common reference intervals an introduction
TRANSCRIPT
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Common Reference Intervals
An Introduction
Graham Jones
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Thank you
• Organisers
• Presenters
• Participants
• AACB
• Sonic Healthcare
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Today
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Today
• Working Session – Building a new future
• Seeking decisions and outcomes!
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What are reference intervals?
• “they are plus and minus 2 standard
deviations of a normal population, aren’t
they?” – Medical students
– AACB exam candidates
– FRCPA candidates
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Setting Reference Intervals
• Also known as: Asteriskology*
* The science / art / skill of putting
asterisks# on the correct results
# or other flags eg, H / L
* *
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Why we need reference intervals?
• They are “the most common decision support tool for numerical pathology results”.
• In the absence of other decision points (eg diabetes cuttoffs for serum glucose) they are all we have.
• NPAAC / NATA requires their:
– Presence on reports.
– Source in a reference manual.
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Why we need reference intervals?
• They have a simple basis
– Separate those results likely to be affected by
disease from those unlikely to be affected.
• This basis is the same for all tests.
• They can provide allowances for method
differences (bias)
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Why we need good reference intervals?
• We put them on every report, we might as well put correct ones.
• The effects of poor reference intervals are considerable
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Reference Interval Errors - Bias
2.5 %
12 % 0.2 %
2.5 %
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Reference Intervals - Too Wide/Narrow
10 % 10 %
80 %
0.1 % 0.1
%
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The effects of poor reference intervals?
• Further investigation of wrong patients
• Lack of further investigation of right patients
• Over / under classification of population as
“normal” or “abnormal”
• Reduced confidence of laboratory users.
• Flow-on effects on some decision points
– Three times URL
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New syndromes
• Dysasteriskosis
• Hyperasteriskosis
– Hypersuperasertiskosis
– Hyperinfrasteriskosis
• Hypoasteriskosis
• Sex-linked (age-linked) dysasteriskosis
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The caveats
Note that Reference Intervals:
• Do not define the presence of disease.
• Do not define the absence of disease.
• Are rarely evaluated as decision points
– (eg treat or further investigate if result outside population reference intervals).
• May be insensitive for individuals.
• Are set up to be “wrong” 5% of the time.
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How well are we doing?
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Reference Intervals – Alb Cr Ratio
Upper Reference
Limit Number
1.0 mg/mmol 3
2.0 mg/mmol 2
2.5 mg/mmol 5
2.5 (m) / 3.5 (f) 7
3.0 mg/mmol 1
3.5 mg/mmol 10
Highest over 3 x lowest
2011 Data
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Reference Interval Differences
• Different assays?
– Not related to assays (from Survey)
– No evidence of assay Difference
RCPA QAP Urine Albumin 2009 data
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Reference Intervals – Sources
Source Number
Local Data 5
Central Lab 14
Manufacturer 15
Literature 19
Don’t know 4
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Australia / NZ Survey: 2007 - 52 labs
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Data Summary
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%S
od
ium
Po
tassiu
m
Ch
lori
de
Calc
ium
Alb
um
in
Iro
n
Mag
nesiu
m
Uri
c a
cid
Ure
a
Cre
ati
nin
e
GG
T
Am
yla
se
Sample
LRL
URL
Scatter of results and reference intervals
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So Far...
• More scatter in Reference intervals than in
analysis
• Is the scatter in Reference Intervals due to
analytical differences?
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Reference Intervals v Results
2.5
3
3.5
4
4.5
5
5.5
6
6.5
3.5 4 4.5 5
SQ10
Re
fere
nc
e In
terv
als
F-URL
F-LRL
0
20
40
60
80
100
120
140
160
50 70 90
SQ10
Re
fere
nc
e In
terv
als
F-URL
F-LRL
ALP Potassium
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Same Method– Free T4
0
5
10
15
20
25
30
Bayer
PI
Lab 1
Lab 2
Lab 3
Lab 4
Lab 7
Lab 8
Lab 1
0
CC
LM
2001
CC
LM
2002
CC
LM
2003
SydP
ath
URL
LRL
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Statement of belief…
• Any study on reference intervals will show
a wide scatter!
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Where do Reference Intervals come from?
• Formal Reference Interval Studies
– Textbook answer (approved for exams)
• Manufacturer’s Product Information
– Requirement for release, commonly used
• Published studies
• Local Studies
• Database mining
• Relevant Guidelines
• Other laboratories
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Current Paradigm
• Based on recommendations from the
NCCLS and the IFCC
• Repeated in Product Information from most
reagent suppliers
• Encoded in the NATA summary of ISO/IEC
guide 15189.
– laboratories may perform their own detailed
reference interval studies
or
– may validate reference intervals published
elsewhere for their own methods and
populations
Each laboratory is responsible for
its own reference intervals
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Reference Interval Variation
• EVEN given the same data, laboratory
scientists WILL interpret it differently.
• Add in variability of data
reviewed
• Variation in Reference intervals:
– Always seen
– AN EXPECTED OUTCOME!
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Change of Paradigm
• Collective decisions
• Common Reference Intervals
(anything would be better)
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How wide? – Patient Factors
• CVg – group CV (of individual set points)
• CVi – within-individual CV
• CV(ref int) = √(CVg2 + CVi2)
Coefficient of variation
?
TO KEEP 95% of “normal” Results within Interval
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How wide? – Sample Factors
• CVg – group CV (of individual set points)
• CVi – within-individual CV
• CVpa – pre-analytical variation
• CV(ref Int) = √(CVg2 + CVi2+ CVpa2)
Coefficient of variation
?
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How wide? + Measurement Factors
• CVg – group CV (of individual set points)
• CVi – within-individual CV
• CVpa – pre-analytical variation
• CVa – analytical CV
• CV(ref Int) = √(CVg2 + CVi2 + CVpa2 + CVa2)
Coefficient of variation
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Analytical CV
• CVa increases with:
– More calibrations, more time
– More lot numbers of reagent and calibrator
– More instruments, more laboratories
– More methods, more manufacturers
• Higher CVa Wider Reference Interval
• A common reference interval will (usually)
be wider than a single site RI
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Between-method / Cal lot CV
• Average method bias depends on:
– Selected accuracy base (eg SRM, method)
– Accuracy of accuracy base
• Transfer of value from “higher order”
standard to calibrator
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130 132 134 136 138 140 142 144 146 148 150
OP-Meth A
Rur-Meth Bcorr
Between method biases – Options:
- Difference Intervals
- Wider shared interval
- Fix bias
Sodium data extracts
2 laboratories
2 methods
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Reference Interval?
• Clinical Decision points:
– Based on trial outcomes
– Not testable in the lab
– Need to work with clinical groups
– Assay quality remains vital
• Examples:
– Glucose, Hba1c, Lipids, eGFR
• RI or clinical decision point(s)
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Common Reference Intervals
• What interval will we use?
• Access data widely:
– Formal studies
– Publications
– Data extracts
• Do we have a good interval?
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Pre-analytical factors
• Are pre-analytical factors relevant?
• Are laboratories different?
• Eg. sample handling / stability, tourniquet
use, low level haemolysis, serum v
heparin
• A: Not relevant / Relevant how?
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Population Differences
• Inpatient v outpatient
• Racial?
• Geographical
• 1. Are there known differences?
• 2. Do I know about the difference?
Does this stop the use of a common RI?
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Statistics
• Central 95%
• Lower 95%
• Lower 97.5%
• Lower 99%
• Lower Other
• Central other?
• What Statistical Principle?
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Partitioning
• Separate intervals for different groups
• Sex?
• Age
– Paediatric?
– Geriatric?
– Other?
• Reproductive
– Pregnancy?
– Puberty, menstrual cycle, menopausal?
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Analytical factors
• BIAS • Precision (affects bias)
• Interference
• Non-specificity
• Are assays close enough to share intervals
• Nature of interval affects allowable bias
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Criteria for sharing
For tests with Gaussian Distribution
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Process and People
• Interval to Share
• Assays close enough
• Process to decide
• Implementation
– Criteria for accepting in a lab
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Checklist for setting an RI
1. Define analyte (measurand)
2. Define assays used, accuracy base, analytical specificity
3. Consider important pre-analytical differences, actions in
response to interference
4. Define distribution of RI values (e.g. central 95/97%%, etc)
5. Describe evidence for merging of RIs
• data sources (literature, lab surveys, manufacturer)
• data mining
• bias goal as quality criterion for acceptance
6. Consider partitioning based on age, sex, etc
7. Define degree of rounding
8. Clinical considerations of the RI
9. Consider use of common RI
10. Document and implement.
Jones GD, Barker T. Reference intervals. Clin Biochem Rev 2008;29 Suppl S93-97.
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We are not alone…
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Other activities
• Pathology Units and Terminology (PUTS)
• RCPA project
• QUPP Funded
– Units
– LOINC codes
– Test names
• Robert Flatman chair of Biochemistry
Group
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• Panansterisktic state
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• Anasterisktic state