Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy.

The state of type 1 diabetes (T1D) therapy in 2019 is a complex mixture of groundbreaking technological innovations and incompletely realized improvements to clinical care. In the past several years, we have seen the commercial approval of the first hybrid closed loop (HCL) artificial pancreas (AP) system, the first real-time factorycalibrated continuous glucose monitor (CGM), and predictive low glucose suspend systems, as well as approvals for the first interoperable continuous subcutaneous insulin infusion (CSII) pump and CGM configurations. Previous registry research has demonstrated that insulin pump users experience significantly lower hemoglobin A1c (HbA1c) values than multiple daily injection (MDI) users and that patients assessing their glycemic status with CGM experience lower HbA1c values than those using self-monitoring of blood glucose alone. Data from HCL AP technology have consistently shown significant glycemic improvements in both short-term and long-term studies. Accordingly, we should expect that the proliferation of these technologies would bring a notable improvement in glycemic control across all patient populations. The recent T1D exchange clinical registry analysis by Foster thus comes as a stark reminder that much work still needs to be done to improve clinical outcomes across the broader patient population. This report on >22,000 persons with T1D builds on the previous article by Miller et al. and documents that between the 2010–2012 and 2016–2018 periods, CSII pump use increased from 57% to 63% and CGM use increased from 7% to 30% with an exponential increase in 2013–2014. Despite this, the adjusted mean HbA1c increased from 7.8% in 2010–2012 to 8.4% in 2016–2018, with the largest increase seen among adolescents and young adults. Sub-analysis by device use best highlighted in a separate commentary by Rodbard shows that CGM users have HbA1c values 0.4% to 1.0% lower than nonusers and pump users have HbA1c values 0.2% to 0.8% lower than MDI users. Foster notes, in particular, that among CGM users, HbA1c levels were similar whether the person was using CSII or MDI for insulin delivery while Rodbard notes that differences in control between different racial/ethnic and income groups may be mediated by access to technology. In their discussions, both Foster and Rodbard were unable to conclude why glycemic control had worsened across the registry despite increased use of technology, although Rodbard speculated that it may be due to changes in the population regarding study sites, racial/ethnic groups, and income groups. The Foster analysis attempted to control for duration of diabetes and failed to identify changes as due to differences over time in racial/ethnic groups, income status, or other known confounding factors. Rodbard, however, notes that some of these effects may be nonlinear and thus not readily identified using multiple linear regression. In searching for other possible confounders, it is notable that BMI z-score was unchanged between time periods. Comparing the Miller 2010–2012 data with the Foster 2016– 2018 data, within CGM users all age groups had worse HbA1c in the latter period, with an even larger effect among CGM nonusers. Among pump users, HbA1c was roughly unchanged across age groups, whereas among MDI users, HbA1c increased mostly in the adolescents and younger adults. One potentially important finding from the Foster article that may be easily missed is that most participants never downloaded their device data (meters, CGMs, or CSII pumps) at home, and other than using the Dexcom Share feature, use of mobile applications was very uncommon. Over 71% of participants not using CGM reported never downloading their blood glucose meter outside of the doctor’s office, 51% of CGM users reported never downloading