Diana Isaacs, PharmD, BCPS, BCACP, CDCES, BC-ADM, FADCES, FCCP, is an endocrine pharmacist and director of education and training in diabetes technology at the Cleveland Clinic. During her session, titled “Into the Unknown: Advanced Concepts in Continuous Glucose Monitoring,” Dr. Isaacs spoke on CGM metric data, how to facilitate shared decision-making, and how to detect potential user error.

Dr. Isaacs explained that there are at least 42 factors that can affect glucose. These factors fall into categories that include food, medication, activity (including exercise), biological, environmental, behavioral, and decision-making. She noted that, “This is where CGM is very helpful because you can see how these individual factors impact someone, the person can see it in real time, and then take action to increase their time in range and optimize their glucose levels.”

She noted that during the pharmacist’s patient care process in optimizing CGM use, it is helpful to have a baseline, such as knowing what type of technology the patient has currently and has tried previously, which medications the patient has tried and are currently taking, what type of insurance coverage the patient has, and the patient’s social history. The next step would be to access current glycemic targets. If the patient is a good candidate for CGM, the next steps would be developing a plan to start CGM and implementing that plan (including ordering the CGM, offering education/training) and following up on that plan (including interpreting the data from the CGM and addressing medication adjustments).

Focusing on glycemic outcomes, she noted that the optimal glucose time in range (TIR) is between 70 mg/dL and 180 mg/dL, with a daily goal of ≥70%, time below range (<70 mg/dL) of <4%, and time above range (>180 mg/dL) <25%. In regard to women who are pregnant and have preexisting type 2 or gestational diabetes, the ranges vary slightly to include a TIR of 63 mg/dL to 140 mg/dL.

When interpreting CGM data with the patient, Dr. Isaacs suggested utilizing the DATAA model. This model consists of five steps: Downloading the data, Assessing safety, Time in range, Areas to improve, and Action plan.

Dr. Isaacs explained that some challenges to CGM include lack of patient education and training, sensors falling off early, CGM miscalibration, lag time (refers to CGM sensor interstitial glucose readings lagging behind fingerstick glucose readings), and false readings. False-high readings can be caused by medications such as vitamin C, acetaminophen, and tetracycline, whereas false-low readings can be caused by high doses of salicylic acid. Additionally, false readings can be caused by compression lows (due to compression of interstitial fluid), dehydration, or a faulty sensor).

Dr. Isaacs concluded that, “CGM data is a valuable tool for medication management.” She added, “The DATAA model can help, especially when you have more time and go more in depth [with the patient], but also, don’t forget the guidelines [established by the American Diabetes Association]…as this is meant to be taken together.”

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