Abstract
Objective
To examine the performance of sonographic cross-sectional area (CSA) measurements
in the diagnosis of cubital tunnel syndrome (CuTS).
Data Sources
Electronic databases, comprising PubMed and EMBASE, were searched for the pertinent
literature before July 1, 2017.
Study Selection
Fourteen trials comparing the ulnar nerve CSA measurements between participants with
and without CuTS were included.
Data Extraction
Study design, participants' demographic characteristics, diagnostic reference of CuTS,
and methods of CSA measurement.
Data Synthesis
Among different elbow levels, the between-group difference in CSA was the largest
at the medial epicondyle (6.0mm2; 95% confidence interval [CI], 4.5–7.4mm2). The pooled mean CSA in participants without CuTS was 5.5mm2 (95% CI, 4.4–6.6mm2) at the arm level, 7.4mm2 (95% CI, 6.7–8.1mm2) at the cubital tunnel inlet, 6.6mm2 (95% CI, 5.9–7.2mm2) at the medial epicondyle, 7.3mm2 (95% CI, 5.6–9.0mm2) at the cubital tunnel outlet, and 5.5mm2 (95% CI, 4.7–6.3mm2) at the forearm level. The sensitivities, specificities, and diagnostic odds ratios
pooled from 5 studies, using 10mm2 as the cutoff point, were .85 (95% CI, .78–.90), .91 (95% CI, .86–.94), and 53.96
(95% CI, 14.84–196.14), respectively.
Conclusions
The ulnar nerve CSA measured by ultrasound imaging is useful for the diagnosis of
CuTS and is most significantly different between patients and participants without
CuTS at the medial epicondyle. Because the ulnar nerve CSA in healthy participants,
at various locations, rarely exceeds 10mm2, this value can be considered as a cutoff point for diagnosing ulnar nerve entrapment
at the elbow region.
Keywords
List of abbreviations:
CI (confidence interval), CSA (cross-sectional area), CuTS (cubital tunnel syndrome), QUADAS (Quality Assessment of Diagnostic Accuracy Studies)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: September 06, 2017
Footnotes
Supported by the National Taiwan University Hospital, Bei-Hu Branch (grant no. Bei-Hu 10601) and the Ministry of Science and Technology (grant no. 104-2314-B-002-022-MY2).
Disclosures: none.
Identification
Copyright
© 2017 by the American Congress of Rehabilitation Medicine
