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Objectives: Point‐of‐care‐ultrasound (POCUS) is increasingly used by pediatric emergency medicine (PEM) fellows, but scant data exists on the accuracy of exam interpretations. Our goal was to determine whether agreement on exam interpretation between quality assurance (QA) faculty (reference standard) and PEM fellows varied by fellowship year or exam type. Methods: Retrospective review of fellow‐performed POCUS exams between January 2019 and June 2022. Negative binomial (NB) random effects regression was used to account for longitudinal measurement of individual fellow performance across 3 years. Fixed effects were exam type and fellowship year. To assess between‐ and within‐user variability across time, a random intercept and slope were included for each fellow. Results: Exactly 3032 exams, performed by 24 fellows, were included. Raw proportion agreement by fellowship year was high for all exam types (≥88%). From the NB model, there was no statistically significant effect of fellowship year on the mean count of agreement. The relative risk (RR) of agreement for exam types was greatest for cardiac vs other types. The standard deviations for the random intercept and random slope were 0.09 and 0.04, respectively, with a correlation of −0.94. Conclusions: PEM fellows generally interpret exams correctly, with little variation through fellowship, although those who began with more basic skills showed more progress over time. Fellowship year did not influence the likelihood of correct interpretation but there was variation across exam type, with the best agreement for cardiac exams. The extent to which disagreements between fellows and QA faculty represent clinically significant errors requires further study. [ABSTRACT FROM AUTHOR]

Introduction: Recent research has investigated the use of ultrasound (US) for confirming endotracheal tube (ETT) placement with varying techniques, accuracies, and challenges. Our objective was to evaluate the accuracy of a novel, simplified, four-step (4S) technique.Methods: We conducted a blinded, randomized trial of the 4S technique utilizing an adult human cadaver model. ETT placement was randomized to tracheal or esophageal location. Three US experts and 45 emergency medicine residents (EMR) performed a total of 150 scans. The primary outcome was the overall sensitivity and specificity of both experts and EMRs to detect location of ETT placement. Secondary outcomes included a priori subgroup comparison of experts and EMRs for thin and obese cadavers, time to detection, and level of operator confidence.Results: Experts had a sensitivity of 100% (95% CI = 72% to 100%) and specificity of 100% (95% CI = 77% to 100%) on thin, and a sensitivity of 93% (95% CI = 66% to 100%) and specificity of 100% (95% CI = 75% to 100%) on obese cadavers. EMRs had a sensitivity of 91% (95% CI = 69% to 98%) and of specificity 96% (95% CI = 76% to 100%) on thin, and a sensitivity of 100% (95% CI = 82% to 100%) specificity of 48% (95% CI = 27% to 69%) on obese cadavers. The overall mean time to detection was 17 seconds (95% CI = 13 seconds to 20 seconds, range: 2 to 63 seconds) for US experts and 29 seconds (95% CI = 25 seconds to 33 seconds; range: 6 to 120 seconds) for EMRs. There was a statistically significant decrease in the specificity of this technique on obese cadavers when comparing the EMRs and experts, as well as an increased overall time to detection among the EMRs.Conclusion: The simplified 4S technique was accurate and rapid for US experts. Among novices, the 4S technique was accurate in thin, but appears less accurate in obese cadavers. Further studies will determine optimal teaching time and accuracy in emergency department patients. [West J Emerg Med. 2014;15(7)-0.]

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]

Objectives The primary goal of this study was to determine accuracy for diagnosing acutely decompensated heart failure ( ADHF) in the undifferentiated dyspneic emergency department ( ED) patient using a lung and cardiac ultrasound (Lu CUS) protocol. Secondary objectives were to determine if US findings acutely change management and if findings are more accurate than clinical gestalt. Methods This was a prospective, observational study of adult patients presenting to the ED with undifferentiated dyspnea. The intervention consisted of a 12-view Lu CUS protocol performed by experienced emergency physician sonographers. The primary objective was measured by comparing US findings to the final diagnosis independently determined by two physicians blinded to the Lu CUS result. Acute treatment changes based on US findings were tracked in real time through a standardized data collection form. Results Data on 99 patients were analyzed; ADHF was the final diagnosis in 36%. The Lu CUS protocol had sensitivity of 83% (95% confidence interval [ CI] = 67% to 93%), specificity of 83% (95% CI = 70% to 91%), positive likelihood ratio of 4.8 (95% CI = 2.7 to 8.3), and negative likelihood ratio of 0.20 (95% CI = 0.09 to 0.42). Forty-seven percent of patients had changes in acute management, and 42% had changes in acute treatment. Observed agreement for the Lu CUS protocol was 93% between coinvestigators. Overall, accuracy improved by 20% (83% vs. 63%, 95% CI = 8% to 31% for the difference) over clinical gestalt alone. Conclusions The Lu CUS protocol may accurately identify ADHF and may improve acute clinical management in dyspneic ED patients. This protocol has improved diagnostic accuracy over clinical gestalt alone. [ABSTRACT FROM AUTHOR]