Recently, an IFCC committee published recommended goals for hemoglobin A1c (HbA1c). Their recommended sigma metrics establish a pass-fail criterion and their discussion revolves around the risk of passing or failing the allowable total error using control samples.
An error grid has zones and zones outside of the “A” zone are associated with increasing harm to patients (e.g., likelihood of an incorrect medical decision made by a clinician based on test error). Typically, patient samples are used to populate an error grid and the likelihood of observing errors is more faithful to the real world.
Each type of goal has its advantages and limitations.
The IFCC pass-fail goal is useful for proficiency surveys to compare different assays, especially over large datasets. Thus, one can spot poorly performing assays. But the limitations are:
- Because control samples are evaluated, certain errors such as patient interferences are not possible to be detected.
- Even if patient samples were used, the Westgard model would not detect interferences or other sources of random bias.
- The pass-fail criterion has its own limitations. An assay that just passes and one that just fails should have similar performance – yet one is acceptable and the other isn’t.
An error grid is more suitable to understanding how an assay will perform in a hospital laboratory, assuming that the error grid is populated using patient samples (requires a reference assay for comparison for each sample). The advantage is that more error sources are sampled and the harm associated with the results is shown (assumes that the zone limits are correct). The limitations are:
- Populating an error grid is impractical across sites so conclusions are limited to the site that conducted the experiment.
So the IFCC goals provide a high level view of an HbA1c assay whereas the error grid provides the detailed view.