NUMBER: Standardized Reference Intervals in the Netherlands using a big data approach

Apr
10
2018

Analytica Conference > DGKL / Room 4a > Reference Ranges – Data Mining at the Cusp of Clinical Translation

10. Apr 2018
16:20-17:00 h  |  ICM - Internationales Congress Center München Room 4a

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Subjects: 

Pharma & Diagnostics

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Medicine/public health

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Biotechnology & Life Sciences

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Analytic

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Digital Transformation / Smart Laboratory

Speaker: 

Dr. Nannette Brouwer 

(Waterland Hospital)

Type:
Lecture
Speech: English

Background

External Quality Assessment (EQA) programs for general chemistry tests have evolved from between laboratory comparison programs to trueness verification surveys. In the Netherlands, the implementation of such programs has reduced inter-laboratory variation for electrolytes, substrates and enzymes. This allows for universal and metrological traceable reference intervals, but these are still lacking. We have initiated a national endeavour named NUMBER to set up a sustainable system for the determination of standardized reference intervals in the Netherlands.

Methods

We used an evidence-based ‘big-data’ approach [1] to deduce reference intervals from primary care data using millions of test results from clinical laboratory databases. We selected 19 medical tests which are either traceable to SI or have JCTLM-listed reference materials and/or reference methods. Per laboratory, per test, outliers were excluded, data were transformed to a normal distribution (if necessary), and means and standard deviations (SDs) were calculated. Then, average means and SDs per test were calculated to generate pooled (mean±2SD) reference intervals. Flagging rates were calculated in 5 representative independent datasets to verify the plausibility of the calculated reference intervals. Results were discussed in expert meetings.

Results

Sixteen clinical laboratories across the country provided anonymous primary care test results (n=7.160.000). During three expert meetings, participants found consensus about calculated reference intervals for ALT, albumin, ALP, amylase, (anorganic) phosphate, AST, (total) bilirubin, calcium, chloride, GGT, iron, potassium, creatinine, LD, magnesium, sodium, total protein and urea and necessary partitioning in subcategories, based on sex, age, matrix and/or method. For creatinine kinase and uric acid further evaluation of the reference interval and the study population were considered necessary. For glucose, the working group advised to adopt the clinical decision limit. Flagging rates were all within the acceptable plus or minus 5% range.

Conclusions

Using a ‘big-data’ approach we were able to determine traceable reference intervals for 17 general chemistry tests. Nationwide implementation of these established reference intervals should improve unequivocal interpretation of test results leading to better diagnosis, treatment, risk stratification and follow up.


[1] Tate JR, et al. Clin Biochem Rev. 2014;35(4):213-35.

Speaker
Dr. Nannette Brouwer, Waterland Hospital Show Profile

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Apr
10
2018

Analytica Conference > DGKL / Room 4a > Reference Ranges – Data Mining at the Cusp of Clinical Translation

Indirect Approaches for the Estimation of Reference Intervals – an Overview

10. Apr 2018
15:00-15:40 h  |  ICM - Internationales Congress Center München Room 4a
Speaker: Dr. Thomas Streichert (Uniklinik Köln)

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Apr
10
2018

Analytica Conference > DGKL / Room 4a > Reference Ranges – Data Mining at the Cusp of Clinical Translation

PEDREF — Next-generation Reference Intervals for Laboratory Analytes in Pediatrics

10. Apr 2018
15:40-16:20 h  |  ICM - Internationales Congress Center München Room 4a
Speaker: Jakob Zierk (University Hospital Erlangen)

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