Björn FalckDepartment of clinical neurophysiologyUniversity hospitalUppsala, Sweden

Reference values in clinical neurophysiology

Clinical decision process

Patient doctor contactHistory Clinical findings

Tests

Diagnostic conclusionIs the diagnostic

conclusion sufficiently certain?

Therapeutic decision

Patient perceives problem

collection of data

Hypothesis

yes

no

??????

Are test results normal?Are test results compatible with a specific disorder?Does this disorder explain the patients problem?

Definition of normality

Typical; usual: according to rule or standardIn psychiatry denoting a level of effective functioning that is satisfactory both to persons and their social milieusNot diseased

Definition of normality

L. normalis, according to pattern

How do we know what is normal?

From experienceBy traditionStatistical definitionRelation to healthRisk for disease or association with diseaseCompare with similar structure assumed to be healthy

Reference values: terminology

International Federation of Clinical Chemistry

Reference individual

Reference population

Reference sample group

Reference values

Reference distribution

Reference limits Reference interval Reference models

Disease related decision limits

Disease related reference individual

Disease related reference population

Disease related reference sample group

Disease related referencee values

Risk of disease

Decision limits

Selection of reference individualsHealthy subjects

Supernormal subjects may lead to very narrow reference limits and false positive findings

Patients referred for examination for unrelated disorders

Patient referred for CTS, but otherwise healthy, lower exremeties may be used

Cannot be usedPatients referred to examination for disorders but found to be healthy: upper extremeties of patients with suspected CTS

N.peroneus profundus

N.peroneus profundus N.peroneus profundus

16 year old male 76 year old female

Conduction velocity and age

age (years)

100806040200

cond

uctio

n ve

loci

ty (m

/s)

60

50

40

30

20

10

0

gender

female

male

Total Population

Rsq = 0,0888

Conduction velocity and height

height (cm)

200190180170160150140

cond

uctio

n ve

loci

ty (m

/s)

60

50

40

30

20

10

0

gender

female

male

Total Population

Rsq = 0,0457

Conduction velocity and temperature

segment temperature (C)

363432302826

cond

uctio

n ve

loci

ty (m

/s)

60

50

40

30

20

10

0

gender

female

male

Total Population

Rsq = 0,0768

Description of sample population

age (years)

90,080,070,060,050,040,030,020,0

70

60

50

40

30

20

10

0

Std. Dev = 15,44 Mean = 45,8

N = 235,00

Reference values: Statistical methods

Descriptive statisticsMean, sd, skewness, kurtosis

Identification of outliers and influential observationsRegression models

Influence of independent variables (age, gender, height, temperature)

Descriptive statistics

N min max mean sd

Cv (m/s) 219 24.8 54.3 39.5 5.1

Ampl (uV) 235 0 5.8 1.2 1.1

Identification of outliers

segment length (mm)

200180160140120100806040200

squa

re ro

ot o

f am

plitu

de, (

uV)

5

4

3

2

1

0

-1

gender

female

male

Total Population

Rsq = 0,2037

Removal of outliers

segment length (mm)

200180160140120100806040200

squa

re ro

ot o

f am

plitu

de, (

uV)

5,0

4,0

3,0

2,0

1,0

0,0

-1,0

gender

female

male

Total Population

Rsq = 0,1396

CV is normally distributed

conduction velocity (m/s)

57,053,0

49,045,0

41,037,0

33,029,0

25,0

40

30

20

10

0

Std. Dev = 5,14 Mean = 39,5

N = 219,00

Skew distribution of amplitude

amplitude (uV)

5,755,25

4,754,25

3,753,25

2,752,25

1,751,25

,75,25

80

70

60

50

40

30

20

10

0

Std. Dev = 1,08 Mean = 1,17

N = 235,00

Square root of amplitude

square root of amplitude, (uV)

2,802,402,001,601,20,80,40,00-,40-,80

100

80

60

40

20

0

Std. Dev = ,50 Mean = ,96

N = 235,00

Linear regression model

y = constant + a*x1 + b*x2 + c*x3

F latency n.peroneus = -14.93 + 0,08 *age(years) + 0,32 * height (cm)

Stepwise linear multiple regression

Dependent variableConduction velocitySquare root of amplitude

Independent variablesAgeHeightGenderSegment temperatureSegment length

Conduction velocity - no model

mean sd cv (m/s) 39.5 5.1

Regression model for CV - step 1

Coefficients significance constant 44.14 0,000 age (years) -0,11 0,000 Sd 4,92 R2 0,09

Regression model for CV - step 2

coefficients significance constant 16.75 <0.0001 age (years) -0.13 <0.0001 seg.temp. 0.92 <0.0001 Sd 4.61

Regression model for CV - step 3

coefficients significance constant 47.43 <0.0001 age (years) -0.58 <0.0001 seg.temp. 1.02 <0.0001 height (cm) -0.19 <0.0001 sd 4.29 R2 0.31

Regression model for CV

CV = 47.73 - 0.16*age + 1.02*seg.temp - 0.19*height

Segment length and gender not included in the model

Sd of the CV regression models

sd no model 5.2 age 4.9 age+seg temp 4.6 age+seg temp+height 4.3

R2 of the models for CV

R2 age 0.09 age, seg.temp. 0.20 age, seg.temp., height 0.31

R2 = Coefficient of determinationProportion of the variability of the dependent variable is explained by the model

Calculation of the standard score (Z)

Z =measured value - expected value

standard deviation

Using Z-score to predict abnormality

% of population observed Z-score one-tailed two-tailed 1 15,87 13,36 2 2,28 4,55 2,5 0,62 1,24 3 0,13 0,27

Is measured value abnormal or not?

Reference distribution mean sd cv (m/s) 39.5 5.2

Measured CV 29.0 m/sAge 50 yearsHeight 180 cmTemp 30 C

Conduction velocity - no model

mean sd cv (m/s) 39.5 5.2

39.5-2*5.2=29.1

Abnormal!

Calculation of expected CV value

Age = 50 yearsheight = 180 cmtemp = 30 C

Expected CV (m/s) = 51,5 -0.18*50+0,85*30-0,18*180 = 35.3

CV (ms) = 51,5 -0.18*age (years)+0,85*seg temp (C)-0,18*height (cm)

Calculation of Z score

Z = (observed value - expected value)/sd

Z = (29,0 - 35,2)/4,6 = -1,32

Probability of Z score >2 number of tests

number of abnormal findings (> 2 sd) 1 2 3 5

1 0,023 2 0,045 0,001 5 0,110 0,005 0,000 0,000 10 0,208 0,021 0,001 0,000 25 0,441 0,112 0,019 0,000 50 0,688 0,320 0,108 0,006

Multicenter databases

Advantages of multicenter databases

Reference values for several muscles and nerves can easily be obtainedStandardization of methodsDetails have been discussedLarge material collected in shorter timeVariations in user´s techniques includedResults receive wider acceptanceFindings between labs can be compared

No reference values are available?

Homologous nerveMedian-ulnar comparison

Compare with opposite sideBeware of bilateral disorders (entrapments)

Compare with adjacent segments of nerveProximal and distal segment of ulnar nerve

Use common sense

Homologous nerveMedian and ulnar nerves in the hand

AGE

908070605040302010

CV

70

60

50

40

30

ulnar nerve

median nerve

Comparison of homologous nerves

Group Statistics

72 20,208 9,205 1,08572 35,050 16,668 1,96472 57,986 4,819 ,56872 58,594 4,426 ,522

NERVEULNMEDULNMED

AMP_P

CV

N Mean Std. DeviationStd. Error

Mean

Antridromic sensory median (D2) and ulnar (D5) nerve

Comparison of homologous nerves

-6,614 142 ,000 -14,842 2,244 -19,278 -10,406-,789 142 ,432 -,608 ,771 -2,133 ,916

AMP_PCV

t df Sig. (2-tailed)Mean

DifferenceStd. ErrorDifference Lower Upper

95% ConfidenceInterval of the

Difference

t-test for Equality of Means

Antridromic sensory median (D2) and ulnar (D5) nerve

Very mild carpal tunnel syndrome

Median sens

Ulnar sens

Right-left differences

Paired Samples Statistics

57,970 33 3,961 ,69059,5273 33 4,8354 ,841730,479 33 14,252 2,481

39,7273 33 17,3946 3,0280

CVCVL

Pair1

Amplitude, rightAmplitude, left

Pair2

Mean N Std. DeviationStd. Error

Mean

Median nerve wrist-digit 2, antidromic sensory

Side differences

Paired Samples Test

-1,5576 4,4602 ,7764 -3,1391 2,396E-02 -2,006 32 ,053

-9,2485 11,1660 1,9438 -13,2078 -5,2892 -4,758 32 ,000

CV - CVLPair 1Amplitude, right- Amplitude, left

Pair 2

Mean Std. DeviationStd. Error

Mean Lower Upper

95% ConfidenceInterval of the

Difference

Paired Differences

t df Sig. (2-tailed)

How to use reference values in medical decisions?

Ideal test

healthy subjects

CTS

CV m/s 4565 35 2555

reference interval

Distribution of CV in CTS

healthy subjects CTS

CV m/s 4565 35 2555

reference interval

Sensitivity

SENSITIVITY= TP/(TP+FN)

TP = number of true positive tests in patients with the disease

FN = number of false negative tests in patients with the disease

the probability of having a positive test result given the patient has the disease

Specificity

SPECIFICITY= TN/(TN+FP)

the probability of a negative test result given that the patient does not have the disease

TP = number of true positive tests in patients with the disease

FP = number of false positive tests in patients with the disease

Calculation of sensitivity and specifity

disease + disease - total

Test + TP FP TP+FP

Test - FN TN FN+TN

Total TP+FN FP+TN TP+TN+FP+FN

Predictive value of a positive test result

Positive predictive value = TP/(TP+FP)

The probability of that the subject has the disease if test result is positive

TP = number of true positive tests in patients with the disease

FP = number of false positive tests in patients with the disease

Predictive value of a negative test result

Negative predictive value = TN/(TN+FN)

Probability that the subject does not have the Probability that the subject does not have the disease if test result is negativedisease if test result is negative

TN = number of true negative tests in patients with the disease

FN = number of false negative tests in patients with the disease

Calculation of predictive value

disease + disease - total

Test + TP FP TP+FP

Test - FN TN FN+TN

Total TP+FN FP+TN TP+TN+FP+FN

Screening population

disease + disease - total

Test + 82 36,996 37,078

Test - 18 962,904 962,922

Total 100 999,900 1,000,000

Prevalence of porphyria 1/10000. Probability for disease = 0.0001, sensitivity of test = 0.820, specificity of test = 0.963

Predictive value of abnormal test = 82/37,078 = 0,0022

Screening large family

disease + disease - total

Test + 410 19 429

Test - 90 481 571

Total 500 500 1000

Testing of a large family with porphyria, autosomal dominant disease: probability = 0.50, sensitivity of test = 0.820 and specificity of test = 0.963

Predictive value of abnormal test = 410/429 = 0,96

Individual patient in a clinical setting

disease + disease - total

Test + 246 26 272

Test - 54 674 728

Total 300 700 1000

No heredity of porphyria, but based on clinical suspicion the prior probability of the disease is = 0.30, sensitivity of test = 0.820 and specificity of test = 0.963

Predictive value of abnormal test = 246/272 = 0.90

Validity

Extent to which a parameter reflects what it is designed to measureHow well does the amplitude of sensory nerve action potential reflect the number of axons

Degrees of validityFace validity

subjective impressionEcological validity

is the test practical to use?Criterion related validity

comparison with other independent measurescomparison with a gold standard

Predictive validityhow well does the test predict disease and health

Interpretation of test resultsConsistency of test Validity of testSensitivity and specifity of testPositive and negative predictive valuePrevalence of disease diagnosed in the laboratoryNumber of tests performedMagnitude of abnormalityClinical situationPattern of abnormal findings

Interpretation of findings

Number of tests performedMagnitude of abnormalityClinical situationPattern of abnormal findings

Interpretation of multiple tests Interpretation of multiple tests

Number of tests performedMagnitude of abnormalityClinical situationPattern of abnormal findings

Game over