common reference intervals an introduction

Common Reference Intervals

An Introduction

Graham Jones

Thank you

• Organisers

• Presenters

• Participants

• AACB

• Sonic Healthcare

Today

Today

• Working Session – Building a new future

• Seeking decisions and outcomes!

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

Setting Reference Intervals

• Also known as: Asteriskology*

* The science / art / skill of putting

asterisks# on the correct results

# or other flags eg, H / L

* *

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.

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)

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

Reference Interval Errors - Bias

2.5 %

12 % 0.2 %

2.5 %

Reference Intervals - Too Wide/Narrow

10 % 10 %

80 %

0.1 % 0.1

%

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

New syndromes

• Dysasteriskosis

• Hyperasteriskosis

– Hypersuperasertiskosis

– Hyperinfrasteriskosis

• Hypoasteriskosis

• Sex-linked (age-linked) dysasteriskosis

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.

How well are we doing?

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

Reference Interval Differences

• Different assays?

– Not related to assays (from Survey)

– No evidence of assay Difference

RCPA QAP Urine Albumin 2009 data

Reference Intervals – Sources

Source Number

Local Data 5

Central Lab 14

Manufacturer 15

Literature 19

Don’t know 4

Australia / NZ Survey: 2007 - 52 labs

Data Summary

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%S

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Scatter of results and reference intervals

So Far...

• More scatter in Reference intervals than in

analysis

• Is the scatter in Reference Intervals due to

analytical differences?

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

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F-URL

F-LRL

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20

40

60

80

100

120

140

160

50 70 90

SQ10

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F-URL

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ALP Potassium

Same Method– Free T4

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5

10

15

20

25

30

Bayer

PI

Lab 1

Lab 2

Lab 3

Lab 4

Lab 7

Lab 8

Lab 1

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CC

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2002

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Statement of belief…

• Any study on reference intervals will show

a wide scatter!

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

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

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!

Change of Paradigm

• Collective decisions

• Common Reference Intervals

(anything would be better)

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

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

?

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

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

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

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

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)

Common Reference Intervals

• What interval will we use?

• Access data widely:

– Formal studies

– Publications

– Data extracts

• Do we have a good interval?

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?

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?

Statistics

• Central 95%

• Lower 95%

• Lower 97.5%

• Lower 99%

• Lower Other

• Central other?

• What Statistical Principle?

Partitioning

• Separate intervals for different groups

• Sex?

• Age

– Paediatric?

– Geriatric?

– Other?

• Reproductive

– Pregnancy?

– Puberty, menstrual cycle, menopausal?

Analytical factors

• BIAS • Precision (affects bias)

• Interference

• Non-specificity

• Are assays close enough to share intervals

• Nature of interval affects allowable bias

Criteria for sharing

For tests with Gaussian Distribution

Process and People

• Interval to Share

• Assays close enough

• Process to decide

• Implementation

– Criteria for accepting in a lab

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.

We are not alone…

Other activities

• Pathology Units and Terminology (PUTS)

• RCPA project

• QUPP Funded

– Units

– LOINC codes

– Test names

• Robert Flatman chair of Biochemistry

Group

• Panansterisktic state

• Anasterisktic state