Understanding the Use of Sigma Metrics in Hemoglobin A1c Analysis

This study utilizes three unique data sets to demonstrate the state of the art of HbA1c analyzers in a range of settings and compares their performance against the international guidance set by the IFCC task force for HbA1c standardization. The data is used to demonstrate the effect of tightening of those criteria and the study serves as a guide to the practical implementation of the sigma metrics approach in a range of clinical settings

HbA1c method performance: The great success story of global standardization

Diagnosing and monitoring the treatment of people with diabetes is a global issue and uses considerable resources in laboratories and clinics worldwide. Hemoglobin A1c (HbA1c) has been the mainstay of monitoring glycemic control in people with diabetes for many years and more recently it has been advocated as a diagnostic tool for type 2 diabetes mellitus (T2DM). Good analytical performance is key to the successful use of any laboratory test, but is critical when using the test to diagnose disease, especially when the potential number of diagnoses could exceed 500 million people. Very small variations in bias or increased imprecision could lead to either a missed diagnosis or overdiagnosis of the disease and given the scale of the global disease burden, this could mean erroneous categorization of potentially millions of people. Fundamental to good performance of diagnostic testing is standardization, with defined reference materials and measurement procedures. In this review, we discuss the historical steps to first harmonize HbA1c testing, followed by the global standardization efforts and provide an update on the current situation and future goals for HbA1c testing

Evaluation of Four HbA1c Point-of-Care Devices Using International Quality Targets: Are They Fit for the Purpose?

BACKGROUND: Point-of-care (POC) testing is becoming increasingly valuable in health care delivery, and it is important that the devices used meet the same quality criteria as main laboratory analyzers. While external quality assessment (EQA) provides a great tool for assessing quality, many POC devices are not enrolled in these schemes and standard laboratory evaluations are needed to assess performance. METHODS: The Clinical and Laboratory Standards Institute (CLSI) protocols EP-5 and EP-9 were applied to investigate imprecision, accuracy and bias. We assessed bias using the mean of 4 certified secondary reference measurement procedures (SRMPs). RESULTS: The Afinion2™ and the Quo-Lab had CVs of ≤1.7 and ≤2.4% respectively in IFCC SI units (≤1.2 and ≤1.7% NGSP) and a bias ≤2 mmol/mol (≤0.2% NGSP) at 48 and 75 mmol/mol (6.5 and 9.0% NGSP). Sigma for the Afinion2 was 5.8 and for the Quo-Lab 4.0. Both methods passed the NGSP criteria with 2 instruments when compared with 4 individual SRMPs. The HbA1c 501 had a CV of 3.4% and 2.7% in IFCC SI units (2.1% and 1.7% NGSP) and a bias ≤2.4 mmol/mol (≤0.2% NGSP) and passed the NGSP criteria with 2 instruments compared with 4 individual SRMPs except for instrument 2 compared with the Tosoh G8. Sigma was 2.1. The A1Care had a sigma of 1.4 and failed all criteria mainly due to a high CV (6.2% and 4.1% in IFCC SI units [4.1% and 2.9% NGSP] at 48 and 75 mmol/mol [6.5 and 9.0% NGSP]). CONCLUSIONS: The analytical performance was excellent for the Afinion2 and the Quo-Lab, acceptable for the HbA1c 501 and unacceptable for the A1Care according to different used criteria, demonstrating that whilst performance is improving there are still areas for considerable improvement

Are hemoglobin A1c point-of-care analyzers fit for purpose? The story continues

Objectives: Point-of-care (POC) analyzers are playing an increasingly important role in diabetes management but it is essential that we know the performance of these analyzers in order to make appropriate clinical decisions. Whilst there is a growing body of evidence around the more well-known analyzers, there are many ‘new kids on the block’ with new features, such as displaying the presence of potential Hb-variants, which do not yet have a proven track record. Methods: The study is a comprehensive analytical and usability study of six POC analyzers for HbA1c using Clinical and Laboratory Standards Institute (CLSI) protocols, international quality targets and certified International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) and National Glycohemoglobin Standardization Program (NGSP) Secondary Reference Measurement Procedures (SRMP). The study includes precision (EP-5 and EP-15), trueness (EP-9), linearity (EP-6), sample commutability (fresh, frozen and lyophilized), interference of Hb-variants (fresh and frozen samples). Results: Only two of the six analyzers performed to acceptable levels over the range of performance criteria. Hb-variant interference, imprecision or variability between lot numbers are still poor in four of the analyzers. Conclusions: This unique and comprehensive study shows that out of six POC analyzers studied only two (The Lab 001 and Cobas B101) met international quality criteria (IFCC and NGSP), two (A1Care and Innovastar) were borderline and two (QuikReadgo and Allegro) were unacceptable. It is essential that the scientific and clinical community are equipped with this knowledge in order to make sound decisions on the use of these analyzers

One in Five Laboratories Using Various Hemoglobin A(1c) Methods Do Not Meet the Criteria for Optimal Diabetes Care Management

Background: We assessed the reference change value (RCV) of currently available hemoglobin A(1c) (HbA(1c)) laboratory assays, which is defined as the critical difference between two consecutive HbA(1c) measurements representing a significant change in health status. Methods: We examined the individual laboratory coefficients of variation (CVs) in the Dutch/Belgian quality scheme based on 24 lyophilized samples and calculated the RCV per laboratory (n-220) and per assay method. In addition, two pooled whole blood samples were sent to the participating laboratories. The individual laboratory results were compared to the assigned value +/- an allowable total error (TEa) of 6%. Results: At HbA(1c) values of 41.0 mmol/mol (5.9%-Diabetes Control and Complications Trial [DCCT]) and 61.8 mmol/mol (7.8%-DCCT), 99% and 98%, respectively, of the laboratories reported a value within a TEa limit of 6%. The analytical CV of the HbA(1c) method used in 78% of the laboratories is Conclusions: The analytical performance of the majority of laboratory HbA(1c) methods is within the clinical requirements. However, based on the calculated RCV, 21.8% of the laboratories using different HbA(1c) methods are not able to distinguish an HbA(1c) result of 59 mmol/mol (7.5%-DCCT) from a previous HbA(1c) result of 53 mmol/mol (7.0%-DCCT). It can be presumed that differences in HbA(1c) results of 5 mmol/mol (0.5%-DCCT) do influence treatment decisions

Investigation of 2 Models to Set and Evaluate Quality Targets for Hb A1c: Biological Variation and Sigma-Metrics

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

Discordance in glycemic categories and regression to normality at baseline in 10,000 people in a Type 2 diabetes prevention trial

The world diabetes population quadrupled between 1980 and 2014 to 422 million and the enormous impact of Type 2 diabetes is recognised by the recent creation of national Type 2 diabetes prevention programmes. There is uncertainty about how to correctly risk stratify people for entry into prevention programmes, how combinations of multiple ‘at high risk’ glycemic categories predict outcome, and how the large recently defined ‘at risk’ population based on an elevated glycosylated haemoglobin (HbA1c) should be managed. We identified all 141,973 people at highest risk of diabetes in our population, and screened 10,000 of these with paired fasting plasma glucose and HbA1c for randomisation into a very large Type 2 diabetes prevention trial. Baseline discordance rate between highest risk categories was 45.6 %, and 21.3 - 37.0 % of highest risk glycaemic categories regressed to normality between paired baseline measurements (median 40 days apart). Accurate risk stratification using both fasting plasma glucose and HbA1c data, the use of paired baseline data, and awareness of diagnostic imprecision at diagnostic thresholds would avoid substantial overestimation of the true risk of Type 2 diabetes and the potential benefits (or otherwise) of intervention, in high risk subjects entering prevention trials and programmes