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Investigation of 2 models to set and evaluate quality targets for hb a1c: biological variation and sigma-metrics
- Source :
- Clinical chemistry. 61(5)
- Publication Year :
- 2014
-
Abstract
- BACKGROUND A major objective of the IFCC Task Force on Implementation of HbA1c Standardization is to develop a model to define quality targets for glycated hemoglobin (Hb A1c). METHODS Two generic models, biological variation and sigma-metrics, are investigated. We selected variables in the models for Hb A1c and used data of external quality assurance/proficiency testing programs to evaluate the suitability of the models to set and evaluate quality targets within and between laboratories. RESULTS In the biological variation model, 48% of individual laboratories and none of the 26 instrument groups met the minimum performance criterion. In the sigma-metrics model, with a total allowable error (TAE) set at 5 mmol/mol (0.46% NGSP), 77% of the individual laboratories and 12 of 26 instrument groups met the 2σ criterion. CONCLUSIONS The biological variation and sigma-metrics models were demonstrated to be suitable for setting and evaluating quality targets within and between laboratories. The sigma-metrics model is more flexible, as both the TAE and the risk of failure can be adjusted to the situation—for example, requirements related to diagnosis/monitoring or international authorities. With the aim of reaching (inter)national consensus on advice regarding quality targets for Hb A1c, the Task Force suggests the sigma-metrics model as the model of choice, with default values of 5 mmol/mol (0.46%) for TAE and risk levels of 2σ and 4σ for routine laboratories and laboratories performing clinical trials, respectively. These goals should serve as a starting point for discussion with international stakeholders in the field of diabetes.
- Subjects :
- Glycated Hemoglobin
Standardization
Computer science
business.industry
Task force
media_common.quotation_subject
Biochemistry (medical)
Clinical Biochemistry
Sigma Metrics
Models, Biological
Reliability engineering
Set (abstract data type)
Biological variation
Proficiency testing
Humans
Quality (business)
business
Laboratories
Quality assurance
media_common
Subjects
Details
- ISSN :
- 15308561
- Volume :
- 61
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- Clinical chemistry
- Accession number :
- edsair.doi.dedup.....58f09f936340915166f4db0c3e30536d