arrow
Return

Implementation and evaluation of a statistical framework for nerve conduction study reference range calculation

delete2010-01-01
delete6
PRE
AI
X
Xuan Kong *
D
David Schoenfeld
E
Eugene A. Lesser
S
Shai N. Gozani
DOI:10.1016/j.cmpb.2009.05.002delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Nerve conduction studies (NCS) play a central role in the clinical evaluation of neuropathies. Their clinical utilization depends on reference ranges that define the expected parameter values in disease-free individuals. In this paper, a statistical framework is proposed and described in detail for deriving NCS parameter reference ranges. The bootstrap technique is used to identify demographic and physiologic covariates that influence the NCS measurements. Multi-variate linear regression is used to improve the accuracy and effectiveness of NCS interpretation by reducing parameter variance. Non-linear mappings are used to transform parameters into a Gaussian distribution in order to minimize the influence of outliers. Modeling of heteroscedasticity observed in this and other studies leads to more sensible normal limits for several parameters. The proposed reference range method is automated using the MATLAB programming language. Data from a large sample of healthy subjects are used to establish reference ranges for 24 commonly measured NCS parameters. All but three parameters follow Gaussian distributions in their respective transformed domains. Excluding the distal motor latency difference between median and ulnar nerves, the reduction of the parameter variance as a result of regression in the transform domain is greater than 50% for all F-wave latency parameters and at least 10% for all other NCS parameters. Subject age is found to influence normal limits of all but one parameter and height has a statistically significant impact on all but three parameters. These reference range specifications provide clinicians with an alternative to developing their own reference ranges as long as their NCS techniques are consistent with those described in this paper. The proposed method should also be applicable to reference range development for other NCS techniques and physiological measurements. (C) 2009 Elsevier Ireland Ltd. All rights reserved.
Keywords:
Reference range
Normal limit
Nerve conduction study
Statistical method
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Computer Methods and Programs in Biomedicine cover
Computer Methods and Programs in Biomedicine
IF:
4.8
Papers:
7.0K
Citations:
2.1W

Organization

H
Harvard University
Scholars:
26.5W
Papers: 22.0W
Citations: 28.7W
H
Harvard Medical School
Scholars:
6.5W
Papers: 4.8W
Citations: 91
Cited Papers

Cited Papers

Characterisation of [125I][MePhe7]neurokinin B binding to tachykinin NK3 receptors: evidence for interspecies variance
err1994-09-01
err0
PREAI
errNirmala Suman-Chauhan; Pauline Grimson; Steve Guard; Zarina Madden; Fu-Zon Chung; Keith Watling; Robert Pinnock; Geoffrey Woodruff
errShare
errSave
errShare
errSave
Cerebral small vessel disease is related to disturbed 24‐h activity rhythms: a population‐based study
err2015-07-24
err0
PREAI
errL. A. Zuurbier; M. A. Ikram; A. I. Luik; A. Hofman; E. J. W. Van Someren; M. W. Vernooij; H. Tiemeier
errShare
errSave
Validation of a novel point-of-care nerve conduction device for the detection of diabetic sensorimotor polyneuropathy
err2006-09-01
err54
errOAAI
errPerkins, Bruce A.; Ngo, Mylan; Grewal, Jaspreet; Ng, Eduardo; Bril, Vera
errShare
errSave
researcher View more