In the latest issue of Clinical Chemistry, there are two articles (1-2) about how much glucose meter error is ok and an editorial (3) which discusses these papers. Once again, my work on this topic has been ignored (4-12). Ok, to be fair not all of my articles are directly relevant but the gist of my articles and particularly reference #10 is that if you use the wrong model, the outcome of a simulation is not relevant to the real world.
How are the authors’ models wrong?
In paper #1, the authors’ state: “The measurement error was assumed to be uncorrelated and normally distributed with zero mean…”
In paper #2, the authors state:” We ignored other analytical errors (such as nonlinear bias and drift) and user errors in this model.”
In both papers, the objective is to state a maximum glucose error that will be medically ok. But since the modeling omits errors that occur in the real world, the results and conclusions are unwarranted.
Ok, here’s a thought people – instead of simulations based on the wrong model, why not construct simulations based on actual glucose evaluations. An example of such study is: Brazg RL, Klaff LJ, Parkin CG. Performance variability of seven commonly used self-monitoring of blood glucose systems: clinical considerations for patients and providers. J Diabetes Sci Technol. 2013;7:144-152. Given sufficient method comparison data, one could construct an empirical distribution of differences and randomly sample from it.
And finally, I’m sick of seeing the Box quote (reference 3): “Essentially, all models are wrong, but some are useful.” Give it a rest – it doesn’t apply here.
- Malgorzata E. Wilinska and Roman Hovorka Glucose Control in the Intensive Care Unit by Use of Continuous Glucose Monitoring: What Level of Measurement Error Is Acceptable? Clinical Chemistry 2014; v. 60, p.1500-1509.
- Tom Van Herpe, Bart De Moor, Greet Van den Berghe, and Dieter Mesotten Modeling of Effect of Glucose Sensor Errors on Insulin Dosage and Glucose Bolus Computed by LOGIC-Insulin Clinical Chemistry 2014; v. 60, p.1510-1518.
- James C. Boyd and David E. Bruns Performance Requirements for Glucose Assays in Intensive Care Units Clinical Chemistry 2014; v. 60, p.1463-1465
- Jan S. Krouwer: Wrong thinking about glucose standards. Clin Chem, 2010;56:874-875.
- Jan S. Krouwer and George S. Cembrowski A review of standards and statistics used to describe blood glucose monitor performance. Journal of Diabetes Science and Technology, 2010;4:75-83.
- Jan S. Krouwer: Analysis of the Performance of the OneTouch SelectSimple Blood Glucose Monitoring System: Why Ease of Use Studies Need to Be Part of Accuracy Studies. Journal of Diabetes Science and Technology, 2011;5:610-611.
- Jan S. Krouwer: Evaluation of the Analytical Performance of the Coulometry-Based Optium Omega Blood Glucose Meter: What Do Such Evaluations Show? Journal of Diabetes Science and Technology, 2011;5:618-620.
- Jan S. Krouwer: Why specifications for allowable glucose meter errors should include 100% of the data. Clinical Chemistry and Laboratory Medicine, 2013;51:1543-1544.
- Jan S. Krouwer: The new glucose standard, POCT12-A3 misses the mark. Journal of Diabetes Science and Technology, 2013;7:1400-1402.
- Jan S. Krouwer: The danger of using total error models to compare glucose meter performance. Journal of Diabetes Science and Technology, 2014;8:419-421.
- Jan S. Krouwer and George S. Cembrowski: Acute Versus Chronic Injury in Error Grids. Journal of Diabetes Science and Technology, 2014;8:1057.
- Jan S. Krouwer and George S. Cembrowski. The chronic injury glucose error grid. A tool to reduce diabetes complications. Journal of Diabetes Science and Technology, in press (available online)