Publications

IMPORTANCE: Ultrasonography-guided nerve blocks (UGNBs) have become a core component of multimodal analgesia for acute pain management in the emergency department (ED). Despite their growing use, national adoption of UGNBs has been slow due to a lack of procedural safety in the ED.

OBJECTIVE: To assess the complication rates and patient pain scores of UGNBs performed in the ED.

DESIGN, SETTING, AND PARTICIPANTS: This cohort study included data from the National Ultrasound-Guided Nerve Block Registry, a retrospective multicenter observational registry encompassing procedures performed in 11 EDs in the US from January 1, 2022, to December 31, 2023, of adult patients who underwent a UGNB.

EXPOSURE: UGNB encounters.

MAIN OUTCOMES AND MEASURES: The primary outcome of this study was complication rates associated with ED-performed UGNBs recorded in the National Ultrasound-Guided Nerve Block Registry from January 1, 2022, to December 31, 2023. The secondary outcome was patient pain scores of ED-based UGNBs. Data for all adult patients who underwent an ED-based UGNB at each site were recorded. The volume of UGNB at each site, as well as procedural outcomes (including complications), were recorded. Data were analyzed using descriptive statistics of all variables.

RESULTS: In total, 2735 UGNB encounters among adult patients (median age, 62 years [IQR, 41-77 years]; 51.6% male) across 11 EDs nationwide were analyzed. Fascia iliaca blocks were the most commonly performed UGNBs (975 of 2742 blocks [35.6%]). Complications occurred at a rate of 0.4% (10 of 2735 blocks). One episode of local anesthetic systemic toxicity requiring an intralipid was reported. Overall, 1320 of 1864 patients (70.8%) experienced 51% to 100% pain relief following UGNBs. Operator training level varied, although 1953 of 2733 procedures (71.5%) were performed by resident physicians.

CONCLUSIONS AND RELEVANCE: The findings of this cohort study of 2735 UGNB encounters support the safety of UGNBs in ED settings and suggest an association with improvement in patient pain scores. Broader implementation of UGNBs in ED settings may have important implications as key elements of multimodal analgesia strategies to reduce opioid use and improve patient care.

<sec id="st1"><title>BACKGROUND</title>Despite the high morbidity and mortality globally, standard microbiologic diagnosis for TB requires laboratory infrastructure inaccessible in many resource-limited areas and may be insufficient for identifying extrapulmonary disease. Point-of-care (POC) ultrasound facilitates visualization of extrapulmonary manifestations, permitting laboratory-independent diagnosis, but its diagnostic utility remains unclear.</sec><sec id="st2"><title>METHODS</title>We conducted a systematic review of five online databases for studies reporting ultrasound findings among cases with and without extrapulmonary TB (EPTB). A minimum of two authors independently screened and reviewed each article, and extracted data elements of interest. We conducted a series of univariate meta-analyses using a random-effects model to calculate the pooled effect estimate and 95% confidence interval (CI) for each outcome: sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).</sec><sec id="st3"><title>RESULTS</title>Of 279 articles identified, 6 were included. There were 699 cases of EPTB among 1,633 participants. The pooled sensitivity estimate was 0.72 (95% CI 0.57-0.88). The pooled specificity estimate was 0.77 (95% CI 0.63-0.90). The pooled PPV and NPV estimates were respectively 0.67 (95% CI 0.47-0.87) and 0.85 (95% CI 0.77-0.93).</sec><sec id="st4"><title>CONCLUSION</title>POC ultrasound showed modest test characteristics for diagnosing EPTB, which may constitute an improvement over some currently available diagnostics.</sec>.

Space travel has transformed in the past several years. Given the burgeoning market for space tourism, in-flight medical emergencies are likely to be expected. Ultrasound is one of the few diagnostic and therapeutic modalities available for astronauts in space. However, while point-of-care ultrasound (POCUS) is available, there is no current standard of training for astronaut preparation. We suggest an organized and structured methodology by which astronauts should best prepare for space with the medical equipment available on board. As technology continues to evolve, the assistance of other artificial intelligence and augmented reality systems are likely to facilitate training and dynamic real-time needs during space emergencies. Summary: As space tourism continues to evolve, an organized methodology for POCUS use is advised to best prepare astronauts for space.

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.

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.

Lung ultrasound (LUS) has become an indispensable tool at the bedside in emergency and acute care settings, offering a fast and non-invasive way to assess pulmonary congestion. Its portability and cost-effectiveness make it particularly valuable in resource-limited environments where quick decision-making is critical. Despite its advantages, the interpretation of B-line artifacts, which are key diagnostic indicators for conditions related to pulmonary congestion, can vary significantly among clinicians and even for the same clinician over time. This variability, coupled with the time pressure in acute settings, poses a challenge. To address this, our study introduces a new B-line segmentation method to calculate congestion scores from LUS images, aiming to standardize interpretations. We utilized a large dataset of 31,000 B-line annotations synthesized from over 550,000 crowdsourced opinions on LUS images of 299 patients to improve model training and accuracy. This approach has yielded a model with 94% accuracy in B-line counting (within a margin of 1) on a test set of 100 patients, demonstrating the potential of combining extensive data and crowdsourcing to refine lung ultrasound analysis for pulmonary congestion.

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.