MEASUREMENT UNCERTAINTY


Intermediate imprecision: challenges and benefits using a simpler method


In this fourth article in his series on measurement uncertainty, Stephen MacDonald moves on to look at some of the lesser discussed points that need to be considered when calculating imprecision and its contribution to combined uncertainty.


The top-down method has simplified measurement uncertainty (MU) estimation. The laborious and statistically demanding bottom-up method is no longer the only approach. Studies have applied external quality assessment (EQA) data only to MU assessment.1


EQA provides different,


very valuable insights, but generally it is not recommended for derivation of imprecision performance due to the smaller number of samples that are routinely available. Imprecision quantified by internal


Assumption


1 Commutability of the internal quality control material


2 QC at a clinically relevant level


3 The measurement of QC is Meaning IQC must behave in a similar manner to patient samples in order


for any variation we measure in IQC samples to represent that expected in patient samples


Variation at the point where importance is highest – clinical


decision limits – needs to be closely represented by the levels of QC used to be transferrable to risk of patient harm


Pre analytical processing, handling and analytical measurement


the same as the measurement must be comparable to both systems otherwise variability of QC of patient samples


and patient data are not transferable


Table 1. Minimum requirement of internal quality control material to support use of the top-down approach for measurement uncertainty estimation. Deviation from these requirements raised doubts as to whether the variability seen in the QC represents the variability that can be expected in patient results.


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quality control (IQC) minimises the risk of fewer uncertainty contributors being detected. IQC captures long- term imprecision, and the uncertainty contributors that cause it. However, the coefficient of variation (CV) of the IQC alone is not MU.2


Imprecision


may contribute up to 50% of our overall MU budget, as seen in the previous article in this series (April 2024),3


For IQC to be a robust


and the convenience and accessibility of IQC data presents unseen challenges for the top-down method.4


mechanism for determining MU, there are some assumptions (Table 1).


Assumptions for quantifying long term imprecision from IQC


n Do IQC samples actually represent patient samples?


To be valid for MU estimation, the variability of the IQC (measured by the standard deviation and/or the CV) must reflect the variability of individual patient samples. It is then possible to project imprecision from IQC data onto patient samples. In the absence of this relationship, IQC data will not reflect the quality of the result for its intended clinical use. Transferability of IQC data and patient data variability may need to be confirmed during assay verification.


n Period of time of data collection All data collected for imprecision must be measured while the analytical process is stable and under control. This is in the context of systems constantly changing. Corrective and preventative maintenance, calibrations and lot changes all influence that stability and can impact on imprecision estimates. Long-term imprecision (uRW


) requires


data to be collected for a sufficiently long period of time to account for such variation. The amount of time appears to have been arbitrarily set to half of the period of time required for the Earth to go around the Sun – the mystical six months. In actuality, the important metric of concern is the amount of data collected over a period of time to accurately represent all variations in the system. Risk-based design of quality control frequency shows that will vary based on the underlying performance of the method.


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