Details

Autor(en) / Beteiligte

• Logigian, Eric L.
• Villanueva, Raissa
• Twydell, Paul T.
• Myers, Bennett
• Downs, Marlene
• Preston, David C.
• Kothari, Milind J.
• Herrmann, David N.

Titel

Electrodiagnosis of ulnar neuropathy at the elbow (Une): A bayesian approach

Ist Teil von

• Muscle & nerve, 2014-03, Vol.49 (3), p.337-344

Ort / Verlag

United States: Blackwell Publishing Ltd

Erscheinungsjahr

2014

Quelle

Wiley-Blackwell Journals

Beschreibungen/Notizen

• 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. Muscle Nerve 49:337–344, 2014