrew J Goldsmith, Anna Condella, Desiree Azizoddin, Adaira I Landry, Edward W Boyer, Andrew J Eyre. Originally published in JMIR Formative Research (https://formative.jmir.org), 22.03.2022.)