Publications

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.

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.