Transcript Biological Variation Data

Dr Bill Bartlett
Joint Clinical Director
Diagnostics Group
Biochemical Medicine
Ninewells Hospital & Medical School
NHS Tayside
Scotland UK
[email protected]
Diagnosis
• Prognosis
• Monitoring
• Screening
• Assessment of
Risk
•
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The metrology
An understanding of its relativity to a point of
reference
 Unusual
 Change
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Biological Rhythms (time)
Homeostasis
Age
Sex
Ethnicity
Pathology
Response to Stimuli
eGFR > 60 in a 30 year old white female: Changing renal function?
Grasbeck & Saris 1969
Introduced the term “reference value”:
The mode of generation of such values is known with respect
to: 
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Selection of subjects
Assessment of state of health
Population characteristics, age, sex,
Specimen collection and storage
Analytical technique and performance characteristics
Data handling techniques.
1.
2.
3.
The Concept of Reference Values. 1987;25:337-342
The selection of Individuals for the Production of
reference values. 1987;25:639-644
Preparation of individuals and collection of
specimens for the production of reference intervals.
1988;26:593-598
4.
5.
Control of analytical variability in the production of
reference values. 1991;29:531-535
Statistical treatment of collected reference limits.
1987;25:645-656
6.
Presentation of observed values related to reference
values. 1987;25:657-662
J Clin Chem Clin Biochem
This looks nice so
far , but what is
the use of
biological variation
data?
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Analytical variance (CVA ).
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Within Subject biological variance (CVI ).
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Between Subject biological variance (CVG )..
s2Total = s2Analytical + s2Individual + s2Group
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Setting of analytical goals (CVgoal).
Quality specifications for :
 total allowable error (TEA)
 Bias (BA )
Evaluating the significance of change in serial results
(RCV).
Assessing the utility of reference intervals (Index of
Individuality).
Assessing number of specimens required to estimate
homeostatic set points.
Choice of specimen type.
Timing of specimens.
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These fundamental data have many applications
that under-pin our practice.
We need to have confidence in the data and
understand its limitations.
Should we not have standards for their
production and characterisation?
www.biologicalvariation.com
Generation and Application of data on Biological Variation in Clinical Chemistry: Fraser CG, Harris EK. Crit Rev Clin Lab Sci 1989:27,(5), 409-435.
Optimal Conditions Precision.
Experimental
Design
Data Analysis
Assay
Characteristics
Uncertainty
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Purpose of study
Experimental Design
Characterisation of the methods
Data analysis
Confidence limits
What are the potential impacts of
error in the data?
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Biological Variation Database
www.westgard.com/biodatabase1.htm
CVI = 5.3% CVG = 14.2%
Desirable
CVA < 0.5 x CVI
BA< 0.25 x (CVI2 + CVG2)0.5
Tea < 1.65 x 0.5 x CVI. + 0.25 x (CVI2 + CVG2)0.5
Optimum
CVA < 0.25 x CVI
BA< 0.125 x (CVI2 + CVG2)0.5
Tea < 1.65 x 0.5 x CVI. + 0.125 x (CVI2 + CVG2)0.5
Minimum
CVA < 0.75 x CVI
BA< 0.0.345 x (CVI2 + CVG2)0.5
Tea < 1.65 x 0.5 x CVI. + 0.375 x (CVI2 + CVG2)0.5
www.westgard.com/biodatabase1.htm
n = [Z * (CVA2 + CVI2)/D] 2
D = % of closeness required
Biological variation data simulator. WWW.biologicalvariation.com
CVI = 5.3 %
CVG = 14.2%
CVA =2.7%
CVI = 5.3 %
CVG = 14.2%
Index of individuality = 0.4
Biological Variation Serum Creatinine: Average within subject (CVI) = 4.1%
Gowans & Fraser. Ann Clin Biochem 1988:25:259-263
Quantity
Units
Group
Mean
CVI
CVG
Index of
Individuality
Serum
Creatinine
µmol/L
Male (7)
83.9
3.4
6.8
0.54
Fraser
µmol/L
Female (8)
71.4
4.9
11.8
0.41
Fraser
µmol/L*
Whole (15)
83.9
4.1
14.1
0.29
Fraser
µmol/L
?
?
5.3
14.2
0.4
BioV Site
4.7
14.4
0.33
Reinhard
et al
µmol/L** N= 20
77
Male (7)
Female(13)
* Jaffe
** Enzymatic
M
G
F
M
G
F
CVG =14.1
CVG =4.1
Probability (%)
Starting
Creatinine
96 µmol/L
Creatinine µmol/L
Probability (%)
Starting
Creatinine
96 µmol/L
Creatinine µmol/L
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Upper Reference Limits:  Male = 106 µmol/L
 Female = 80 µmol/L
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RCV larger for men than for women.
If True: • Clinically important as disease progression needs
to be monitored and appropriate actions taken
(e.g. Acute on Chronic Kidney failure).
• Tighter analytical performance characteristics to
be applied for females.
• Impact will be greater on eGFR
CVI
Rise in Creatinine 4.3
5.3
Fall in eGFR
4.3
6.8
Assumes a CVA = 1%
% Change at %
Probability
95%
99%
10.3%
14.6%
12.6%
17.8%
12.8%
15.4%
16.0%
22.6%
"% Probability that %Rise in Serum Creatinine is
Significant
25
% Rise in Creatinine
20
15
10
5
0
50
55
60
65
70
75
80
85
90
% Probability that %Rise in Creatinine is Significant
95
100
"% Probability that % Fall in eGFR is Significant
25.0
% Fall in eGFR
20.0
15.0
10.0
5.0
0.0
50
55
60
65
70
75
80
85
90
% Probability that % Fall in eGFR is Significant
95
100
Significance of Fall in eGFR at CKD Classification
Boundaries
25
Fall in eGFR in
mL/min/1.73m2
20
90 mL
15
60 mL
45 mL
30 mL
10
15 mL
4 mL/min/1.73m2
5
0
65
70
75
80
85
90
95
% Probability that Fall is Significant
100
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Use eGFR for initial classification of CKD stage.
Use creatinine to follow patients with RCV
indicator flag?
 More Precise?
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Difficulty is that there is a suggestion that
creatinine CVI is variable in disease. Therefore
which CVI?
State of Health
CVI
Number of
Subjects
Length of
Studies
(days)
Number
Samples/Sub
Healthy Median?
4.3
CRF
5.3
17
21
8
Type 1 DM
5.9
27
56
8
Impaired renal
function
6.9
9
2
11
Type 1 DM
6.5
11
56
8
Post renal transplant
11.5
41
90
8
Acute MI
13.4
20
4
19.5
CKD children
13.0
54
540
9
Ricos et al Ann Clin Biochem 2007;44: 343-352
Experimental
Design
Data Analysis
Assay
Characteristics
What is the uncertainty?
What are the quality standards for BV Data?
40 years of
data
The Literature
• Do the data travel
through time
• Method
developments
Quality
Commutable
Translated
into
databases
• Enough reported
detail.
• Good Design?
• Population
demographics.
• Healthy?
• Diseased?
• Excellent
Resources
• Granular enough?
• Data archetype
required?
• 319 Constituents:
• 90 entries based on 1 Paper
ISSUES
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Non-complex v complex
molecules.
Improved assay specificity.
 Creatinine
 PTH
Longish history of evolving assay systems with differing
analytical performance characteristics and specificities.
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1970s – C-Terminal RIA
Late 80s – Sandwich IRMA Assay
1990 – 98 Nichols IRMA assays dominate
Late 1990s – variety of “intact” sandwich assays on a
number of different analytical platforms.
2004 – Bioactive PTH assay
Adapted from M Scott Focus 2010
Much evidence in the literature indicating that
assays react to varying extents with the variety
of PTH fragments present in Serum.
M Scott Focus 2010
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If clearance of fragments is not identical in all
patients and non diseased patients the apparent
biological variation will vary and be assay
specific.
Assay specificity an important BV qualifier?
Ankrah Tet et al. Ann Clin Biochem 2008;45:167-169
PTH = Nichols Advantage
4 Males 6 Females
“Normals”
Gardham et al . Clin J Am Soc Nephrol ePress May
24th 2010
Abbot Architect Intact PTH
Immunotopics Inc. Biointact PTH 1-84
12 “Normals” 22 Haemodialysis patients
Subjects
n
Assay
PTH
CVI
CVG
CVA
RCV N-Set*
(%)
23.8
5.0
72.3
27
ng/L
“Normal”
10
Nichols
51.7
25.9
“Normal”
12
Abbott
51.9
19.2
3.5
54.0
15
Immunotopics
Bio-intact 1-84
27.5
23.8
4.2
67.0
22
Abbott
303.0
25.6
3.6
72.0
26
Immunotopics
Bio-intact 1-84
131.0
30.2
6.3
86.0
37
Dialysis
22
* Number of Specimens Required to estimate homeostatic point
within 10% with a probability of 95%
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Data in chronic stable
disease “often can be
considered constant
over time and
geography”
“Same order of
magnitude in disease
and health”
Within Subject Variation (CVI,%) for Serum
Sodium and Urea
No. of
subjects
11
11
62
11
10
14
111
37
274
15
9
15
16
Time
Sex
status
Na+
Urea
0.5 h
8h
1d
2 weeks
4 weeks
8 weeks
15 weeks
22 weeks
6 months
40 weeks
2d
6 weeks
8 weeks
m
m
H
H
H
H
H
H
H
H
H
H
RF
HP
DM
0.6
0.5
0.6
0.7
0.9
0.5
0.6
0.5
0.5
0.7
0.8
0.8
0.8
2.2
6.0
4.8
12.3
14.3
11.3
15.7
11.1
11.2
13.9
6.5
14.5
13.0
Fraser 2001
m
m
F
m
m
-
F
m
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66 quantities 34 disease with 45 references.
“For the majority of quantities studied CVI of same
order as diseased. “
Disease specific RCVs may be necessary in some
cases.
Effect of variability in variability not quantitatively
studied.
“Heterogeneity in study designs and methods
compiled”
I’m healthy
and normal !
I’m a
biochemist!
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“Blood samples were taken at weekly intervals
from 10 healthy subjects (4 men and 6 women,
median age 21 years, range 19–27 years; mean
body mass index 21.3, range 19.0–25.9) for six
weeks at the same time of the day (between
12:30 and 14:30 h),”
Need to assess on a case by case basis.
• Questions around uncertainty.
• What are the implications for their application?
• Can the impact of uncertainty be quantified and
reduced where necessary.
• Accepted standard needed for their production.
• Critical appraisal checklist required to enable veracity of
existing and new publications.
• Meta-analysis of data
Questions to be addressed by the EFCC biological Variation
Working group
•
1.
2.
Define the purpose for which they are to be used.
Only meaningful and transferable if defined for
the population or individual in terms of: 
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Inclusion and exclusion criteria
Intake of food & drugs
Physiological and environmental conditions
Specimen collection criteria
Performance characteristics of the analytical method
The statistical methods used for estimation of the limits
3.
State of health defined.
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WHO Defn: “ a state of complete physical mental and social well being and not
merely the absence of disease or infirmity”
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Disease is a state of health.
Conceptually different in different countries.
The term “Reference” should be accompanied or preceded by a word
qualifying the state of health. E.g diabetic, hospitalised diabetic,
ambulatory diabetic, Healthy laboratory worker?
The reference change value: a proposal
to interpret laboratory reports in serial
testing based on biological variation.
C. RICO´ et al
Scand J Clin Lab Invest 2004; 64: 175 – 184
“The RCV data in this study are presented as a point of
departure for a widely applicable objective guide to
interpret changes in serial results.”
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HL7 recognised concept
Requests for additional flags pending
Fit for
Purpose?
Kinoull Hill, Perth Scotland.
Ruth Bartlett