Page 332 - Glucose Monitoring Devices

Exploring the equipment: components and characteristics of the 670G 339

Similarly, an analysis of 139 adults followed at the Barbara Davis Center found that
the use of auto mode led to an increase in the percent time in target range [110].

Challenges that remain
For years, closed-loop studies have provided undeniable evidence that fixed basal
rates can never approximate physiologic needs due to the multitude of factors that
impact glucose control. Thus, it is not surprising that with each of these systems, over-
night performance is best when responses to food and activity are, often, no longer
required. Yet, the greatest issue that remains is postprandial glycemic control. As
current rapid-acting insulin analogs do not have the time action profile to adequately
address glycemic excursions following meals, a number of strategies are being
explored to overcome this issue. The use of bihormonal systems that would allow
for the use of glucagon to moderate a more aggressive insulin delivery algorithm
and more closely mimic normal physiology may be the key to a fully closed-loop
system [70]. The creation of soluble glucagon products has been critical to this
endeavor, and multiple companies have been working in this area [111]. Others
have explored the use of adjunctive agents, with early feasibility studies showing
that both pramlintide (amylin analog) and liraglutide (glucagon-like peptide 1 recep-
tor agonist), have lessened postprandial hyperglycemia [44,45,50]. Indeed, the feasi-
bility of using a fixed combination of rapid-acting insulin analog and pramlintide
infusions, administered through two separate infusion pumps, has been demonstrated.
Ongoing studies will assess the impact of such a fixed combination therapy [112].
Most importantly, the integration of hybrid closed-loop therapy into clinical
practice requires setting realistic expectations for both providers and patients and
educating providers on the differences between systems. To aide with understanding
how systems differ, the CARE acronym has been proposed [113]. More recently this
framework has been refined [114]. The goal is to understand five essential features of
each system:
Calculate: How does the algorithm CALCULATE insulin delivery? Which
components of insulin delivery are automated?
Adjust: How can the user ADJUST insulin delivery? Which parameters can be
adjusted to influence insulin delivery during automation? Which parameters are
fixed?
Revert: When should the user choose to REVERT to open-loop/no automation?
When will the system default to open-loop/no automation?
Educate: What are the key EDUCATION points for the advanced diabetes de-
vice? How does the user optimize time using the automated features? Where
can users and clinicians find additional education resources?
Sensor/Share: What are the relevant sensor characteristics for each device?
What are the system capabilities for remote monitoring and cloud-based data
sharing?
As more systems become commercially available, a standardized approach to
compare them will help both clinicians and patients understand the benefits that
each algorithm and the system components afford.