An Additional Electrodiagnostic Tool for Ulnar Neuropathy: Mixed across the ElbowFunding None.
Background Diagnosing ulnar neuropathy at the elbow (UNE) remains challenging despite guidelines from national organizations. Motor testing of hand intrinsic muscles remains a common diagnostic method fraught with challenges.
Objective The aim of the study is to demonstrate utility of an uncommon nerve conduction study (NCS), mixed across the elbow, when diagnosing UNE.
Methods Retrospective analysis of 135 patients, referred to an outpatient University-based electrodiagnostic laboratory with suspected UNE between January 2013 and June 2019 who had motor to abductor digiti minimi (ADM), motor to first dorsal interosseus (FDI), and mixed across the elbow NCS completed. To perform the mixed across the elbow NCS, the active bar electrode was placed 10-cm proximal to the medial epicondyle between the biceps and triceps muscle bellies. The median nerve was stimulated at the wrist followed by stimulation of the ulnar nerve at the ulnar styloid. The difference between peak latencies, labeled the ulnar-median mixed latency difference (U-MLD), was used to evaluate for correlation between the nerve conduction velocities (NCV) of ADM and FDI.
Results Pearson r-values = −0.479 and −0.543 (p < 0.00001) when comparing U-MLD to ADM and FDI NCV across the elbow, respectively. The negative r-value describes the inverse relationship between ulnar velocity across the elbow and increasing U-MLD.
Conclusion Mixed across the elbow has moderate–strong correlation with ADM and FDI NCV across the elbow. All three tests measure ulnar nerve function slightly differently. Without further prospective data, the most accurate test remains unclear. The authors propose some combination of the three tests may be most beneficial when diagnosing UNE.
Previous Presentation: Awarded a “Top Poster” Designation at Association of Academic Physiatrists Annual Meeting in Orlando, FL 2020.
Eingereicht: 15. April 2020
Angenommen: 08. Juni 2020
26. August 2020 (online)
© 2020. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).
Georg Thieme Verlag KG
Stuttgart · New York
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