Reference Intervals for New Methods Dr Graham Jones

Reference Intervals for New Methods Dr Graham Jones Department of Chemical Pathology St Vincent’s Hospital, Sydney www. sydpath. stvincents. com. au

Reference Intervals for new Methods Subtitle: “Reference Intervals”

Contents • Introduction to reference intervals • Reference intervals for the new method – Derive de-novo – Transfer from old method – Literature – Other Laboratories • Conclusions

Defining Reference Intervals • Central 95% of results from a reference population – IFCC/NCCLS definition • Excludes 2. 5% above and below interval • For healthy population are “Health-associated Reference intervals” • Can be any population, but must be defined – eg, pregnant, premature, hospitalised, treated.

Other forms: • Other statistical cuttoffs – Troponin: 99 th centile of healthy population – Apo (a): 80 th centile of total population • Recommended interval (decision point) – Impaired fasting glucose (6. 1 - 6. 9 mmol/L) – Target LDL concentration (<2. 0 mmol/L) • Therapeutic Interval – Drugs, INR, APTT, TSH

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 17025. – laboratories may perform their own detailed reference interval studies or – may validate reference intervals published elsewhere for their own methods and populations

Generating a new reference interval • • • Define and select reference population* Define collection conditions and numbers Collect samples Analyse samples Perform statistical evaluation* Put into practice • Tietz Textbook covers standard approach very well (HE Solberg)

Define Reference Population • Source – eg blood bank, lab volunteers, students • Numbers • Exclusions • Likely Partitioning – Age – Sex – Other • Difficult to get extremes of age and high numbers

Study Imprecision • Estimates of reference limits has limitations • Expressed as the confidence interval of the Reference Limits, eg 90% CI of the upper and lower reference limits • Confidence intervals decrease as the number of people in the study increases. Large n Small n

Non-parametric statistics • Lowest number where error envelope can be calculated is 120 • For n=120 – 2. 5 th centile is 4 th lowest result – 90% confidence limit for LRL is lowest sample and 7 th lowest sample • These values often very scattered giving wide intervals

NORIP STUDY Female ALT (n=1220) 11 19 ALT (U/L) 27 33 41 49 57 Female Upper Reference Limit: 45. 6 (90% CI 42. 5 – 49. 3, n=1220) Male Upper Reference Limit: 68 (90% CI 63. 4 – 73. 6, n=1080)

Generating Intervals • Is hard to do well • Requires time and effort and money • But any local data may be very useful for validation of other intervals

“Impossible” Intervals • Some reference intervals are essentially impossible to produce from local studies: – Paediatric intervals – Stages of pregnancy (eg h. CG in 5 th week) – Stages of menstrual cycle – Nutritional parameters • Reflects local diet • May normalise deficiency state

Transfer Intervals from previous method • Implies previous intervals are good – Check source and validity • Transfer requires good correlation • Advantage is clinical acceptance – Note: much the following data related to introduction of a Bayer Centaur for Vitamin B 12.

Transferring Intervals Wide range of results, assayed over several days, excellent correlation And linearity. Transfer with no problem

Transferring Intervals – more difficult BAYER 181 (top of deficient) 156 (bottom of normal) BECKMAN 107 (bottom of normal) 126 Syd. Path 133 (top of deficient) 95% Confidence Limits Slope: 0. 78 – 0. 86 Intercept: 28 - 48

Correlation Data • Patient samples • Focus on results near limits • Beware effect of extreme values on statistics – Passing and Bablock preferred to linear regression • Use correlation data from several days and calibrations • Review source of previous Intervals

Validation of reference intervals • NCCLS protocol • Measure 20 samples appropriate for reference interval on new method • Exclude outliers • If 2 or fewer are outside proposed inetrvals – Accept intervals • If >2 are outside proposed intervals – Measure another 20 – If 2 or fewer are outside – accept intervals • Cannot detect overly wide intervals

Review Previous Method • Previous method may have significant amounts of data (information) • For many assays many of the results will be on “normal” patients • For all assays will allow assessment of previous reference intervals • Methods: – Inspection – Frequency histograms (all data, some data) – Formal methods (Bhattacharya)

Access 126 pmol/L 5. 8% rate of “low” results Centaur - predicted 180 pmol/L cuttoff 16% positive rate Assess effect of possible Assay change

Data Mining old results • Bhattacharya, LG. Journal of the Biometric Society. 1967; 23: 115 -135. • Example data: Frequency Distribution of the forkal length of the Porgy caught by pair-trawl fishery in the East China Sea.

Bhattacharya • Assumes Gaussian (or Log Gaussian) distributions • Assumes a significant proportion of requests are on unaffected individuals Creatinine

Data Mining • Bhattacharya ignores effects of outliers and samples not part of majority distribution. • Reference intervals based on majority. Creatinine (mmol/L)

Literature • Look for same method • Equivalent population • Sources – Peer-reviewed publications – Gray Literature • Abstracts (eg AACB, AACC, ACB) – Company literature • Product information (PI) • Other

Literature sources • Vital where population reference intervals may be of limited use • Dietary factors • Special groups – Eg paediatrics • Numbers are prohibitive – eg 99 th centile for troponins • Following examples taken from Syd. Path data for creatinine (Roche) and Vitamin B 12 (Centaur)

Combining data • Local and blood bank (M 101, F 110) M: 62 – 105 umol/L F: 51 – 82 umol/L. • Literature: • South Australia (M 293, F 269) Mazzachi BC et al, Clin Lab. 2000; 46: 53 -55 M: 62 – 106 umol/L F: 44 – 80 umol/L • Germany (M 127; F 125) Junge et al. Clin Chem Acta. 2004; 344: 137 -148 M: 63 – 103 umol/L F: 48 – 85 umol/L • Values rounded out as follows: M: 60 – 110 mmol/L F: 40 – 90 mmol/L

Literature Sources - distribution 180 pmol/L 150 300 450 600 750 Vitamin B 12 – ACS: 180 Klee 2000 (pmol/L)

Homocysteine and Methylmalonic acid relative to serum B 12 (Centaur) Homocysteine MMA 1562 people, age >65. MMA and Homocysteine. B 12 measured on Bayer Centaur Bin width 50 pmol/L. Red Arrow 200 pmol/L. - Clarke et al, Am J Clin Nutrit. 2003; 77: 1241 -7.

Dorevich Pathology Sikaris et al 25, 201 B 12 measurements ACS: 180 and Bayer Centaur Central 95% of results with Normal Hb and MCV: 178 - 741 pmol/L

Vitamin B 12 v MCV 126 • Syd. Path Data (3 months, 1497 results) • Beckman-Coulter Access

Product Information VB 12, pmol/L 588 441 294 147 181 pmol/L 156 pmol/L 140 pmol/L

Centaur Vitamin B 12 • 6 studies using Centaur or ACS: 180 • • Product information 3 x refereed publications 1 x AACB abstract 1 x local study (NZ) • Data combined to make reference interval – Deficient <120 pmol/L – Indeterminate 120 – 180 pmol/L – Replete >180 pmol/L

Product Information… Manufacturer’s Interval: well-defined population, appropriate exclusions But: Outliers? , bi/trimodal distribution?

Other laboratories • If someone has done the work, and uses the same method, review their work and apply the intervals. • Need to verify assay bias. • Collaborative effort between several labs with the same method may be a powerful method of setting reference intervals – Spanish Group – NORIP: http: //wip. furst. no/norip/

Combining Laboratories • 13 Spanish laboratories (all Centaurs) • 11 – 15 samples from each laboratory (tot 150 samples) • Combined data used for Reference Intervals – Ferre-Masferrer et al. Clin Chem Lab Med 2001; 39: 166 -169

Other Sources: accuracy base • In order to share method-specific literature need to ensure assay accuracy. • “Is my Bloggs method for X working the same as everyone else’s Blogg’s method? ” • QAP results – measure QAP samples – look up results for method group • QC material target values • Shared samples

Comparison with QAP targets Method-specific medians (and scatter) available on QAP website

Plea to manufacturers. . . • Searching refereed literature by trade names can be difficult – ie Abbott, Elecsys, Immulite, Vitros are terms that are not often searchable in Medline, pubmed etc • If companies keep a resource library of information it would be very useful. • Note “google Scholar” can be useful – http: //scholar. google. com

Clinical Input • Previous slides about Vitamin B 12 are taken from a presentation to haematologists at St Vincent’s Hospital • Actively seeking their input on decision points • Allows inclusiveness and practical input

Putting it all together • Different sources will give (slightly) different values. • Judgement is required to combine data • Other factors include: – Precision of intervals – Long term precision of assay(s) – Biological variation – Rounding for ease of memory – Partitioning

Implementing • Recommend temporary footnote • eg change in method and change intervals, see lab for further details • Make further details available if needed – source document (NATA) – Handout – Website

Conclusions • A new method is a good time to review reference intervals • Uncritical transfer of old intervals is bad practice • Many sources of information can be used • Judgement is required for final decision • Working with other labs may be of great benefit