Robert Busch, MD, and Diana Isaacs, PharmD, BCPS, BCACP, BC-ADM, CDCES, discuss metrics to evaluate diabetes control, including time in range and coefficient of variation.
Dhiren Patel, PharmD, CDE, BC-ADM, BCACP:When we think about metrics associated with diabetes care, we tend to think about A1c [HbA1c glycated hemoglobin]. I know we’re all out there telling folks to be mindful of comorbid conditions, and we shouldn’t live and die by the A1c. When we think about CGMs [continuous glucose monitors], we have other terminology that gets introduced. How have you talked to your peers, primary care providers you work with, in explaining to them time in range, coefficient of variation? Sometimes everyone associates technology with being more complicated but it’s not necessarily more complicated; we’re just now dealing with different metrics and different ways to look at it.
Robert Busch, MD: We talk about variability and how the patient with an A1c could be having a good A1c. You could have 300s average with 50s [mg/dL], so your A1c looks good; you have variability like that or smooth control. Obviously, the smooth control is a lot better for the patient and probably has less vascular disease by doing that versus significant peaks and valleys. The other thing is that time in target, you become like a seer with the patient to predict what their A1c is when you look at their time in target. As we know, if your time in target of 70 to 180 [mg/dL] is 70% or more, your A1c is going to be below 7%. Hence, it’s very nice to predict even before you make the phone call for their A1c, what their A1c is going to be.
It’s almost like when you put someone on a statin and you know what the statin efficacy is, you can predict what their LDL [low-density lipoprotein] will be based on what you know. Time in target, most people can understand that, and they can understand variability. Some people use the analogy of if you’re on a plane, would you like the plane to go smooth, or would you like to go up and down? You can still get to your destination the same way.
Dhiren Patel, PharmD, CDE, BC-ADM, BCACP:Yes, that’s a great analogy that I might tuck away for later. Dr Isaacs, what are your thoughts on this? I tell folks you read a couple AGP [ambulatory glucose profile] reports, and you get them under your belt. They’re very visual, color coordinated, and then everyone has their own nuances about where they would like to go and view and drill down into. However, overall, it’s very easy to use. How have you talked to your peers regarding not just the AGP reports but time in range, the coefficient of variation?
Diana Isaacs, PharmD, BCPS, BCACP, BC-ADM, CDCES: I think there are so many limitations to A1c, and the big one is that it doesn’t tell us anything about glucose variability. Thus, that’s where time spent in that target range as well as the other CGM key metrics come in, where there’s one for glucose variability, that coefficient of variation. I think people are busy. We don’t have time to be sifting through 20 pages of data.
Dhiren Patel, PharmD, CDE, BC-ADM, BCACP:That’s right.
Diana Isaacs, PharmD, BCPS, BCACP, BC-ADM, CDCES: You could if you really wanted to, but there’s no need for that. Thus, we have the AGP report, which is 1 page and it has everything, and this has been standardized by the international consensus on time in range. At the top of the report, you have your CGM key metrics like time in range, the percentage of time the CGM was worn, as well as that coefficient of variation. Then, in the middle of that report you have that AGP itself, which is that nice visualization where you can see, are there certain times of the day where the person may be spiking higher or experiencing hypoglycemia? Then, at the bottom of that report has those last 14 days, so you can see those day-by-day variations. Consequently, that has everything and yes, I agree, once you’ve looked at a few of them, you become very comfortable and can do it very quickly and efficiently.
Transcript edited for clarity.