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

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

Research output: Contribution to journalArticle

10 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)337-344
Number of pages8
JournalMuscle and Nerve
Volume49
Issue number3
DOIs
StatePublished - Jan 1 2014

Fingerprint

Electrodiagnosis
Ulnar Neuropathies
Bayes Theorem
Elbow
Wrist
Forearm

All Science Journal Classification (ASJC) codes

  • Physiology
  • Clinical Neurology
  • Cellular and Molecular Neuroscience
  • Physiology (medical)

Cite this

Logigian, E. L., Villanueva, R., Twydell, P. T., Myers, B., Downs, M., Preston, D. C., ... Herrmann, D. N. (2014). Electrodiagnosis of ulnar neuropathy at the elbow (Une): A bayesian approach. Muscle and Nerve, 49(3), 337-344. https://doi.org/10.1002/mus.23913
Logigian, Eric L. ; Villanueva, Raissa ; Twydell, Paul T. ; Myers, Bennett ; Downs, Marlene ; Preston, David C. ; Kothari, Milind J. ; Herrmann, David N. / Electrodiagnosis of ulnar neuropathy at the elbow (Une) : A bayesian approach. In: Muscle and Nerve. 2014 ; Vol. 49, No. 3. pp. 337-344.
@article{9f64c7a8b8094cd5a8433926af467277,
title = "Electrodiagnosis of ulnar neuropathy at the elbow (Une): A bayesian approach",
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.",
author = "Logigian, {Eric L.} and Raissa Villanueva and Twydell, {Paul T.} and Bennett Myers and Marlene Downs and Preston, {David C.} and Kothari, {Milind J.} and Herrmann, {David N.}",
year = "2014",
month = "1",
day = "1",
doi = "10.1002/mus.23913",
language = "English (US)",
volume = "49",
pages = "337--344",
journal = "Muscle and Nerve",
issn = "0148-639X",
publisher = "John Wiley and Sons Inc.",
number = "3",

}

Logigian, EL, Villanueva, R, Twydell, PT, Myers, B, Downs, M, Preston, DC, Kothari, MJ & Herrmann, DN 2014, 'Electrodiagnosis of ulnar neuropathy at the elbow (Une): A bayesian approach', Muscle and Nerve, vol. 49, no. 3, pp. 337-344. https://doi.org/10.1002/mus.23913

Electrodiagnosis of ulnar neuropathy at the elbow (Une) : A bayesian approach. / Logigian, Eric L.; Villanueva, Raissa; Twydell, Paul T.; Myers, Bennett; Downs, Marlene; Preston, David C.; Kothari, Milind J.; Herrmann, David N.

In: Muscle and Nerve, Vol. 49, No. 3, 01.01.2014, p. 337-344.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Electrodiagnosis of ulnar neuropathy at the elbow (Une)

T2 - A bayesian approach

AU - Logigian, Eric L.

AU - Villanueva, Raissa

AU - Twydell, Paul T.

AU - Myers, Bennett

AU - Downs, Marlene

AU - Preston, David C.

AU - Kothari, Milind J.

AU - Herrmann, David N.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84893965081&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893965081&partnerID=8YFLogxK

U2 - 10.1002/mus.23913

DO - 10.1002/mus.23913

M3 - Article

C2 - 23716377

AN - SCOPUS:84893965081

VL - 49

SP - 337

EP - 344

JO - Muscle and Nerve

JF - Muscle and Nerve

SN - 0148-639X

IS - 3

ER -

Logigian EL, Villanueva R, Twydell PT, Myers B, Downs M, Preston DC et al. Electrodiagnosis of ulnar neuropathy at the elbow (Une): A bayesian approach. Muscle and Nerve. 2014 Jan 1;49(3):337-344. https://doi.org/10.1002/mus.23913