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Muscle Nerve. 2014 Mar;49(3):337-44. doi: 10.1002/mus.23913. Epub 2013 Jun 26.

Electrodiagnosis of ulnar neuropathy at the elbow (Une): a Bayesian approach.

Author information

1
Department of Neurology, Box 673, 601 Elmwood Avenue, University of Rochester Medical Center, Rochester, New York, 14642, USA.

Abstract

INTRODUCTION:

In ulnar neuropathy at the elbow (UNE), we determined how electrodiagnostic cutoffs [across-elbow ulnar motor conduction velocity slowing (AECV-slowing), drop in across-elbow vs. forearm CV (AECV-drop)] depend on pretest probability (PreTP).

METHODS:

Fifty clinically defined UNE patients and 50 controls underwent ulnar conduction testing recording abductor digiti minimi (ADM) and first dorsal interosseous (FDI), stimulating wrist, below-elbow, and 6-, 8-, and 10-cm more proximally. For various PreTPs of UNE, the cutoffs required to confirm UNE (defined as posttest probability = 95%) were determined with receiver operator characteristic (ROC) curves and Bayes Theorem.

RESULTS:

On ROC and Bayesian analyses, the ADM 10-cm montage was optimal. For PreTP = 0.25, the confirmatory cutoffs were >23 m/s (AECV-drop), and <38 m/s (AECV-slowing); for PreTP = 0.75, they were much less conservative: >14 m/s, and <47 m/s, respectively.

CONCLUSIONS:

(1) In UNE, electrodiagnostic cutoffs are critically dependent on PreTP; rigid cutoffs are problematic. (2) AE distances should be standardized and at least 10 cm.

KEYWORDS:

Bayes theorem; ROC curve; electrodiagnosis; nerve conduction; ulnar neuropathy

PMID:
23716377
DOI:
10.1002/mus.23913
[Indexed for MEDLINE]
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