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Can Ultrasound Identify Traumatic Knee Arthrotomy in a Cadaveric Model?

Authors :
Matthew Kongkatong
James H. Moak
Christopher Thom
Source :
The Journal of Emergency Medicine. 57:362-366
Publication Year :
2019
Publisher :
Elsevier BV, 2019.

Abstract

Background Traumatic arthrotomy of the knee (TAK) involves the violation of the knee capsule. TAK differs from simple lacerations because it requires operative management to prevent resultant septic arthritis. The diagnostic test of choice in the emergency department is the saline load test (SLT). SLT sensitivity ranges from 34% to 99%, depending on volume used. Computed tomography (CT) is a possible alternative, using intra-articular air as a diagnostic marker. Ultrasound can identify air in various tissues, given its highly echogenic nature. Objective We sought to determine the sensitivity and specificity of ultrasound for detecting intra-articular air in cadaveric knee joints. Methods Soft embalmed cadavers were utilized. The knees were block randomized to having 1 mL of air injected into the joint or sham skin injection. Two blinded, expert operators scanned the knees with a high-frequency linear transducer. The sensitivity and specificity were calculated. Results Twenty knees were included. Knees that had any prior dissection were excluded from analysis. Ten knees were randomized for air injection. The pooled sensitivity was 0.65 (95% confidence interval [CI] 0.41–0.85) with a specificity of 0.75 (95% CI 0.48–0.93). Mean time taken was 143 s. Conclusions Ultrasound may have utility in evaluation of TAK. There were limitations. Some knees had effusions with echogenic material present, which could have led to false-positive results. It is also unknown how much air is typical of TAK. One milliliter was used based on previous work with CT. The use of ultrasound for diagnosis of TAK warrants further study.

Details

ISSN :
07364679
Volume :
57
Database :
OpenAIRE
Journal :
The Journal of Emergency Medicine
Accession number :
edsair.doi.dedup.....182209544c0eaec7fa47958dd12c79a8