Efficacy of an online education program for ultrasound diagnosis of pneumothorax.

Journal Article (Journal Article)

BACKGROUND: Experienced ultrasonographers can rule out pneumothorax reliably. The authors hypothesized that with basic training, anesthesia residents and faculty can also reliably rule out pneumothorax when presented with an optimal ultrasound image of the chest. METHODS: The study investigators created a library of 99 ultrasound video images of the chest with or without pneumothorax obtained from 53 patients undergoing elective thoracic surgery. After a 5-min tutorial, the physicians were invited to take a quiz based on 20 ultrasound videos randomly selected from the library. Sensitivity and specificity were calculated for overall performance, and a generalized estimating equations model was created to identify significant independent covariates affecting performance. To detect the retention rate for this skill, participants were asked to take the quiz again 6 months later. RESULTS: Seventy-nine anesthesia residents and faculty took part in the study. The sensitivity and specificity for ruling out pneumothorax was 86.6% and 85.6% respectively. On generalized estimating equation model, participants were significantly less likely to identify ultrasound features of pneumothorax if there was probe movement (P value = 0.002; OR 2.69; 95% CI 1.61-4.5) or heartbeat (P < 0.001; OR 3.54; 95% CI 2.27-5.51) on the ultrasound video. The median and interquartile ranges for scores (90%, and 80-95% respectively) did not change from the first to the second quiz. CONCLUSION: After viewing a 5-min online training video, physicians can reliably rule out pneumothorax on an optimal ultrasound image. They are also able to retain this skill for up to 6 months.

Full Text

Duke Authors

Cited Authors

  • Krishnan, S; Kuhl, T; Ahmed, W; Togashi, K; Ueda, K

Published Date

  • March 2013

Published In

Volume / Issue

  • 118 / 3

Start / End Page

  • 715 - 721

PubMed ID

  • 23291625

Electronic International Standard Serial Number (EISSN)

  • 1528-1175

Digital Object Identifier (DOI)

  • 10.1097/ALN.0b013e31827f0979


  • eng

Conference Location

  • United States