Electrophysiologic Measures of Ulnar Sensory Nerve Function: Electrophysiologic Measures of Ulnar Sensory Nerve Function: Reference Values and Reliability.

Slides:



Advertisements
Similar presentations
Richard M. Jacobs, OSA, Ph.D.
Advertisements

13- 1 Chapter Thirteen McGraw-Hill/Irwin © 2005 The McGraw-Hill Companies, Inc., All Rights Reserved.
Intrusion of Incisors to Facilitate Restoration: The Impact on the Periodontium Intrusion of Incisors to Facilitate Restoration: The Impact on the Periodontium.
Copyright © 2011 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 12 Measures of Association.
Power Analysis in Grant Writing Jill Harkavy-Friedman, Ph.D.
Quantitative Techniques
LECTURE 3 Introduction to Linear Regression and Correlation Analysis
POSTER TEMPLATE BY: Modeling The Relationship Between Sleep Characteristics and Pediatric Obesity Student: Andrew Althouse,
© 2010 Pearson Prentice Hall. All rights reserved Least Squares Regression Models.
Linear Regression and Correlation
Clustered or Multilevel Data
Regression Chapter 10 Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania.
Correlational Designs
Using Statistical Methods to Improve Disease Classification Ryan Sieberg Advisor: Rebecca Nugent Abstract In this research project, we combined statistical.
Chapter 7 Correlational Research Gay, Mills, and Airasian
Summary of Quantitative Analysis Neuman and Robson Ch. 11
Week 14 Chapter 16 – Partial Correlation and Multiple Regression and Correlation.
Linear Regression/Correlation
T-tests and ANOVA Statistical analysis of group differences.
Example of Simple and Multiple Regression
Quantitative Research Methods Project 3 Group 4A Valerie Bryan Emily Leak Lori Moore UWG Fall 2011.
1. An Overview of the Data Analysis and Probability Standard for School Mathematics? 2.
Chapter 13: Inference in Regression
PTP 560 Research Methods – Week 13 Thomas Ruediger, PT.
CPE 619 Simple Linear Regression Models Aleksandar Milenković The LaCASA Laboratory Electrical and Computer Engineering Department The University of Alabama.
Simple Linear Regression Models
Class Meeting #11 Data Analysis. Types of Statistics Descriptive Statistics used to describe things, frequently groups of people.  Central Tendency 
© Copyright McGraw-Hill CHAPTER 3 Data Description.
Chapter 11 Descriptive Statistics Gay, Mills, and Airasian
L 1 Chapter 12 Correlational Designs EDUC 640 Dr. William M. Bauer.
Statistics for clinicians Biostatistics course by Kevin E. Kip, Ph.D., FAHA Professor and Executive Director, Research Center University of South Florida,
Statistics in Applied Science and Technology Chapter 13, Correlation and Regression Part I, Correlation (Measure of Association)
Descriptive Statistics
Lecture 8 Simple Linear Regression (cont.). Section Objectives: Statistical model for linear regression Data for simple linear regression Estimation.
© Copyright McGraw-Hill Correlation and Regression CHAPTER 10.
Statistical planning and Sample size determination.
Chap 18-1 Copyright ©2012 Pearson Education, Inc. publishing as Prentice Hall Chap 18-1 Chapter 18 A Roadmap for Analyzing Data Basic Business Statistics.
One-arm reach (heels down example) Two-arm reach (heels up example) Conclusions Highest reliability for all age groups was in the toe-to-finger method.
Measurement MANA 4328 Dr. Jeanne Michalski
Sampling and Nested Data in Practice-Based Research Stephen Zyzanski, PhD Department of Family Medicine Case Western Reserve University School of Medicine.
Kin 304 Descriptive Statistics & the Normal Distribution
D/RS 1013 Data Screening/Cleaning/ Preparation for Analyses.
Handout Twelve: Design & Analysis of Covariance
ABSTRACT The purpose of the present study was to investigate the test-retest reliability of force-time derived parameters of an explosive push up. Seven.
Handout Six: Sample Size, Effect Size, Power, and Assumptions of ANOVA EPSE 592 Experimental Designs and Analysis in Educational Research Instructor: Dr.
School of Nursing Health Literacy Among Informal Caregivers of Persons With Memory Loss Judith A. Erlen, PhD, RN, FAAN; Jennifer H. Lingler, PhD, RN; Lisa.
Educational Research: Data analysis and interpretation – 1 Descriptive statistics EDU 8603 Educational Research Richard M. Jacobs, OSA, Ph.D.
Assumptions of Multiple Regression 1. Form of Relationship: –linear vs nonlinear –Main effects vs interaction effects 2. All relevant variables present.
Venn diagram shows (R 2 ) the amount of variance in Y that is explained by X. Unexplained Variance in Y. (1-R 2 ) =.36, 36% R 2 =.64 (64%)
Statistics Josée L. Jarry, Ph.D., C.Psych. Introduction to Psychology Department of Psychology University of Toronto June 9, 2003.
Approaches to quantitative data analysis Lara Traeger, PhD Methods in Supportive Oncology Research.
Educational Research Descriptive Statistics Chapter th edition Chapter th edition Gay and Airasian.
NURS 306, Nursing Research Lisa Broughton, MSN, RN, CCRN RESEARCH STATISTICS.
5. Evaluation of measuring tools: reliability Psychometrics. 2011/12. Group A (English)
Chapter 11 Linear Regression and Correlation. Explanatory and Response Variables are Numeric Relationship between the mean of the response variable and.
Statistics & Evidence-Based Practice
Clinical practice involves measuring quantities for a variety of purposes, such as: aiding diagnosis, predicting future patient outcomes, serving as endpoints.
Chapter 13 Simple Linear Regression
BPK 304W Descriptive Statistics & the Normal Distribution
Objectives Define what is nerve conduction study (NCS) and electromyography ( emg) . Explain the procedure of NCS using Abductor Pollicicis Brevis muscle.
Kin 304 Descriptive Statistics & the Normal Distribution
Week 14 Chapter 16 – Partial Correlation and Multiple Regression and Correlation.
Examples of motor and sensory studies for the ulnar nerve
Statistical Methods For Engineers
BPK 304W Descriptive Statistics & the Normal Distribution
Evaluation of measuring tools: reliability
Linear Regression and Correlation
Linear Regression and Correlation
Clinical prediction models
Journal reviews 이승호.
Presentation transcript:

Electrophysiologic Measures of Ulnar Sensory Nerve Function: Electrophysiologic Measures of Ulnar Sensory Nerve Function: Reference Values and Reliability Reference Values and Reliability ABSTRACT PURPOSE: Determine reliability and clinical reference values for electrophysiologic measures of ulnar sensory nerve. SUBJECTS: 100 disease free volunteers. METHODS: Surface antidromic ulnar sensory nerve conduction studies were performed; sensory nerve evoked potentials (SNAP) were elicited from stimulation at wrist, below-elbow, and above-elbow sites. Thirty subjects were tested a second time for intrarater reliability analyses. ANALYSES: Sensory distal latency (SDL) and sensory nerve conduction velocity (SNCV) were calculated to the onset and to the peak of the evoked potentials; SNAP amplitude measured from onset to negative peak. Intrarater reliability analyses were performed. Multiple linear regression models were constructed to examine the use of age, body mass index, gender, and finger girth to predict forearm and across- elbow SNCV and SNAP amplitude. RESULTS: Intraclass correlation coefficients (ICC [3,1]) were greater than 0.85 for all paired measures of SDL, SNCV, and SNAP amplitude. Reference values computed from data optimally transformed to minimize skew were: SDL measured to onset and peak 2.34 msec and 3.11 msec; forearm SNCV measured to onset and peak 54 m/sec and 53 m/sec; across-elbow SNCV measured to onset and peak 47 m/sec and 52 m/sec; SNAP amplitude from wrist, below elbow, and above elbow stimulation 10.79µV, 4.66µV, and 3.33µV; SNAP amplitude decrement across the elbow 46%. No independent variable accounted for more than 5% of the variance in across-elbow SNCV; finger girth accounted for approximately 28% of the variance in above-elbow SNAP amplitude. CONCLUSION: No strong statistical models for prediction of SNCV or SNAP amplitude could be derived from the limited set of predictor variables. The reliability analyses in the current study suggest that these ulnar sensory reference may be used with confidence. ANALYSES Intraclass correlation coefficients (ICC) were calculated to establish intra-reliability for single measurements. (ICC 3,1) Seven separate multiple linear regression models were constructed to examine predictive relationships among the four independent variables and the seven dependent variables. The effect of sample size on descriptive statistics of the dependent variables was analyzed. Reference values were calculated from the optimally transformed data. The mean difference of the SNCV across the elbow to the SNCV of the forearm and the 95% CI around this difference were computed. CONCLUSION Ulnar SDL, SNCV, and SNAP amplitude are reliable measures. The reliability results from the current study, combined with relatively low levels of measurement error, suggest that clinical electrophysiologists can be confident using ulnar sensory reference values computed using the recommended elbow position and appropriate statistical methods. BACKGROUND Seven previous studies reporting reference values for the ulnar sensory nerve at the elbow; none met current reporting standards. One of these studies addressed the statistical limitations of using descriptive statistics designed for normative data when the data are not normally distributed by reporting data transformation; the other six did not address the data distribution. No previous published work has reported the reliability of ulnar sensory nerve conduction measurements. Various patient attributes may be predictive for electrophysiologic measurements. For ulnar sensory values at the elbow, only one of the reviewed studies explored of association and was for a single potential predictor: age. The previous reference studies used sample sizes of between 20 and 53 subjects. A minimum sample size of 100 has been suggested to allow reporting the percentile values using statistical guidelines for reference values. Thomas M. Ruediger, University of Michigan – Flint; Stephen C. Allison, Rocky Mountain University of Health Professions; Josef H. Moore, U.S. Army – Baylor University, U.S. Army Medical Department; Robert S. Wainner, Texas State University RESULTS Responses were obtained from all subjects. Intraclass correlation coefficients (ICC [3,1]) were greater than 0.85 for all paired measures of SDL, SNCV, and SNAP amplitude. Reference values (mean ± 2SD) were computed from data optimally transformed to minimize skew. No independent variable accounted for more than 5% of the variance in across-elbow SNCV; finger girth accounted for approximately 28% of the variance in above-elbow SNAP amplitude. Measures: Antidromic sensory nerve conduction studies of the ulnar nerve using surface electrodes were performed on 100 disease free volunteers. Sensory nerve evoked potentials were elicited from stimulation at the wrist, below-elbow, and above-elbow sites in all subjects. Sensory distal latency (SDL) and sensory nerve conduction velocity (SNCV) were calculated to both the onset and to the peak of the evoked potentials; sensory nerve action potential (SNAP) amplitude was measured from onset to negative peak of the evoked potential. Thirty subjects were tested a second time to collect data for intrarater reliability analyses. Multiple linear regression models were constructed to examine the use of age, body mass index, gender, and finger girth to predict forearm and across-elbow SNCV and SNAP amplitude. PURPOSE To determine reliability and clinical reference values computed with appropriate statistical methods for electrophysiologic measures of ulnar sensory nerve function using data from healthy subjects obtained with the recommended elbow testing position. SDLFA NCVAE-BE NCVAmplitude Amplitude change across the elbow Msecm/sec (µV) OnsetPeakOnsetPeakOnsetPeakWristBEAE Mean % SD % Min % Max % Percentile % Kurtosis N/A Skew N/A CV8% 7%14%13%36%47%50% Descriptive statistics – untransformed electrophysiologic variables SDLFA NCVAE-BE NCVAmplitude msecm/sec (µV)% OnsetPeakOnsetPeakOnsetPeakWristBEAE AE-BE Change Mean SD Untransformed Transformed* Percentile Lowest Reference values for electrophysiologic measures SUMMARY Reference values for SNCV and SNAP amplitude were computed using recommended statistical methods and elbow position. No strong statistical models for prediction of SNCV or SNAP amplitude could be derived from the limited set of predictor variables. Statistical transformation methods to correct for minor skewness in the data distributions improved reference value determination for most measurements, even though raw data distributions were reasonably normal. METHODS This study was approved by the Institutional Review Boards of both Walter Reed Army Medical Center and Rocky Mountain University of Health Professions. All volunteers read and signed an informed consent document. Reference values calculated from data : mean + 2 SD for the SDL, mean – 2SD for SNCV and SNAP amplitude. The 97.5th percentile values were used for SDL, and 2.5th percentile values for NCV and SNAP amplitude. SDLFA NCVAE-BE NCVAmplitude OnsetPeakOnsetPeakOnsetPeakWristBEAE ICC (3,1) CI – Upper Bound CI – Lower Bound Intraclass Correlation Coefficients (3,1)