April 21, 2018
In the Milan conference, the preferred specification is the effect of assay error on patient outcomes. This seems reasonable enough but consider the following two cases.
Case 1, a glucose meter reads 350 mg/dL, truth is 50 mg/dL; the clinician administers insulin resulting in severe harm to the patient.
Case 2, a glucose meter reads 350 mg/dL, truth is 50 mg/dL; the clinician questions the result and repeats the test. The second test is 50 mg/dL; the clinician administers sugar resulting in no harm to the patient.
One must realize that lab tests by themselves cannot cause harm to patients; only clinicians can cause harm by making an incorrect medical decision based in part on a lab test. The lab test in cases 1 and 2 has the potential (a high potential) to result in patient harm. Case 2 could also be considered a near miss. From a performance vs. specification standpoint, both cases should be treated equally in spite of different patient outcomes.
Thus, the original Milan statement should really be the effect of assay error on potential patient outcomes.
April 20, 2018
My article “Interferences, a neglected error source for clinical assays” has been published. This article may be viewed using the following link https://rdcu.be/L6O2
April 20, 2018
As readers are probably aware, I am not a fan of measurement uncertainty (1). This is not because there is something wrong with measurement uncertainty but rather because it is proposed to model actual results of an assay (as in a method evaluation). As I described earlier, there is no reason to model actual results, since one can simply directly obtain the distribution of differences between an assay and reference.
Where a measurement uncertainty model is appropriate would be for an assay under development. In this case, there are no results, only a proposed collection of reagents at various concentrations and a proposed physical system to measure a response output.
- Krouwer JS A Critique of the GUM Method of Estimating and Reporting Uncertainty in Diagnostic Assays 2003 Clin Chem 49:1818-1821.
April 4, 2018
I had an occasion to read a paper in which I saw the term “LT” – I didn’t know what it meant. When I read a paper, I tend to skip around – the authors had correctly identified LT, on first use. So “LT” was an abbreviation for “long term”. This got me thinking, each of these is two syllables long. From the reader’s perspective, it is not laborious to read “long term” as opposed to “LT.”
So this leads me to a new rule.
Don’t create an abbreviation for something unless the abbreviation has fewer syllables than the term that being abbreviated.