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Learning curves for point-of-care ultrasound of the proximal femur: a prospective multicenter study
Remi L H Barateau1, Ralitsa Stoeva1,2, Fabien Le Van1,2
1Emergency Department Hospital Cochin, Assistance Publique Hôpitaux de Paris, 27 Rue du Faubourg Saint Jacques, 75017, Paris, France.
Background:
Proximal femur fractures are associated with high morbidity and mortality. Although X-ray remains the reference diagnostic tool, point-of-care ultrasound (POCUS) shows promising diagnostic accuracy and allows immediate guidance for regional anesthesia. However, its learning process has not been formally evaluated.
Objectives:
To establish the learning curves for a standardized proximal femur POCUS protocol, acquisition times were compared according to prior ultrasound experience, and participant feedback was collected.
Methods:
This prospective multicenter study was conducted between September 2024 and February 2025. Volunteer healthcare professionals underwent a short training session and performed repeated proximal femur ultrasound examinations on healthy subjects. Learning curves were analyzed via the LC-CUSUM method. Acquisition times and questionnaire-based feedback were collected.
Results:
Fifteen participants underwent 520 ultrasound examinations. After approximately 25 scans, all participants were able to complete a bilateral proximal femur examination within 60 s. The mean acquisition time decreased significantly between sessions (p < 0.001). Radiologists performed faster than nonradiologists did, but the differences narrowed over time. The technique was perceived as easy to learn and perform.
Conclusion:
Proximal femur POCUS is a rapidly acquired skill requiring approximately 25 scans to reach technical proficiency. This technique could be easily integrated into emergency department workflows and may facilitate earlier integration of ultrasound into the diagnostic pathway as well as faster implementation of regional anesthesia. Further patient-based studies are needed to assess diagnostic accuracy in traumatic settings and to determine whether technical proficiency translates into reliable fracture detection.
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