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A plea for intra-laboratory reference limits. Part 1. General considerations and concepts for determination
Rainer Haeckel1, Werner Wosniok, Farhad Arzideh
1Diagnostisches Zentrum Wagner Stibbe, Göttingen, Germany. rainer.haeckel@t-online.de
Establishing reliable laboratory reference limits is crucial for accurate diagnostics. While external limits are common, internal, population-based limits derived from large datasets show promise for improved quality management.
Area of Science:
- Clinical Laboratory Science
- Medical Diagnostics
- Quality Management Systems
Background:
- Accurate laboratory results depend on reliable reference limits for interpretation.
- Current quality management emphasizes the need for laboratories to verify and identify appropriate reference limits.
- Many laboratories neglect internal limit determination, relying on external sources with transference issues.
Purpose of the Study:
- To review methods for determining laboratory reference limits.
- To evaluate the diagnostic efficiency of different reference limit concepts.
- To discuss the challenges and future directions in establishing reliable reference limits.
Main Methods:
- Review of prospective and retrospective methods for reference limit determination.
- Evaluation of unimodal and polymodal concepts, with and without disease prevalence.
- Assessment of statistical approaches and their diagnostic sensitivity, specificity, and efficiency.
Main Results:
- The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) unimodal prospective approach is the current gold standard for harmonization.
- Harmonization is limited to analytes with achievable SI units and traceability.
- Newer retrospective concepts using laboratory information system data show promise for internal limit derivation.
Conclusions:
- Trueness-based standardization with IFCC reference limits offers the best basis for decision-making but faces traceability challenges.
- Significant research is needed for most laboratory medicine measurands.
- Retrospective methods using large datasets are a promising avenue for developing efficient, internal, population-based reference limits.
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