American Diabetes Association 80th Scientific Sessions

June 12-16, 2020; Virtual; Diabetes Technology Highlights

Table of Contents 

Diabetes Technology Highlights

1. MiniMed 780G Pivotal Trial: +1.4 Hour/Day Gain on TIR and 0.5% A1c Improvement on MiniMed 780G vs. Baseline (SAP or MiniMed 670G); 95% Time in Closed Loop Through 3-Month Study

Dr. Bruce Bode (Emory University) presented extremely positive results from the ~3.5-month, single-arm (n=157, ages 14-75) study compared Medtronic’s “Advanced Hybrid Closed Loop” (AHCL), a.k.a., MiniMed 780G system, with sensor-augmented pump therapy or MiniMed 670G. During the 14-day run-in phase, about one-third of patients were using MiniMed 670G with Auto Mode (automatic basal modulation with set point of 120 mg/dl). Compared to run-in, AHCL delivered improvements on every glycemic outcome. Time in Range during the three-month study period improved from 69% to ~75% (+1.4 hours/day); most of this improvement came from reductions in hyperglycemia – percent time >180 mg/dl fell from ~28% to ~23% (-1.1 hours/day). Adolescents (ages 14-21; n=39) saw a particularly large improvement in Time in Range, from 62% to 73% (+2.5 hours/day). For the overall group, AHCL delivered an A1c reduction of 0.5% (baseline: 7.5%) and mean sensor glucose was reduced from 153 mg/dl to 148 mg/dl. Trial participants spent an impressive 95% of time in closed loop, a significant jump from the 87% time in closed loop reported with MiniMed 670G’s pivotal trial. Participants averaged just 1.3 Auto Mode exits per week in the 780G pivotal study.

As we’ve seen in many closed loop studies now, outcomes saw the greatest improvement overnight (midnight – 6 AM). During the overnight period, Time in Range increased from 71% to 82% using AHCL and mean glucose fell from 150 mg/dl to 140 mg/dl. Most of the improvement came from reductions in hyperglycemia: time >180 mg/dl fell from 26% to 16% in the overnight period and time >250 mg/dl dropped from 5.1% to 2.6%.

Medtronic’s press announcement shares that a study questionnaire found 96% of participants saying the system was “easy to use.” It also notes that system requests for fingersticks fell by 46% compared to 670G. Ease of use was a major focus of MiniMed 780G and participants in the pivotal averaged ~1.3 closed loop exits/week, a huge improvement from the nearly once per day Auto Mode exits seen with 670G.

The study reported no serious adverse events (SAEs), such as DKA, severe hypoglycemia, serious adverse device effects, or unanticipated device effects. There were three non-device related SAEs, including one incident of severe hypoglycemia during run-in, one appendicitis during the study period, and one sepsis during the study.

Like the 670G pivotal, Medtronic elected for a single-arm study, comparing the same group of users before and after AHCL. This contrasts with Tandem’s Control-IQ pivotal study, which randomized users to participants into separate SAP and closed loop groups. Halfway through this trial (45 days), Medtronic also included a cross-over with half of participants changing their target glucose set points from 100 mg/dl to 120 mg/dl, and the other half changing from 120 mg/dl to 100 mg/dl.

There were no updates on regulatory timelines for MiniMed 780G, though Medtronic’s accompanying press announcement confirms that the algorithm will be part of a class III Premarket Approval (PMA) application, i.e., a class II, interoperable automated glycemic controller (“iController”). Medtronic announced CE-Marking for its AHCL MiniMed 780G system yesterday – that report contains a detailed breakdown of MiniMed 780G’s product features.

Pictures were not allowed, but we’ve summarized the data below.

 

Overall Group (n=157)

Adolescents (ages 14-21; n=39)

Adults (ages 22-75; n=118)

Outcome

Baseline

Study

Baseline

Study

Baseline

Study

Time in Range

69%

75%

62%

73%

71%

75%

A1c

7.5%

7%

7.6%

7.1%

7.5%

7%

Time >180 mg/dl

28%

23%

34%

25%

26%

23%

Time >250 mg/dl

6.2%

4.6%

9.1%

5.6%

5.3%

4.3%

Time <70 mg/dl

3.3%

2.3%

3.3%

2.4%

3.4%

2.3%

Time <54 mg/dl

0.8%

0.5%

0.9%

0.6%

0.8%

0.5%

Mean CGM

153 mg/dl

148 mg/dl

162 mg/dl

150 mg/dl

151 mg/dl

147 mg/dl

Time in closed loop

33%

95%

37%

94%

32%

95%

Note: Time in closed loop during baseline is from some participants using the MiniMed 670G system during run-in.

  • Time in Range improved from 69% to 75% (p<0.001), an improvement of 1.4 hours/day. During the overnight period (midnight – 6 AM), Time in Range jumped from 71% to 82% (p<0.001), while Time in Range saw a more modest improvement from 68% to 72% (p<0.001) during the daytime period.

    • At baseline, 54% of participants were meeting the consensus target for >70% Time in Range. With AHCL, 73% of participants were able to hit the >70% Time in Range goal. Notably, average TIR was 79% with a 100 mg/dL set point and two-hour AIT (active insulin time). This effect was particularly pronounced in adolescents: at baseline, 18% of adolescents had Time in Range >70%; during the study period, more than half (59%) of adolescents had Time in Range >70%.

  • Mean sensor glucose fell from 153 mg/dl to 148 mg/dl with AHCL (p<0.001). When using AHCL and a set point of 100 mg/dl, mean glucose fell even further to 144 mg/dl. The difference was greater in the adolescent subgroup, which saw mean glucose improve from 162 mg/dl at baseline to 150 mg/dl with AHCL and 147 mg/dl with AHCL and a 100 mg/dl set point. During the overnight period, the overall group saw a mean glucose of 135 mg/dl when using AHCL and a set point of 100 mg/dl.

Overall Group (n=157)

Baseline

Study

Study w/ 100 mg/dl set point

Mean glucose

153 mg/dl

148 mg/dl

144 mg/dl

Daytime mean glucose

155 mg/dl

151 mg/dl

148 mg/dl

Overnight mean glucose

150 mg/dl

140 mg/dl

135 mg/dl

  • Time in hypoglycemia was low at baseline and slightly reduced with AHCL. Overall, time <70 mg/dl decreased from 3.3% to 2.3% (-14 min/day) and time <54 mg/dl decreased from 0.8% to 0.5% (-4 min/day). Of note, the results shared are all well below consensus goals for time in hypoglycemia, even without automation. As seen in the table below, using the lower (default) set point of 100 mg/dl increases time <70 mg/dl, though it’s unclear whether that difference is statistically significant.

  • A1c was significantly improved from 7.5% to 7% (p<0.001). At baseline, about one-third (34%) of participants were meeting the A1c goal of <7%; during the study period, the number of participants meeting this goal was almost doubled, to 61%.

  • MiniMed 780G includes the ability to adjust active insulin time between two and eight hours. With shorter active insulin time settings, Time in Range trended higher without increasing time in hypoglycemia. The most common active insulin time settings were between 2-3 hours.

Time in Range

Active Insulin Time

2 hours

>2-3 hours

>3-4 hours

>4 hours

100 mg/dl set point

79%

n=29

76%

n=76

74%

n=65

71%

n=4

120 mg/dl set point

75%

n=26

74%

n=76

72%

n=74

68%

n=2

 

Time <70 mg/dl

Active Insulin Time

2 hours

>2-3 hours

>3-4 hours

>4 hours

100 mg/dl set point

2.6%

n=29

2.9%

n=76

2.8%

n=65

4.8%

n=4

120 mg/dl set point

1.9%

n=26

2.0%

n=76

1.7%

n=74

1.4%

n=2

  • The 16-center study was limited to participants with baseline A1c <10% and >6 months of pump therapy (CGM use was not a requirement). The study enrolled 39 adolescents, ages 14-21, and 118 adults, ages 22-75 years. We believe ~155 participants completed the entire trial, though this was not officially given, nor were reasons for drop-outs. Dr. Bode reported that just two weeks into the study period of the trial, clinic centers and patients began asking Medtronic about keeping their AHCL systems, as they felt like they “can’t live without [the system].”

Baseline Characteristics

Overall (n=157)

Adolescents (n=39)

Adults (n=118)

Age

38.3

16.2

45.6

Female (%)

86 (55%)

23 (59%)

63 (53%)

A1c

7.5%

7.6%

7.5%

Weight, kg

80.1

68.8

83.9

BMI, kg/m2

27.5

24.2

28.6

Diabetes duration, years

22.6

9.2

27

  • The slides below taken from ATTD 2019 nicely summarize key CGM outcomes from the MiniMed 670G pivotal trials. The NIDDK-sponsored FLAIR trial (see highlight below) provides a more direct, randomized, cross-over comparison hbetween the AHCL and 670G systems.

2. First Ever Head-to-Head Comparison of Closed Loop Systems: MiniMed 780G Delivers Improved Glycemic Outcomes vs. 670G in Teens/Young Adults; 5.7 Auto Mode Exits per Week with 670G vs. 1.7/Week for 780G

On behalf of the cleverly-named FLAIR (Fuzzy Logic Automated Insulin Regulation) study group, Dr. Rich Bergenstal (International Diabetes Center) presented results from the first-ever study of an AID system with a commercially approved AID comparator. The six-month trial showed superiority of Medtronic’s Advanced Hybrid Closed Loop (AHCL) over MiniMed 670G and baseline therapy on nearly every glycemic and usability outcome. The international, seven-center study (four sites in the US, two in Europe, and one in Israel) was sponsored by Jaeb and sponsored by NIDDK. At the beginning of the presentation, Dr. Bergenstal listed out three specific focuses for the FLAIR study: (i) daytime, post-prandial hyperglycemia; (ii) glycemic control in individuals 14-29 years old (adolescents/young adults); and (iii) broad entry criteria. The study randomized 113 participants with type 1 diabetes, ages 14-29, into MiniMed 670G and AHCL (MiniMed 780G) groups with a crossover halfway through (at ~three months). Two of the 113 study participants dropped out due to personal reasons.

  • Dr. Bergenstal called the study’s participants “probably the broadest study population done to date” in AID. One-fifth (20%) of participants were on MDI at baseline and two-fifths (38%) were CGM-naïve. Additionally, a quarter of participants had baseline A1cs between 8.6%-11% and just 19% had A1cs between 7%-7.4%. At time of randomization, mean A1c was 7.9%, though at time of screening, Dr. Bergenstal noted that mean A1c was higher, at “8.2% or so.”

  • AHCL was superior to MiniMed 670G on both primary endpoints: time >180 mg/dl during the daytime (6 AM – midnight) was improved to 34% with AHCL vs. 37% with 670G (-43 min/day; p<0.001) without increasing time <54 mg/dl (p<0.001 for non-inferiority). The 24-hour glucose profiles show the biggest benefit with AHCL during the overnight and early morning periods, though mean glucose through the entire 24-hour period is lower for AHCL. MiniMed 670G also delivered significant improvements over baseline during the overnight period, with less benefit during the daytime hours.

  • A1c at baseline was 7.9%, compared to 7.6% with 670G and 7.4% with 780G. Time in Range at baseline was 57%, compared to 63% with 670G and 67% with AHCL. The entirety of the Time in Range benefit came from reductions in hyperglycemia, though time in hypoglycemia was not significantly increased. At baseline, just 12% of participants were meeting consensus goals of >70% Time in Range. This number rose to 22% of participants with 670G, increasing further to 32% with AHCL. Glucose variability remained steady from baseline to 670G to AHCL.

  •  

    Baseline

    MiniMed 670G

    AHCL (MiniMed 780G)

    A1c

    7.9%

    7.6%

    7.4%

    Time in Range

    57%

    63%

    67%

    Time <54 mg/dl

    0.5%

    0.5%

    0.5%

    Time <70 mg/dl

    2.3%

    2.1%

    2.1%

    Time >180 mg/dl

    41%

    34%

    31%

    Time >250 mg/dl

    13%

    10%

    9%

    %CV

    36%

    37%

    37%

  • AHCL was superior to MiniMed 670G across age groups and baseline A1cs. In the teenage group (14-20 years), Time in Range was 59% at baseline, compared to 64% on 670G and 67% with AHCL. The young adult group (21-29 years) had almost identical results, with Time in Range improving from 57% to 63% to 67% for baseline, 670G, and 780G, respectively. Stratifying by baseline A1c, those with higher A1cs saw a slightly smaller improvement in Time in Range. For baseline A1c ≤8.5%, Time in Range rose from 58% to 65% to 68% for baseline, 670G, and 780G, respectively; for baseline ≥8.6%, Time in Range rose from 46% to 52% to 54%.

  • In the 14 participants who were on MDI+SMBG, Time in Range rose from 45% at baseline to 65% with AHCL. For these participants, initiating on MiniMed 670G was enough to bring Time in Range 670G. On these results, Dr. Bergenstal urged clinicians and researchers not to exclude participants who are not experienced with technology. Across all groups by insulin delivery method (MDI vs. CSII) and CGM-use at enrollment, AHCL delivered superior Time in Range outcomes (some differences may not have been statistically significant).

  • Time in Range by tech use at enrollment

    Baseline

    MiniMed 670G

    AHCL (MiniMed 780G)

    SMBG + MDI (n=14)

    45%

    63%

    65%

    SMBG + CSII (n=29)

    58%

    62%

    66%

    CGM + MDI (n=9)

    53%

    58%

    61%

    CGM + CSII (n=46)

    59%

    65%

    69%

    MiniMed 670G (n=15)

    60%

    64%

    66%

  • With AHCL, mean total daily dose of insulin increased to 55 units (0.75 U/kg), compared to 50 units (0.67 U/kg) on 670G. Remember, this was a crossover study, so the participants are the same. This increase in insulin use was driven by AHCL’s automatic correction boluses; for the 670G periods, insulin use was evenly split between bolus and basal insulin (49% vs. 51%), whereas bolus insulin made up 64% of the total daily dose with AHCL. Of that 64%, 36% (i.e., 13 units) came from automatic correction boluses. Dr. Bergenstal speculated that the higher reliance on automatic corrections comes from the study population being more likely to forget meal boluses, demonstrating the 780G’s greater “forgiveness” for missed or miscalculated meal boluses.

  • The number of closed loop exits per week dropped from 5.7 with 670G to 1.7 with AHCL. The 1.7 exits per week translates to approximately one exit every four days and is actually slightly higher than results from the US pivotal (see above) of 1.3 exits/week, though this could probably be attributed to the “more difficult” study population in FLAIR. The reduced number of closed loop exits translated to much higher time in closed loop, at 86% vs. 75%. With AHCL, CGM use was also 86%, meaning when CGM data was available, the system was in closed loop nearly the entire time.

  • Neither MiniMed 670G or AHCL arms reported incidents of DKA, though there was one severe hypo event in the AHCL arm requiring “mild assistance.” The 670G arm recorded two “other” serious adverse events, one relating to suicidal tendencies and one due to a ruptured appendix. The MiniMed 670G arm reported three events of hyperglycemia or ketosis related to the pump, while the 780G arm reported two of these events.

3. Horizon Pre-Pivotal Delivers +1.7 Hour/Day Time in Range in Adults (n=18) and +3.3 Hours/Day in Children (n=18); 97% (!) Time in Closed Loop and Very High System Usability Scores

  • In front a Stanford Cardinal-colored background, the eminent Dr. Bruce Buckingham (Stanford) presented very impressive study results from the first outpatient study of Insulet’s Omnipod Horizon hybrid closed loop system. The single-arm study compared 14-days of standard therapy with 14-days of hybrid closed loop therapy for 18 adults (ages 14-70) and 18 children (ages 6-13). In the closed loop phase, participants spent 3 days with a glucose target of 130 mg/dl, 3 days at 140 mg/dl, 3 days at 150 mg/dl, followed by 5 days in which participants were free to choose set points ranging from 110 mg/dl to 150 mg/dl. After the study, participants were invited to continue on closed loop therapy and participate in the pivotal trial for Horizon, which all participants elected to do.

  • In adults, Time in Range was improved from 66% to 73% (+1.7 hours/day) from the standard therapy to hybrid closed loop with user-specific glucose set point. Time in Range was improved on both ends, with marked reductions in both time in hyperglycemia and hypoglycemia. Impressively, time below 70 mg/dl dropped from 2.6% to 0.3% (-33 min/day) and time below 54 mg/dl dropped from 0.6% to 0% (-9 min/day). After a longer period on Horizon of 4-9 weeks, it appears that time <70 mg/dl bounced back slightly to 0.9%, but still that translates to just ~13 minutes/day – wow! Mean glucose and A1c remained about the same on both standard therapy and closed loop, but glucose variability was crushed, with coefficient of variation dropping from 36% to just 29%. In the 24-hour glucose profile below, the blue line and blue-shaded regions represent the median and IQRs for glucose values on closed loop and are, very clearly, as Dr. Rich Bergenstal would say, “flat, narrow, and in-range.”

  • Time in Range for pediatric trial participants was even more dramatic, jumping from 51% on standard therapy to 65% (+3.3 hours/day) on the five closed loop days with user-chosen set points. For the 11 participants who chose the lowest set point of 110 mg/dl, Time in Range was a striking 71%. Similar to the adults, pediatric patients saw very low levels of hypoglycemia, but also massive reductions in hyperglycemia. Time >180 mg/dl fell from 47% to 34% with closed loop (-3 hours/day) and just 27% for closed loop with a set point of 110 mg/dl. With those reductions in hyperglycemia, mean glucose decreased from 185 mg/dl to 167 mg/dl to 155 mg/dl and GMI fell from 7.7% to 7.3% to 7% from standard therapy to closed loop to closed loop with a set point of 110 mg/dl. Importantly, these improvements were maintained for longer term use of Horizon, as well.

  • As with most AID systems, there was a massive benefit in overnight glycemic control. In 6-<14 year old participants, Time in Range overnight (midnight – 6 AM) rose from 56% to 72% with closed loop. For closed loop with a set point of 110 mg/dl, overnight Time in Range was 77%. In the table below, the 0% time <70 mg/dl when using closed loop also really stands out. Lastly, overnight mean glucose improved significantly, as did glucose variability.

 

  • Throughout the presentation, Dr. Buckingham highlighted Horizon’s focus on reducing hypoglycemia, and the results (see above) are stunning. Dr. Buckingham shared that with Horizon taking away the fear of lows, a lot of the kids in the study were able to go on their first sleepovers. Beyond what’s reflected in the glycemic outcomes in the tables above, Dr. Buckingham focused on Horizon’s ability to remove mental and psychological burden from having diabetes or having a child with diabetes.

  • Both children and adult users spent an incredible 97% of time in closed loop and scored very high on system usability. As the Horizon algorithm is built directly onto the Omnipod pump, which can communicate directly with the Dexcom G6 sensor, users don’t need to worry about staying within range of a handset or receiver. Additionally, both Omnipod and G6 are waterproof, allowing users to stay in closed loop when showering. For parents of children ages 6-<12 years, Horizon scored a 90 (out of 100) on system usability. For teenagers, the system received a 93. During Q&A, Dr. Tim Bailey (Advanced Metabolic Care and Research) noted that Apple’s iPhone scores ~79 on the same scale. Dr. Buckingham even suggested Horizon might have a higher usability when launched, as Insulet plans on launching with smartphone control, whereas study participants had to use a separate, locked-down Samsung phone for controlling their Omnipods. User friendliness and simplicity have been a focus on the design of Horizon from the very beginning and the pre-pivotal data suggest that focus has paid off greatly.

 

  • There were not serious adverse events reported in the study, though there was one incident of prolonged hyperglycemia when one adult participant’s cannula was dislodged.

  • When launched, Insulet’s hybrid closed loop system will likely be marketed under the name “Omnipod 5.” For a long time, we (and others) have referred to the system as Omnipod Horizon, though Horizon may actually be the term for the algorithm component of the system. Dr. Buckingham’s presentation was titled, “Omnipod 5 Automated Glucose Control System, Powered by Horizon” and during the ADA session’s live-chat feature, we saw Insulet Medical Director Dr. Trang Ly write: “Horizon (now called Omnipod 5) is expected to be avail first half 2021 in US [sic].”

  • Just last week, Insulet announced resuming of the pivotal trial for Omnipod Horizon after a ~three month pause. We’d estimate the trial (n=240) will wrap up within the next 3-4 months, in line with plans to launch in the “first half of 2021.”

4. MiniMed 780G New Zealand Data: Time in Range +3.3 Hours/Day with Advanced Hybrid Closed Loop and 100 mg/dl Set Point vs. Run-in; 96% Time in Closed Loop; CE-Marked Announced Yesterday

In the third (and final) MiniMed 780G presentation from the day, Dr. Martin de Bock (University of Otago) presented data from a randomized crossover study of AHCL vs. the predictive low-glucose suspend MiniMed 640G system. Results of this study (n=60) were used for the CE-submission for MiniMed 780G; just yesterday, Medtronic announced CE-Marking for the system. The study enrolled 60 participants (20 participants 7-15 years old). The sensor-augmented pump (SAP) run-in phase lasted 2-4 weeks before participants were randomized to MiniMed 640G or AHCL for four weeks, followed by two weeks of washout with SAP, and finally, four weeks of crossover. At enrollment, 44% of participants were on SMBG, while >6 months on pump therapy was required for inclusion. Time in Range increased from 59% during run-in to 73% for AHCL with a 100 mg/dl set point (+3.3 hours/day); accordingly, time >180 mg/dl fell from 38% to 25% (-3.1 hours/day). Results with AHCL and a set point of 120 mg/dl were slightly less impressive, but still improved over run-in. During Q&A, moderator Dr. Tim Bailey commented that the results (along with those of the other two MiniMed 780G talks) really showed that MiniMed 780G is a device that is “designed to be used” with a set point of 100 mg/dl. Dr. de Bock also added that the improvements in the study were seen even with “plenty of petrol in the tank” (i.e., relatively conservative settings): the study used a longer active insulin time of three hours, kept insulin:carb ratios from baseline, and the higher set-point in pediatric participants.

 

Run-in

MiniMed 640G

AHCL with 120 mg/dl set point

AHCL with 100 mg/dl set point

Mean glucose

168 mg/dl

171 mg/dl

162 mg/dl

149 mg/dl

Time in Range

59%

58%

65%

73%

Daytime Time in Range

59%

57%

61%

70%

Nighttime Time in Range

60%

59%

72%

77%

Time > 180 mg/dl

38%

40%

33%

25%

Time <70 mg/dl

3.1%

2.5%

2.2%

2.1%

Time <54 mg/dl

0.6%

0.6%

0.5%

0.4%

  • Time in closed loop was remarkably high, at 96%. Patients experienced a mean of 1.2 Auto Mode exits/week and the number of alarms and alerts was reduced from ~13/day with MiniMed 640G to 8/day with AHCL. Additionally, Diabetes Treatment Satisfaction Questionnaires showed improvements in treatment satisfaction for AHCL vs. MiniMed 640G for adolescent and adult users.

  • The study saw one episode of mild DKA, due to a combination of a site occlusion and viral illness. There were no incidents of severe hypoglycemia. There were five “possible or probable device related adverse events”; four were skin reactions related to infusion sites and one was a skin infection.

  • Lastly, but certainly not least, Dr. de Bock also shared a few powerful quotes and impressions from the trial:

    • “…created a situation for our family that was as close to not having diabetes as we have been in the last decade. It wasn’t what it made us do that made the difference, it was what we no longer had to do.”

    • “We didn’t have to worry, we didn’t have to be fearful at night or have that thought when we opened her bedroom door in the morning that she might not be conscious.”

    • “She could think about horses and friends and Keeping up with the Kardashians like any other tweeny with a junk TV habit.”

    • “I forgot I had diabetes today.”

5. Dr. Richard Berganstal and Emma Wilmot Face off on Whether Technology Alone Can Prevent Severe Hypoglycemia

In a fascinating debate, Drs. Richard Bergenstal (International Diabetes Center) and Emma Wilmot (University Hospitals of Derby and Burton) hit on whether or not technology alone can prevent hypoglycemia and its complications in patients. Dr. Bergenstal argued in favor of technology alone while Dr. Wilmot took the opposite stance for the purposes of the debate. Overall, the discussion touched on a number of valuable topics, highlighting the distinctions between A1c and Time in Range, the value of structured education in diabetes therapy, and questions of access and reimbursement. See below for a table summary of the key points from both sides.

Dr. Bergenstal’s arguments FOR “Technology Alone Can Prevent Severe Hypoglycemia”

  • As always, Dr. Bergenstal was so strong in explaining the power of diabetes metrics, and the power of standardization of these metrics, which has made such a phenomenal difference to patients and HCPs lucky enough to experience CGM and AID. It’s so clear at a population level how much better diabetes management can become as more patients able to get access. The narratives on CGM and AID themselves keep getting better with so much focus on reducing burden and improving management through better use of data, which itself keeps getting better. Dr. Bergenstal’s ability to help frame and standardize metrics is so valuable for the field and far beyond the debate, this was really valuable thinking about time in range(s) broadly speaking. For example, Dr Bergenstal riffed really  interestingly on what he characterized as the ripple effect of hypoglycemia, which was new to us:

    • This is a situation where some people (who have not done well historically) have (understandable and well-intended) attempts to avoid hypoglycemia;

    • Then, this can lead these same folks who are not already using CGM or AID to continue to not use it;

    • Then, they or their HCPs say, “just loosen glycemic targets, as we don’t want more hypo”;

    • Then, they deal with the ripple of higher A1c, less TIR and higher risk of complications. 

    • The whole point was that, of course, the technology and data management capabilities have improved so much that people can have both improved TIR and reduced TBR (time below range, or hypoglcyemia) – all we have to do, of course, is look at the AID symposium from earlier in the day.

  • So, back to the point of the debate, can technology alone prevent severe hypo? From our view, the answer is clearly yes, although of course, it is not only about technology – other components can obviously help and access is critical, a point that both Dr. Bergenstal and Dr. Wilmot have long histories of working in and around. Some other points made by Dr. Bergenstal included:

    • Flash glucose monitoring is sufficient to improve glycemic awareness. For example, a European analysis of over 60 million FreeStyle Libre scans found a direct correlation with the number of scans per day and reduction in hours spent in hyperglycemia. For example, in the UK, as the number of scans increased from 0 to 50/day, the hours per day spent above 180 mg/dl decreased on average from ~11 hrs to ~6 hrs. Regardless of external circumstances, simple technology has the power to reduce time spent in hypoglycemia and increase Time in Range.

    • Dr. Bergenstal also showed one-day ambulatory glucose profiles from three patients enrolled in the HypoDE study who all had the same A1c of 6.7%. However, the time spent in hypoglycemia significantly varied by patient, with data suggesting high-quality technology could reduce time spent in hypoglycemia. For example, one patient on MDI therapy spent 9% of time (130 min/day) in hypoglycemia while a patient using an insulin pump with CGM spent just 6% of time below range (86 min/day). Finally, the patient using a hybrid closed loop system spent just 1% of time in hypoglycemia (15 min/day).  

  • Additionally, related to the ripple effect, Dr. Bergenstal emphasized that the benefits of diabetes technology are not limited to just the type 1 population. We were so glad to hear this as it needs to be said again and again and again to reinforce the importance of what CGM can do in terms of identifying foundational changes that may need to be made on the therapeutic front, to start. Dr. Bergenstal presented data from the REPLACE trial, showcasing blood glucose management among the type 2 population (n=224) on flash glucose monitoring relative to those on SMBG. After three months, patients on flash CGM saw hypoglycemia rates drop 43% compared to those on SMBG. These findings illustrate that regardless of patient subgroup, patients who receive advanced technology such as CGM naturally improve. Presumably, of course, data was used to show changes where different therapy or different food (the two biggest levers in our view) could change things.

  • Dr. Bergenstal finished on an emphatic note, emphasizing the value of a very recent piece, Continuous Glucose Monitoring as a Matter of Justice” that was just published in late May in HEC Forum (Health Care Ethics Committee Forum). He also pointed listeners to a paper by the highly respected Dr. Ed Gregg, formerly of the CDC, and others, who wrote a concerning paper in JAMA in April 2019, “Resurgence in Diabetes-Related Complications.” Generally, Dr. Bergenstal made a big argument for CGM as a means of reaching greater health equity, arguing that improved glycemic control resulting from technology such as CGM has multiple valuable societal downstream benefits, including reduced stigmatization, improved autonomy, stronger interpersonal relationships, and higher productivity. While the reductions seen in hypoglycemia alone, simply through technology onboarding, in addition to the potential social and systemic benefits stemming from reduced CV and kidney complications are a very strong case for increased usage in diabetes treatment and management.

Dr. Wilmot’s arguments AGAINST “Technology Alone Can Prevent Severe Hypoglycemia”

  • Overall, Dr. Wilmot and Dr. Bergenstal had a lot in common in their arguments, big picture, although Dr. Wilmot did assert that no technology has been able to demonstrate a significant and sustained improvement in hypoglycemia awareness, a strong risk factor for severe hypoglycemia. Dr. Wilmot presented data from 135 CGM users in Utah, 33% of which had impaired awareness of hypoglycemia. Despite CGM usage, 25% of patients had at least one episode of severe hypoglycemia within the last six to twelve months. However, she emphasized, if one looks at patients who receive structured education, outcomes change. She cited an older study from 2005 that studied 9,683 type 1 patients in Germany and found a 50% reduction in severe hypoglycemia after 20 hours of structured inpatient training on insulin therapy. While technology certainly confers benefits, she built her argument around the importance of enhancing technology with educational and behavioral health support that yields significantly improved outcomes. We imagine most of her peers would agree with this – certainly learning how to review data isn’t something that is completely intuitive, by any stretch!

  • Dr. Wilmot further reminded attendees that access to technology is highly limited because of gaps in national policy and reimbursement. Based on 2018 data from national registries, pump use is highly fragmented by age group and region in the UK. For example, only ~37%-38% of young diabetes patients (<18 years old) in Wales, Scotland, and England use an insulin pump compared to 70% in Denmark. With such large fractions of society disconnected, it is unreasonable, she said, to expect technology alone to fill gaps in care.

  • Regardless of the technology, Dr. Wilmot added, there are always subgroups of patients who struggle with onboarding and use. She presented data from Pediatric Diabetes (n=92 youth) which found that 30% discontinued using the MiniMed 670G system. Skin reactions, data overload, and unwanted alarms are a few of the many reasons, she asserted, why certain patient populations, regardless of the device, do not bear the benefits of technology. A separate study on adults published in Diabetes Care (n=84) found that after 12 months, 1/3rd of individuals had also stopped using the 670G mostly because of sensor issues (62%) and challenges in securing supplies (12%). From our view, these were extremely important points and the “prize” is to continue to work on the drawbacks of technology in order to improve it. That’s how product improvement progresses, along with the very big help of so many clinicians and researchers like Dr. Wilmot and Dr. Bergenstal and their teams.

  • Even with technology and education, some patients may still not benefit. Dr. Wilmot presented a case study of one patient, a type 1 woman since 1999, who started facing impaired awareness in 2009. Despite receiving structured education in 2011 and an insulin pump in 2012, she still experienced three severe hypoglycemic episodes per week. The case became so difficult that she was banned from a local McDonalds because staff mistook her hypoglycemia for inebriated behavior, and her husband had to stop working to observe her. However, after undergoing islet cell transplantation, she began to see improvements in time in range, reducing her severe hypoglycemia by ~90%. Furthermore, in extreme cases, additional intervention beyond technology and education are required to support diabetes patients.

  • Both doctors gave very valuable learning in this debate and we highly recommend visiting this part of the website while this is up – we felt ultimately that the two experts were in agreement with each other and also expressed valuable perspectives. Ultimately, we’d love to see the field get closer to addressing barriers presented by Dr. Wilmot since there are many opportunities for people with diabetes to benefit, given the right environment.

6. Dana Lewis Presents Qualitative User Experiences with Closed Loop Systems, Tandem Control-IQ, Medtronic MiniMed 670G, and CamDiab CamAPS FX

Outside of ADA, at DiabetesMine’s D-Data Exchange, DIY leader Ms. Dana Lewis presented qualitative findings on the real-world experiences of seven patients using a mixture of automated insulin delivery systems including Tandem’s Control-IQ (n=4), Medtronic’s MiniMed 670G (n=2), and CamDiab’s recently launched CamAPS FX (n=1) – see this valuable data published here. The findings were collected using semi-structured phone interviews with these users, who ranged in age and technology experience, on these ease of training, Time in Range improvements, hypoglycemia reductions, quality of life enhancements, sleep quality boosts, ease of troubleshooting, and how expectations were met. While findings slightly varied, several important themes emerged: (i) Time in Range mostly increased or stayed the same; (ii) some systems decreased hypoglycemia while others increased it; (iii) post-prandial hyperglycemia was a common issue Control-IQ and MiniMed 670G users observed; (iv) new CGM users had a more noticeable learning curve and experienced alarm fatigue; and (v) MiniMed 670G users were more likely to cite connection issues and CGM calibration difficulties, both of which impacted sleep. The table below provides a case-by-case summary highlighting both the benefits and difficulties that each user witnessed.

Patient

Closed Loop System

Positive Experiences

Challenges

Parent of Child with Type 1 Diabetes (4+ year on pump/CGM)

Had prior experience with tech

Control-IQ

Improved Time in Range by 30% (baseline not provided)

No Change in Hypoglycemia (3% of time <70 mg/dl)

Minimal troubleshooting

None

Adult Male with Type 1 Diabetes

Previous Experience with DIY APS system and “longtime” Pump and CGM User

Control-IQ

Pleased with the ease of use/convenience

Quality of life enhancements driven by minimal troubleshooting and improved sleep

Time in range decrease by 5%-10%

Post-meal highs (actually had to stop eating breakfast)

More manual intervention

 

Female Adult with LADA Previously on Basal Insulin

Control-IQ

Enhanced sleep quality and offered “peace of mind”

Initial onboarding difficulties – turned off the system after two weeks before attempting again

Aggressiveness of settings

Lack of information on how much basal insulin has been dosed each hour

Lack of flexibility on blood glucose level driven by “target”

Adult Female with Type 1 Diabetes

Two years of experience on CGM and pump

Control-IQ

Time in Range improved to 70% overnight

Does not correct as often as desired

Adult Male with Type 1

1 year on CGM before switching to CGM and pump

MiniMed 670G

Ability to adjust according to exercise with AutoMode and activity target

“Easy” learning curve

No A1c or Time in Range Changes

Post-Meal highs especially if missed/delayed bolus or underestimated carbs

CGM calibration issues 3x/week

Adult Male Previously Considered Type 2 but now Type 1

5 years on pump therapy

MiniMed 670G

Improved sleep

Alarm fatigue

Woke up “every other night” due to calibration issues

Adulty Type 1 Male for 5 Years on Pump/CGM

CamAPS FX

Easy learning curve

Found it easier to use when on a “regular routine”

No Time in Range difference

Time spent in hypoglycemia doubled relative to DIY therapy

Lack of visibility to insulin on board

 

  • Ultimately, these findings lend themselves to important lessons for diabetes companies currently developing closed loop systems: (i) supporting onboarding for users based on prior diabetes tech experience; (ii) including insulin-on-board displays along with temporary/adjustable targets; (iii) enabling hypoglycemia protection even if corrections for hyperglycemia are disabled; (iv) improving post-prandial glucose measurements; and (v) adding support for remote bolusing and monitoring

 

7. Rural Diabetes Telehealth Intervention Delivers 1.4% A1c Reduction After Six Months among Veterans with High Baseline A1cs

Ms. Elizabeth Kobe, a third-year medical student at Duke University, presented results on Advanced Comprehensive Diabetes Care (ACDC), a six-month novel telehealth intervention providing diabetes care to rural veterans. Despite the promise of telehealth, which has gained prominence since the COVID-19 pandemic (see the diaTribe Foundation's Musings 2020 and Insulet webinar) intensive interventions have rarely been implemented in standard diabetes care due to a lack of trained staff, availability of equipment, limited integration with electronic health records, and poor reimbursement. To address this implementation gap, the Department of Veterans Affairs (VA) has invested in a nationwide network of Home Telehealth (HT) nurses, currently used for telemonitoring of diabetes and other chronic conditions. ACDC leveraged the VA’s HT network and EHR infrastructure to deliver intensive telehealth care to rural veterans with diabetes. Overall, the intervention involved four steps: (i) nurses held 30-minute phone calls every two weeks for six months where they would review glucose data, reconcile medication usage, and analyze medication adherence; (ii) patients received self-management support through eight modules; (iii) a clinical report was compiled; and (iv) a pharmacy manager would review the data and recommend care changes to the HT. In the initial cohort, mean A1c (n=50; baseline: 10.5%) among patients randomized to ACDC (vs. usual care) in the program saw a reduction of 1.0% at six months.

  • Following the pilot, ACDC began its formal implementation phase in 2017, expanding to seven total VA sites across North Carolina, Eastern Colorado, New Mexico, and Montana. Across patients who have enrolled in the program 2017-2019 (n=125; 5 sites), participants have seen a mean A1c of reduction of 1.4% (baseline 9.3%) after six months – notably, these reductions were sustained up to 18 months (-1.1%). Participant engagement has also been satisfactory, with an average of 8-10 of 12 scheduled intervention calls completed per patient at each site. These findings continue to demonstrate how telehealth can improve access to care, promote diabetes self-management, and improve health literacy.

  • Veterans are particularly at-risk for diabetes and we’re excited to see such promising results for this underserved population. About a quarter of veterans in the US have diabetes a rate more than double the national average, and 1.5+ million veterans with type 2 diabetes currently receive diabetes care from the VA. In 2017, the VA estimated annual spend on diabetes to be $1.5 billion – this makes us imagine that the 1.5 million estimate is incorrect (and too high), since otherwise, the VA would be investing only $1,000/person with diabetes, which is far too low to have a chance of being correct (we’ll explore that estimate and come back in time for the full report!).

 

8. Swedish GOLD Study Extension (n=107) Shows Sustained Effects with Dexcom G4 vs. SMBG Out to 2.5 Years: 0.35% A1c Reduction, +2.1 Hours/Day TIR, Satisfaction and Hypo Confidence Improvements

University of Gothenburg’s Dr. Marcus Lind presented strong results from the 12-month, SILVER extension phase of GOLD, comparing SMBG vs. CGM. GOLD was a 16-month Swedish trial, that enrolled 161 type 1s on MDI; after a 1.5-month run-in, participants were randomized to either SMBG or CGM (Dexcom G4) for six months, followed by four months of wash-out, and six-months of crossover. GOLD showed strong advantages for CGM vs. SMBG on every glycemic outcome, including a 0.4% A1c improvement and ~30 minutes per day less time <70 mg/dl (4.8% vs. 2.8%) – and this was with the G4, which was less accurate and less easy to use. Of the 141 GOLD trial completers, 107 were enrolled in the 12-month SILVER extension phase. In SILVER, all participants received CGM and had “brief consultations with a diabetes nurse” every three months. Compared to SMBG users at the end of the GOLD trial, switching to CGM produced a 0.35% A1c reduction (8.3% vs. 8.0%; p<0.001), 2.1 more hours/day time in range (51% vs. 43%; p<0.001 – this is a big deal), and significant reductions in hypoglycemia (falling from 7.5% time total to 3.7% total). As shown in the line graph below, A1c remained relatively steady through the entire 12-month extension, demonstrating the benefits of CGM were sustained beyond the initial 1.5-year trial. That said, getting to 53% time in zone means that 47% of their time was not in zone – it turns out most of it was above 180 but below 250, and although a 25% reduction in time over 250 is impressive, still being at 17% or four hours above 250 per day, is not nearly good enough – and, a bit shocking for Sweden. So, again – relatively speaking, yes it’s better, but in absolute terms, it’s not nearly good enough. The fact that this is consistent with an 8% (or 8.3%) A1c is also shocking since those are generally assumed to be “about the same.”

 

SMBG GOLD

CGM SILVER

Difference

A1c

8.3%

8.0%

-0.35%

Time <54 mg/dl

2.1%

0.6%

-1.4%

Time <72 mg/dl

5.4%

2.9%

-2.3%

Time in Range

43%

51%

+9%

Time >180 mg/dl

52%

46%

-5%

Time >250 mg/dl

24%

17%

-6%

Standard deviation

4.2 mg/dl

3.6 mg/dl

-0.6 mg/dl

  • Compared to run-in, CGM showed even stronger improvements compared to SMBG. The end of the SILVER extension study represents ~2.5 years from run-in, providing compelling evidence for the long-term sustainability of CGM benefits. Compared to run-in, CGM improved A1c by 0.5% (baseline: 8.5%) and boosted Time in Range by 2.7 hours/day (!), from 40% to 51%. Again – 51% is not close to being okay, this is nearly 12 hours time out of range. Hypoglycemia, while being significantly lowered, from 2.1% to 0.6% (-22 min/day; p<0.001) for time <54 mg/dl and 5.5% to 2.9% (-36 min/day; p<0.001) for time <70 mg/dl, was already pretty low, leading us to focus on the higher percentages of time that people are way off base.

  • As noted, the 51% Time in Range, even with CGM, is strikingly low. At ATTD 2020, we saw a post-hoc analysis of the GOLD study demonstrating how difficult it is to achieve the new consensus CGM metrics guidelines. In the study, just 3 out of the 137 participants analyzed met both A1c <7% and Time in Range >70% goals when using CGM, and only one participant met both goals on SMBG. The graph below really underscores the difficulty – the green box was added by us and highlights the areas where both A1c and time below 70 mg/dl targets are met.

  • The SILVER study showed sustained benefits with Dexcom G4 vs. SMBG on nearly all behavioral and satisfaction outcomes. Diabetes Treatment Satisfaction Questionnaire scores jumped from ~25 to 31 with CGM. Well-being, measured by WHO-5’s Well-Being Index, rose 11% from 60 to 66. The Swedish-Hypoglycemia Fear Scale showed significant improvements around worry and a reduction in hypoglycemia avoidance behaviors, though this second reduction was not statistically significant (p=0.07). Hypoglycemia confidence scores rose from 3.2 on SMBG to 3.45 with CGM (p<0.001).

  • Through the 12-month extension, there were 5 incidents (between 4 patients) of severe hypoglycemia and no DKA. None of these events were deemed to be related to CGM-use. In the original GOLD trial, there was a non-significant trend towards fewer severe hypo events with CGM: there were five events of severe hypoglycemia during SMBG (0.19 per 1,000 patient-years) vs. one event with CGM (0.04 per 1,000 patient-years).

9. SENCE RCT (CGM in Young T1s) 12-Month Extension: Sustained CGM Utilization and Hypo Reduction, but Still Over Half of Day >180 mg/dl

Yale’s Dr. Michelle Van Name presented 12-month follow-up data from the SENCE RCT of CGM in children ages 2-7, showing sustained CGM use and hypoglycemia reduction but no/minimal improvement in Time in Range or hyperglycemia. Positive primary outcomes from the six-month study were presented at ADA 2019. As a reminder, the 14-site trial randomized participants to three groups: CGM + a family behavioral intervention (FBI; n=50) vs. CGM with standard education (CGM-only; n=44) vs. SMBG (n=49). FBI consisted of five visits (weeks 1, 3, 6 13, 19) with additional training on using and living with CGM, using CGM away from home, CGM burnout, and more. The CGM groups used the Dexcom G5 non-adjunctively, with access to Dexcom Share. Participants were not on CGM at enrollment, so baseline metrics were derived from blinded CGM. After the six-month trial portion completed, 131 participants opted to continue using CGM for an additional six months, which is a pretty positive 92%—we’re not sure if G6 was employed during this follow-up phase. For the two CGM groups, the extension period consisted of CGM with no continued educational intervention; members of the SMBG control group who opted to continue were crossed over to receive CGM + FBI.

  • Positive finding #1: CGM usage remained high in both CGM + FBI and CGM-only groups at 12 months. 86% and 95% of children in the respective groups continued to wear CGM ≥6 days per week through the extension phase; compare these figures to 93% and 90% at six months. Overall, we think this is a pretty easy goal with the latest technology in particular, but particularly so with G4 and G5.

  • Positive finding #2: Hypoglycemia reductions observed at six months in both CGM groups were sustained at 12 months. Notably, in both CGM groups, time <70 mg/dl was approximately cut in half at 12 months (CGM+FBI: 69->36 mins/day; CGM-only: 81->39 mins/day). Similarly, percent time <54 mg/dl at 12 months as reduced to ~one-third of baseline levels (CGM+FBI: 30->10 mins/day; CGM-only: 35->12 mins/day – this was very positive, at over two hours per week). In accordance with these findings, the mean hypoglycemic event rate per week was also cut in half in both CGM groups at 12 months (CGM+FBI: 2.3->1.2 events/week; CGM-only: 2.8->1.2 events/week).

    • Those in the SMBG group who crossed over to CGM + FBI for the 6-12 month extension phase saw similar hypoglycemia benefit. From CGM initiation at six months to follow-up at 12 months, notably, although mean time <70 mg/dl fell by 54 minutes per day (6.2% to 2.4%), there was no change in Time in Range.

  • Cautionary reminder: Even after a year of CGM use, young children with type 1 diabetes in a rigorous study treated at top clinical centers are still spending over half of their days with glucose levels >180 mg/dl and just ~40% in-range. Because CGM use did not significantly reduce time >180 mg/dl in either CGM group, time in range hovered at ~40% across all reported measurement periods. This resonates with how hard it is to achieve the outcomes that everyone wants. While it is encouraging that there was no compensatory increase in hyperglycemia as a result of reduced hypoglycemia exposure (we guess), given all we know (and are learning) about the risks of hyperglycemia exposure and the legacy effect, we feel very strongly that future interventions must target the high end. As Drs. Lutz Heinemann and David Klonoff pointed out late last year in the wake of T1D Exchange Registry data showing drastically increased CGM adoption but also increased A1c, “Investment into CGM usage does not result in an automatic improvement in glucose control.” They go on to make the case for better, more comprehensive training programs and comparative studies of such programs. From our view, we hope people do not get too hung up on A1c – we imagine less hypoglcyemia overall and particularly less time in hypo, is increasingly prompting higher A1cs  - higher A1cs that could be higher-quality A1c.

  • Dr. Van Name highlighted the relative diversity of the study population (for a diabetes technology study), as ~1/3 were minority, ~1/3 were on a pump, and the mean A1c was 8.2%. More than one-third of study participants did not have private health insurance, and the majority of parents did not have a bachelor’s degree or higher.

10. Pre-/Post- Study Shows FreeStyle Libre is Associated with 60% Reduction in Acute Diabetes Events, 33% Reduction in All-Cause Hospitalization

In a retrospective pre-/post- study leveraging IBM Watson Health’s MarketScan database, presenter Dr. Rich Bergenstal (IDC) and co. found striking reductions in acute diabetes events (-60%) and all-cause hospitalizations (-33%) among 2,463 adults with type 2 diabetes on short- or rapid-acting insulin who began using FreeStyle Libre between November 2017 - September 2018. Though not without limitations (e.g., retrospective; no A1c data available; exclusion of Medicare fee-for-service and Medicaid populations; no access to socioeconomic or educational data), Dr. Bergenstal suggested this study offers “compelling support for the use of flash CGM to both improve clinical outcomes and potentially reduce costs in this patient population.” Notably, in the live chat off to the side of him presenting, this presentation incited optimism for CGM reimbursement as well as calls for researchers to take the next logical step for payers by calculating changes in expenditure. While true calculations of overall cost can be complex, Dr. Barry Ginsberg proposed simplifying by reporting cost-savings from reduced hospital days (~$8,000 each), and Dr. Robert Gabbay suggested extrapolating savings from A1c reductions. In the same chat, Dr. Irl Hirsch, a co-author, alluded to a poster (875-P) from the same group showing that FreeStyle Libre use is associated with reduced acute diabetes events regardless of prior blood test strip usage. He proclaimed, “the SMBG requirement [for Medicare CGM reimbursement] makes no sense.” Dr. Aaron Neinstein aptly responded, “Agree... the SMBG requirement is, in the parlance, bats$&$.”

  • Methods: MarketScan is a research database containing commercial and Medicare supplemental insurance claims for more than 30 million individuals in the US. Because it tracks down to the level of the individual and claims for health care facility utilization and pharmacy, the researchers were able to identify codes for inpatient/emergency outpatient utilization in the six months prior to and after the first use of FreeStyle Libre. Dr. Bergenstal commented that the database appears to hold “a reliable collection of data” due to the study participant characteristics: mean age 54 years, 90% with lipid disorder, 88% with hypertension, 60% with obesity, 48% with neuropathy, etc. 

  • As seen in the Kaplan-Meier plot below, there was clear (!) separation and 60% overall reduction (HR=0.40, 95% CI: 0.31-0.51, p<0.001) in acute diabetes events in the six months after vs. six months prior to first FreeStyle Libre use. Acute diabetes events are defined as a combination of inpatient and outpatient emergency events including hyperglycemia, hypoglycemia, DKA, hypoglycemic coma, and hyperosmolarity. Overall number of events fell from 221 (0.18 per patient-year) in the six months pre-purchase to 84 (0.07 per patient-year) in the six months post-purchase. In sub-analyses, this reduction was significant for both genders and across age (stratified by <50 and 50+ years). We’d love to better understand how it cuts across race and socioeconomic lines.


  • In the secondary outcome, there was a 33% reduction in all-cause hospitalization in the six months post-purchase vs. six months pre-purchase (HR=0.67, 95% CI: 0.58-0.77, p<0.001). Overall hospitalizations fell from 516 (0.42 per patient-year) to 331 (0.28 per patient-year). Dr. Bergenstal pointed out that, as expected, the number of endocrinology-related hospitalizations fell by 59% (6.4 per 100 patient-years to 2.6 per 100 patient-years). Also intriguing, he noted, against the backdrop of a resurgence in diabetes complications in the US, were reductions in hospitalizations for infectious disease, respiratory, renal, and hepatobiliary/pancreatic issues.

11. Use of Dexcom G6 Delivers Two-Times Greater A1c Improvements vs. Connected BGM Across All Baseline A1c Groups in Onduo’s Virtual Diabetes Clinic

Onduo’s Head of Clinical Affairs Dr. Ron Dixon presented data from the company’s Virtual Diabetes Clinic, demonstrating ~2x greater A1c reductions in participants who used CGM (Dexcom G6) vs. those using a connected BGM. The retrospective analysis looked at 612 Onduo participants from February 2018 – April 2019, comparing 213 participants who were initiated on Dexcom G6 with 399 who were not. Participants were well matched at baseline with a mean age of 53 and 54 years, mean BMIs of 35 and 36 kg/m2, and baseline A1cs of 7.8% and 7.7% for the CGM and non-CGM groups, respectively.

  • For the highest baseline A1c group (>9%), the CGM group saw a mean A1c reduction of 3.3% (!), compared to a 1.7% reduction (still massive) for the non-CGM group after six months (p<0.001 for between-group difference). Though less pronounced, the CGM group saw greater A1c reductions vs. non-CGM in every other baseline A1c grouping. For baseline A1c of 8%-9%, A1c reductions were 1.3% and 0.6% for CGM and non-CGM groups, respectively (p=0.004). For baseline A1c of 7%-8%, these reductions were 0.4% and 0.1% (p=0.02). Lastly, for those with baseline A1cs <7%, A1c rose by 0.1% in the CGM group and 0.2% in the non-CGM group.

  • As seen in the graphs below, using CGM as part of Onduo’s virtual clinic had a “flattening out” effect on participants’ A1cs across the initial A1c cohorts. While initial A1c in the groups ranged from <7% all the way to ~11%, after six months, mean A1c across all groups was between ~6%-8%. This reminded us of Dr. Rich Bergenstal’s (International Diabetes Center) presentation yesterday in which participants on MDI+SMBG were brought onto Medtronic’s Advanced Hybrid Closed Loop and saw their time in range shoot up from 45% to 65% (+4.8 hours/day). So often, clinicians and researchers seem to exclude participants who are CGM- and pump-naïve, and those with poor baseline A1cs, when in fact, they may have the most to gain from using technology like CGM.

At an Abbott-sponsored symposium, Dr. Irl Hirsch (University of Washington) shared his quick math estimating that in the US alone, the healthcare system could see an astounding $4.6 billion in DKA-related cost savings by using CGM in all people with diabetes.  Note that the following calculations come with Dr. Hirsch’s disclosure that he is not a healthcare economist. Dr. Hirsch’s math hinges on two recent studies from the UK (one from ADA 2019 and another published in Diabetologia in 2019) showing that the use of Freestyle Libre reduces DKA events and hospitalizations by 80%. Using an average cost of $26,556 per DKA-related hospitalization and 188,965 DKA-related hospitalizations recorded in 2014 (Diabetes Care, 2018), Dr. Hirsch calculated the cost of DKA in 2014 at ~$5.0 billion. Then turning to a medical inflation calculator, Dr. Hirsch estimated the cost of DKA hospitalizations in the US in 2019 at $5.73 billion. Using the 80% reduction in DKA hospitalizations using FreeStyle Libre from above, the total potential hospitalization cost savings comes out at $4.6 billion. This simple back-of-a-napkin calculation from Dr. Hirsch is pretty compelling evidence for, at the very least, expanding access to CGM to people with diabetes and cost-savings analyses will become increasingly important as the field continues to move into new populations, particularly basal-only or non-insulin type 2s.

  • Dr. Hirsch’s estimate for annual DKA-related hospital spending (~$6 billion) is a shocking number, but less than 2% of the CDC’s most recent estimate of $327 billion for the total cost of diagnosed diabetes in the US. Additionally, undiagnosed diabetes, prediabetes, and gestational diabetes has been estimated to cost another $77 billion. In classic Dr. Hirsch fashion, he tried to put some of these massive numbers into context, this time using three examples: (i) one billion seconds is equal to ~31.5 years; (ii) one billion pennies stacked on top of each other would make an 870-mile high tower; and (iii) if you saved $100,000 every year, it would take you 10,000 years to save $1 billion.

  • Dr. Hirsch also highlighted findings from a retrospective, observational analysis which showed immediate reductions of adverse events among patients with type 2 diabetes after initiating FreeStyle Libre. Additionally, there was a less significant, but still notable drop in all-cause inpatient hospitalizations with FreeStyle Libre. 

 

  • The real-world, prospective FUTURE study from Belgium showed reductions in adverse diabetes event hospitalizations, hypoglycemic comas, work absenteeism, and in days spent in the hospital due to diabetes events following nationwide reimbursement for FreeStyle Libre in type 1s. Dr. Hirsch pointed to the reduction in work absenteeism as being particularly notable, because it is an often-forgotten economic benefit from using continuous glucose monitoring systems.

  • Lastly, cost-effectiveness analysis of the 2017 2017 DIaMonD study (6-months of Dexcom G4 in type 1s) projected a reduced risk of long-term complications and a quality-adjusted life-year (QALY) increase of 0.54. Incremental cost-effectiveness ratio (ICER) for Dexcom G4 was $98,108/QALY with a 7-day sensor and $35,459 with a 10-day sensor. As Dr. Hirsch noted, US payers are open to paying for interventions <$100,000/QALY and most payers will cover interventions lower than <$50,000/QALY. UK’s NICE generally considers products conferring ~$30,000-$40,000 per QALY worth covering. We would anticipate the newer and improved CGMs would show even more promising long-term benefits. Dr. Hirsch focused these studies as providing strong evidence that CGM is a powerful, and underutilized, tool for bending the massive and quickly rising cost curve of diabetes. Many other clinicians have expressed similar sentiments, perhaps most eloquently written in a powerful commentary published last year in DT&T, calling current eligibility requirements for CGM insurance coverage “clinically irresponsible” and “penny wise and pound foolish.”

13. Medtronic’s ADA Analyst Day: $337 Million R&D Investment from Blackstone; No Publicly Shared Pipeline Timings Under New Leadership

On Friday evening, Medtronic hosted its annual one-hour Diabetes investor briefing, the first under new Diabetes Group head Sean Salmon – download the slides here and watch the webcast here. Obviously, a lot of airtime was dedicated to MiniMed 780G, which was really the star of the day on Friday (see read-outs from the US pivotal study, FLAIR trial comparing 670G with 780G, and a New Zealand study). Diabetes CMO Dr. Robert Vigersky gave most of the company’s remarks around MiniMed 780G, focusing on the system’s ability to increase Time in Range without increasing hypoglycemia, the system’s versatility across various populations (including teens and young adults), and ease of use (44%-46% fewer fingersticks vs. 670G, 95%-96% time in closed loop). Both Mr. Salmon and Dr. Vigersky also highlighted MiniMed 780G’s automatic correction bolus feature (which makes the system more forgiving of mistimed, miscalculated, or altogether missed meal boluses) and the ability to use a glucose set point of 100 mg/dl. Dr. Vigersky’s slides are on pages 15-22 and do a good job of highlighting key points from yesterday’s readouts – we’ve put a few down below, but you can get the full slide deck here.

  • Yesterday, Medtronic Diabetes announced a $337 million investment from Blackstone Life Sciences. That $337 million figure certainly jumps off the page and the funding will be used to “pull forward specific programs in [Medtronic’s] pump and CGM pipeline … beyond PCL [Personalized Closed Loop] and Synergy.” The funding will be used for “four identified Diabetes R&D programs”; when we had a chance to talk with Medtronic’s team this morning, we didn’t get many more details. If successfully commercialized, Medtronic expects to pay royalties in the “low- to mid-single digit range.”

  • Noticeably absent from this year’s presentation were anticipated regulatory submission and launch timelines for Medtronic’s pipeline products. See last year’s slides for comparison. On our call with Medtronic this morning, Sean Salmon noted that the company doesn’t want to “overpromise” to its patient and provider users. This seems like a prudent approach, as Medtronic, and many other diabetes device companies, have often failed to deliver on publicly announced products and timelines. “Reinvigorating” the CGM pipeline remains a priority for Medtronic, first with its “Zeus” sensor, which will bring calibrations to day 1-only in the US and zero calibrations outside the US. Synergy will bring a new, slimmer and fully-disposable design, along with an easier insertion process. An extended-wear infusion set remains in a US pivotal trial; CE-Marking was announced at ATTD 2020.

14. Drs. Philis-Tsimikas and Huang Debate Value of CGM in T2D; Clear Clinical Utility, but Case for Cost-Effectiveness Needs More Research and to Take the Who, What, and Where into Account

Drs. Athena Philis-Tsimikas (Scripps Whittier Diabetes Institute) and Elbert Huang (University of Chicago) debated the value of CGM in type 2 diabetes. Dr. Philis-Tsimikas bolstered the “pro” argument while Dr. Huang—admitting that he was assigned the more challenging side to defend—argued the “con.” Even as Dr. Huang made a strong economic-based case against the use of CGM in type 2 diabetes, he was primarily arguing that CGM shouldn’t be worn by all type 2s all of the time: “Should it be used for the whole population or are there subpopulations or moments for using CGM? As a technology for the entire type 2 diabetes population, the answer is probably that, no it’s not valuable to be deployed widely.” Both speakers were intrigued by the concept of “rental” (intermittent) CGM use and noted that the expected decline in cost in the coming years will tip the scale in the direction of value.

  • Wearing her clinician hat, Dr. Philis-Tsimikas wondered aloud: “Do we have to wait until someone who has type 2 diabetes for many years is on a complex medical insulin regimen and potentially has significant complications before providing them with a CGM to manage their disease?” She cited a handful of the early evidence of CGM benefit in non-MDI/CSII-treated type 2 diabetes: Erhardt et al (JDST 2011); Vigersky et al. (Diabetes Care 2012); Yoo et al. (Diabetes Res Clin Prac 2008); Lensing et al. (Diabetes Spectrum 2019); Allen et al. (Diabetes Res Clin Prac 2008); Cox et al. (Diabetes Care 2016); and Bailey et al. (DT&T 2016). These studies are all encouraging and range from prediabetes to basal-insulin-treated diabetes, but we’re in the early innings of building the evidence base for CGM in this population as many of them were small and short-term. Still, there is certainly enough evidence that both patients and providers can use CGM to improve clinical and behavioral outcomes to warrant further investigation. Dr. Philis-Tsimikas proposed that the value of can be improved (i.e., made more cost efficient) by integrating it into DSMES, leveraging AI tools to aid in data interpretation, using it intermittently and integrating with coaching, and by reducing/removing medications due to improvements from CGM.

  • Dr. Huang wore his economist hat (he’s also a physician), pointing out that not everything in health care can be “valuable” and there are budget constraints in the US health system. How then, given budget constraints, do we decide between spending money on CGM and spending money on other cost-effective interventions? While that question remains in the air, he did propose that CGM could be more valuable if it were used intermittently, the price were to decline, it were incorporated into the DPP without increasing programmatic costs (we’re not sure how this could work), improved glucose control while reducing need for medicines, and—most provocative—if we could show that it produces clinical benefits beyond glucose control, such as allowing people to social distance during the COVID-19 pandemic. We love this last idea of identifying positive externalities of CGM use and presenting them as opportunities for reducing cost! Dr. Huang also made the case that CGM use in type 2 diabetes hospitalizations and the long-term care setting is likely cost-effective, though that has not yet been studied.

    • The US is “famous” for spending >$10,000 per capita on healthcare annually (the highest in the world), for healthcare share of GDP that approaches 18%, for high obesity/diabetes rates and subsequent costs, and for high prices of services and products (insulin, for example). In this setting—exacerbated by COVID-19 and the likely coming depression—Dr. Huang explored whether CGM in type 2 diabetes is where we should be spending our marginal dollar. In type 1 diabetes, a lifetime of Dexcom CGM use was determined to be cost-effective in the DIaMonD trial, with an incremental cost-effectiveness ratio (ICER) of ~$98,000/quality-adjusted life-year (QALY). CGM is costly, but it’s A1c reductions are modeled to lower incidence of amputations, stroke, heart failure, renal disease, and other costly complications, resulting in a substantial QALY increase of ~0.5 years. Notably, with real-world use (e.g., extended sensor wear), ICER was reduced to ~$34,000/QALY—this shows that ICER for CGM in type 1 diabetes is highly sensitive to cost. In cost-effectiveness for type 2 diabetes, Dr. Huang pointed to two studies that have “wildly” different results: (1) Garcia-Lorenzo et al (J eval clin pract 2018) calculated an ICER of $198,453/QALY based on a meta-analysis of 5 studies; and (2) Fonda et al. (JDST 2016) calculated an ICER of $13,030/QALY. [Editor’s note: The cost of CGM use in Fonda et al. was significantly lower—$631.72—because it was used intermittently over the span of 3 months.] Fonda et al. point to the possibility of very cost-effective CGM, but there are many cost-effective interventions in diabetes, e.g., lifestyle, statin use for secondary prevention of CVD. How do we decide where to spend the marginal dollar? “Should a patient spend money on CGM over fresh food or medications?”, Dr. Huang posited. He also put forth that more data might not be better for mental health, and people might not share their data with their doctor, both of which could decrease the utility of CGM.

15. The “Frontline of Diabetes Care”: Dr. Richard Bergenstal Demonstrates Practical CGM Use in Type 2 and Hints at Further Uptake with COVID-19 Telehealth

“Is there anyone more grounding than Dr. Richard Bergenstal?” is a question we often ask ourselves while watching presentations from the Executive Director of Park Nicolett’s International Diabetes Center – today’s fantastic session on practical ways to personalize and optimize care using CGM was no exception. During the session, which came as part of Abbott’s industry symposium entitled “Translating Clinical Evidence for Sensor-Based Glucose Monitoring and Technological Innovations to the Front Line of Diabetes Practice,” Dr. Bergenstal treated us to one of his favorite rules-of-thumb: treat patients by both “thinking fast and slow” – “Fast” referring to in-the-moment therapeutic or behavioral changes based on real-time CGM numbers or trends, and “slow” meaning deliberative analyses of AGPs (many may also know the famous and Nobel-prize winning book by this name). To exemplify “slow” thinking, Dr. Bergenstal walked the virtual audience through a number of clinical examples, in which CGM led to practical, actionable changes in care.

  • For Jean, a 72-year-old woman with type 2, Dr. Bergenstal began by recommending HCPs ask the patient themselves to look at their ambulatory glucose profile (AGP) and self-identify areas that could be improved – although seemingly obvious, we found this tip to be quite useful in a time when both HCPs and patients may be rushed through in-person appointments or still trying to familiarize themselves with telehealth platforms. In Jean’s case, Dr. Bergenstal honed-in on her history of heart disease, and with a Time in Range of just 65%, recommended a long-acting GLP-1 to provide further cardio-protection and glucose management. Removal of Jean’s sulfonylurea further improved care by reducing time below 70 mg/dl.

 

  • Next, a man with type 2 diabetes exhibited what Dr. Bergenstal referred to as a “classic stair-step” AGP, in which glucose levels rise after each meal. The patient had previously tried and failed to tolerate a GLP-1 and had already worked up to 70U of insulin glargine at bedtime, with only 51% Time in Range and a worrisome amount of time in hypoglycemia . Dr. Bergenstal noted that he would recommend starting meal-time insulin, subsequently reducing glargine, and dropping the sulfonylurea. Dr. Bergenstal surmised that even one dose of prandial insulin at breakfast could improve the entire day’s outlook by preventing time above 180 mg/dL straight away.

  • In terms of “fast thinking,” Dr. Bergenstal shared a classic example of using trend arrows to better calculate insulin dose at mealtime. Patients can require vastly different doses depending on current glucose trends. Here, a patient with an “up” trend arrow, would need to add 3.5 units, while the same patient with a “down” arrow would need to reduce by 3.5 units, despite using the same insulin-to-carb ratio and correction factor calculation in each example.

  • Overall, Dr. Bergenstal’s very easy-to-understand clinical applications in patients with type 2 further highlight Abbott’s commitment to expanding into this patient population. During the company’s 1Q20 update, we estimated ~one million Freestyle Libre users with type 2, demonstrating Abbott’s undeniable headway into the field. Of course, Abbott’s low-cost CGM, in addition to its accessibility through the pharmacy, make it a very appealing option for payers, patients, and providers alike at a cost of $109/month, which is only $34/day.

  • Closing out his presentation, Dr. Bergenstal emphasized that the process of slowly working with patients through their AGPs “visit after visit, phone call after phone call” does seem to be translating well to telehealth during current stay-at-home advisories. As we imagine a healthcare system “after” COVID-19, we wonder if HCPs who have had to rely on CGM data during the pandemic will continue to prioritize the technology even when in-person visits are more of a possibility, and the CMS likely rolls back temporary exceptions for the treatment. From our view, they are likely to be much more enthusiastic about CGM following this work.

16. DKA Rates Among PWDs on Pump/MDI Cut in Half Following FreeStyle Libre Initiation in Large French Observational Study

Bichat Hospital’s Dr. Ronan Roussel presented compelling data from a nationwide cohort study (n=74,076 people with diabetes), showing that DKA rates were cut in half in the year following vs. preceding initiation of FreeStyle Libre. The analysis was performed using the SNDS database (the French reimbursement claims database), including type 1s and 2s on pump therapy or ≥3 injections/day (French reimbursement criteria) who had at least a year of follow-up. This cohort is made of 45% type 1s and 55% type 2s. As seen in the chart below, hospitalization rates fell by 52% for type 1 and 47% for type 2s (albeit from a lower base).  Improvements were observed in both pumpers and MDIers. And, while the improvement was seen across baseline strip usage, the most marked reduction was in the group who was registered as using zero strips per day. We’re noticing a greater push to generate evidence that baseline strip usage has little to no bearing on outcomes with CGM (see 875-P)—Medicare requires records of 4+ daily fingersticks before it will cover.

  • Remarkably, pharmacy claims show that ~1/4 of type 2s and type 1s on pump/MDI are using zero strips per day. Only roughly half are using at least four strips per day. It’s possible that they are obtaining strips through some other method (e.g., subscription bundle, black/gray market), but on its face, it is very striking that people who are dosing insulin multiple times per day are performing fingersticks so infrequently. Then again, this is one of the powerful forces underlying CGM adoption in this population.

  • Belgium has also reported very strong results from nationwide reimbursement of FreeStyle Libre. Their one-year follow-up report documents higher treatment satisfaction, less severe hypoglycemia (including coma), maintained A1c, and less work absenteeism.

17. Dr. Einhorn Describes the “New Era” of Diabetes Care Enabled by CGM and Telemedicine

During an Abbott product theater, Scripps Whittier’s Dr. Daniel Einhorn spoke excitedly about how the potent combination of CGM and telemedicine is bringing about “a new era” in diabetes care. On the CGM side, Dr. Einhorn provided a slate of Beyond-A1c aphorisms, such as “It’s the difference between having a synopsis vs. the whole short story.” He conveyed that adding telemedicine into the mix helps providers and patients get the most out of CGM: “In the old days we waited months between visits, and it would take years before people would get A1c lower. So those years are now being compressed to days and weeks. The idea of waiting three months between visits becomes silly because you can make decisions much faster [now with CGM]. We’re actually going to change the diabetes guidelines to reflect this possibility. The three-month adjustment cadence is artificial, based on A1c maturation. At the beginning I’ll see a patient a lot; when that person is stable, then I’ll see them less and less and less…If you need me every day, you got me every day. If you need me every six months, you got me every six months.” Dr. Einhorn’s team has the process down: Staff sets up the appointment through a telemedicine platform; the staff then emails/prints the preferred CGM reports for the provider; Dr. Einhorn then has a 25-30 minute discussion with the patient; Finally, the patient and doctor decide on a follow-up plan and set a time to follow up (“Historically, a month, two, or three later, but now, maybe a week or two later. It’s easier now”). This is undoubtedly a much-improved approach for diabetes care, but Scripps Whittier is a well-funded, diabetes-focused center. Even with the availability of codes for remote monitoring and CGM interpretation, reimbursement for more high-touch care (for those who need it, when they need it) is insufficient. CGM and telemedicine provide the technology, but there will likely need to be significant payment reform to encourage reorganization of care teams and delivery models before most clinics are able to offer the Scripps Whittier level of service. This is even more so the case at the primary care level, where the vast majority of people with diabetes are treated. But we’d love to be proven wrong!

18. Abbott Poster Shows FreeStyle Libre Decreases A1c by 0.9% in Non-Insulin Type 2s (n=497) and 0.6% in Basal-Only Type 2s (n=277) After Six Months; Reductions Sustained at 12 Months

A very exciting Abbott-sponsored poster (84-LB) presented by Dr. Eden Miller (Diabetes Nation) showed significant A1c reductions in basal-only and non-insulin-using type 2s one year after using FreeStyle Libre. The retrospective analysis looked at LibreView data from November 2017 – September 2019, A1c data from Quest Diagnostics, and medical and pharmacy claims data from Decision Resources Group. Compared to A1cs values recorded within 0-180 days before starting FreeStyle Libre, 6-month A1c values were reduced by a remarkable 0.9% (baseline: 8.5%) in non-insulin type 2s (n=497; p<0.0001). Similarly, basal-only type 2s (n=277) saw their A1cs drop from 8.5% to 7.9% (p<0.0001) after six months. Looking out to 12 months, non-insulin type 2s saw a 0.7% A1c reduction, from 8.6% to 7.9% (n=120; p<0.0001) and basal-only type 2s saw a 0.5% A1c reduction, from 8.4% to 7.9% (n=87; p=0.001). Unfortunately, data on demographics and comorbidities were not assessed. Similarly, data on non-insulin medications (e.g., SGLT-2s, GLP-1s) was not available and it’s certainly possible that some of the participants analyzed may have been initiated on a new medication and FreeStyle Libre at the same time. Still, the results provide convincing evidence that FreeStyle Libre has considerable value beyond intensive insulin users. As stated by Dr. Miller in Abbott’s press announcement of the abstract, “These data highlight how use of Abbott's continuous glucose monitor could be game-changing for people beyond intensive insulin users, translating to broader use of the technology to benefit all those living with diabetes, no matter where they are in the spectrum of care.”

  • Looking ahead, many (especially payers) will be especially interested in cost savings analyses for CGM in these populations. This was a particularly hot topic at ADA yesterday, where we saw Dr. Rich Bergenstal present data showing a 60% reduction in acute diabetes events and 33% reduction in all-cause hospitalizations for ~2,500 adults with type 2 diabetes after starting on FreeStyle Libre – this study included adults on intensive and non-intensive insulin therapy. We also saw an observational study from France linking FreeStyle Libre to a 47% reduction in DKA rates in type 2s on intensive insulin therapy. Lastly, Dr. Irl Hirsch looked at previous studies and some of his own calculations to estimate the US healthcare system could save $4.6 billion in DKA-hospitalization cost savings alone by using CGM.

19. Tandem’s Control-IQ in Young Children (2-5 Years; n=12) Increases TIR by 1.8 Hours/Day, Time <70 mg/dl Reduces From 3.7% to 1.5% (-32 min/day)

Stanford’s Dr. Laya Ekhlaspour read out very positive results from a small (n=12) study of Tandem’s Control-IQ in young children (ages 2-5 years). The study involved 2-7 days of run-in, 48-hours of Control-IQ in a supervised hotel setting, followed by 3-days of home use. During run-in, participants used a study pump at home in open-loop; during the hotel phase, the children participated in 30 minutes of activity per day, but there were no restrictions on meals or snacks and boluses were delivered per parents’ routines. Compared to run-in, every glycemic outcome was improved with Control-IQ at home. The study’s primary outcome measured the percentage of subjects meeting less than 6% time <70 mg/dl and less than 40% time >180 mg/dl goals. At baseline, one-third of participants met both goals, compared to two-thirds during the hotel phase (p=0.01) and 75% in the Control-IQ at home phase (p=0.002).

  • Time in Range was improved by 1.8 hours/day with Control-IQ at home vs. open-loop at home (62% vs. 68%). Most notably, Control-IQ at home delivered large reductions in hypoglycemia, reducing time <70 mg/dl by 32 min/day (3.7% vs. 1.5%; p=0.004) and time <54 mg/dl by 7 min/day (0.6% vs. 0.1%; p=0.004). Time in hyperglycemia (>180 mg/dl) was decreased by 1 hour/day (34% vs. 30%), though this difference was not statistically significant (p=0.075).

  • As we saw with Control-IQ’s adult and older children pivotals (read outs at ADA 2019 and ATTD 2020, respectively), Control-IQ delivered the biggest improvements overnight (11 PM – 7 AM). Time in Range overnight was 59% at baseline, compared to 79% during the hotel phase and 76% during the at home phase. Time >180 mg/dl was reduced from 39% at baseline to 24% during at home and time <70mg/dl was reduced from 2.1% to 0%.

  • Tandem’s Control-IQ is currently approved for ages 14+, but comes with a specific black box warning against use in patients under 6 years old. Mean total daily dose in this study was 15U/day and a special modification from Tandem was required to carry out the study for patients this young. Pediatric indication (6+ years) has already been submitted to the FDA, but larger and longer studies will certainly be needed in this very young population.

20. Five Medtronic Posters on Extended-Wear Infusion Set: 7-Day Survival Rate of 81%; Can Reduce Annual Insulin Waste by 5-10 Vials Compared to 2/3-Day Sets

We rounded up five posters on Medtronic’s Extended Wear Infusion Set (EWIS), which was CE-Marked as of ATTD 2020 and is currently in pivotal trial in the US. At ATTD, Dr. Ohad Cohen (Medtronic) shared that EWIS included a new “H-Cap Connector” to improve site performance, improved tubing to improve insulin preservative retention & stability, and an improved adhesive patch for better adherence. Three of the posters below do an excellent job outlining some of Medtronic’s work and considerations around those three improvements. A final poster also calculates ~$1,500-$3,000 in annual cost savings from reduced insulin waste with a 7-day infusion set.

  • A small (n=21 participants) study showed 7-day survival rates of ~81% for Medtronic’s EWIS (994-P). The 21 participants wore four infusion sets (82 total insertions) until set failure or seven days. There were no safety signals, including DKA, severe hypo- or hyperglycemia, device-related severe adverse events, and death. Survival of the extended-wear set at 7-days (80.5%) was actually higher than the published survival rate of Medtronic’s 3-day infusion set at 3 days (77%). When this data was first presented at ATTD 2020, Dr. Cohen also noted that survival rate increased to 85% if insertion failures were removed. Reasons for failure were site reaction/blood (7% of all wears), insertion failure (5%), unexplained hyperglycemia (3%), and adhesive failure (3%). Notably, the total daily dose did not increase over the seven days, indicating insulin delivery efficiency was not significantly changed over the entire period.

  • A modified version of Medtronic’s 3-day infusion set showed 7-day survival rates of ~73%-75% (997-P). The study, presented by Dr. Bruce Buckingham (Stanford), modified the cap of the MiniMed Quick-set connector to create a chamber which contained either foam or foam with 80 units of heparin (anticoagulant). Twenty participants were randomized into foam or foam + heparin groups to start. The participants wore their modified infusion sets for a week (or until failure) before crossing over. The study lasted four weeks (four insertions) with crossover after each week. The rate of 7-day survival was not significantly affected by the addition of heparin, as both modified versions had 7-day survival rates of ~73%-75%. The connector foam, without heparin, is part of Medtronic’s EWIS, in addition to other improvements.

  • Medtronic tested seven different adhesives across three studies – the best performing variant achieved an 8-day survival rate of 100% (986-P). The studies enrolled 75 adults testing 2 adhesive variants each, totaling 150 adhesive placements. “Non-functional” Medtronic pumps were used to simulate conditions. Eight-day survival rates for the first four variants (adhesives used with current infusion sets) ranged from 63% to 89%; eight-day survival rates for three new adhesive variants ranged from 75% to 100%. Across variants, there were no significant differences around overall appearance, skin irritation, or device awareness; however, the newer adhesives trended slightly more difficult to remove and clean off. The highest-performing adhesive (100% 8-day survival rate) was chosen in Medtronic’s EWIS.

  • In-vitro and porcine tests showed infusion set wear-time and inflammatory response were significantly impacted by loss of preservatives in the insulin (1012-P). One test demonstrated pumping insulin through an infusion set lowered the preservative content, though this did not significantly impact insulin chemical stability. However, lower preservative content insulin created more aggregates when the insulin was shaken. With increased aggregates in the insulin solution, the inflammatory response was greatly increased in an in-vitro cell culture and greatly decreased survival time of an infusion set in a porcine model.

  • Assuming insulin costs of $300/vial ($3/U), Medtronic estimated annual savings of ~$1,500 to $3,000 per user with a 7-day infusion set vs. 3-day (1167-P). The estimate used a total daily dose of insulin of 35U/day, translating to 5-10 vials per year of reduced insulin waste. Savings would be lower for those using >35U/day as the pump reservoir wouldn’t hold enough volume for seven days of insulin. Medtronic is also taking a look at the environmental benefits from increasing reservoir volume and extending infusion set wear time.

21. Dexcom-Sponsored Symposium Sheds Light on Success, Failures, and Opportunities for Inpatient CGM Implementation During the COVID-19 Pandemic

During a Dexcom-sponsored symposium, Dr. Shivani Agarwal (Albert Einstein) presented takeaways from implementing inpatient CGM during the COVID-19 pandemic at the Albert Einstein Medical Center, a three-hospital system in the Bronx. The FDA first authorized use of CGM in the hospital setting in April in the early stages of the pandemic, giving both Abbott and Dexcom the greenlight. Dr. Agarwal characterized the overall experience as positive, sharing key insights for early implementation, logistics, training, challenges, lessons, and future opportunities. For context, Dr. Agarwal noted that some of these experiences were unique to her hospital and patient population and that naturally, other hospital systems attempting to bridge the implementation gap should consider their unique circumstances. Regardless of the conditions, Dr. Agarwal shared the key to success in any setting is obtaining buy-in and support all around from hospital staff.

  • Early Implementation: Identifying eligibility criteria for patients requiring CGM on top of maintaining a positive attitude were two critical components of success during implementation. At her hospital system, type 1 patients, individuals with documented hypoglycemia, those experiencing insulin drips and requiring one-hour fingerstick measurements were eligible. Exceptional circumstances per nurse request or where self-monitoring was not possible also existed. To reiterate, creating a positive environment to encourage hospital buy-in was key.

  • Logistics: Nurse practitioners were responsible for inserting sensors in the arm. Receivers were placed on the door of the patient room facing outwards, within 20 feet of the patient, and reused after sanitization.

  • Training: A dedicated training team was assembled to help hospital nurses and medical staff learn how to interpret CGM data and make treatment decisions. Paper instructions were placed on patient doors, and 1:1 conversation was regularly utilized between hospital staff and a care team. Alarm triggers were used when signal disconnects occurred, indicating the need for a point-of-care fingerstick test. The hospital validated CGM readings with the common “20/20” rule, where CGM readings must be within 20 mg/dl or 20% of the fingerstick value. Interestingly, not all data was documented into the hospital’s electronic medical records system. Dr. Agarwal noted that an ongoing point of clarification is determining how much CGM data is necessary to collect and store from patients.

  • Successes: To-date, Dr. Agarwal shared that CGM has been implemented on twenty patients within the hospital system. The reduced need or personal protective equipment and hospital staff contact led to massive hospital buy-in. CGM use also shifted how care teams within the inpatient ward operated and increased appeal for additional medical device usage in inpatient settings. Today, hospital physicians partner with inpatient nurses and care teams to provide educational lessons about technology used in the hospital setting, increasing overall enthusiasm for future pilots.

  • Future: Dr. Agarwal shared that additional information on integrating CGM into data records and identifying patient characteristics most amenable to CGM are critical. Additional randomized control trials to determine the safety, accuracy, and utility of CGMs in hospitals are needed, along with securing legal approval to use cellphones as receivers.

22. Users Randomized to Glooko’s MIDS Basal Titration System and Control Group (Paper-Based Titration Tool) Have Similar A1c Reductions at 16 Weeks

In a large prospective RCT, use of Glooko’s FDA-cleared Mobile Insulin Dosing System (MIDS) for basal insulin titration in type 2 diabetes resulted in similar A1c reductions as standard of care (paper titration + enhanced CDCES support) at 16 weeks. The study enrolled 242 adults with type 2 diabetes who were on (89%) or initiating (11%) basal insulin and were not on or planning to start short-acting insulin. Both groups used Tresiba pens (Degludec U-200) and the Degludec Step-Wise titration algorithm which is based on fasting SMBG readings and personalized treatment plans to adjust insulin doses in increments of two units. The difference was the algorithm was configured by a provider and baked into the patient-facing Glooko app (see a deeper dive on this in the bullets), or done manually by the patient on a worksheet, with the guidance of an educator. At week 16, median A1c had improved significantly in both groups (-1.3% in MIDS; -1.2% in control), but the between-group difference was not significant. Similarly, median daily insulin doses increased significantly from baseline in both MIDS (+8 units) and control (+10 units) groups, but there was no between-group difference. While there were unfortunately no CGM outcomes to share (even periodic blinded CGM wear would’ve gone a long way), SMBG outcomes were presented: notably, proportion of readings <70 mg/dl were similar between MIDS and control (0.9% vs. 1.6%), but the MIDS group had significantly higher percent of readings in 70-180 mg/dl (77% vs. 70%) and fewer percent of readings >250 mg/dl (5% vs. 9%). While the study was planned (and failed) to show superiority of MIDS on the primary outcome of A1c reduction, we consider this study to be a major victory for Glooko and the field of digital health. Scripps Health’s Dr. Athena Philis-Tsimikas, the presenter, does as well:We’ve always known that having the help of an educator starting someone on insulin can encourage and allow them to better manage their diabetes. It’s impressive that you could do this equally with a digital tool that didn’t have as much contact with patients and you still see effect on A1c.” Indeed, MIDS is far more scalable and user-friendly than paper-based titration–assuming a base level of technological literacy and access—and there was no sacrifice in quality of outcome. Furthermore, a clinic that uses MIDS will free up its educators to do other, higher-impact work. We are hopeful that this rigorous study will propel MIDS and the handful of other basal titration apps (many also FDA-cleared) to increased adoption.

  • Dr. Philis-Tsimikas provided a glimpse at the clinician and patient interfaces. The clinician interface—which is presumably built right into Glooko—allows the clinician  to enter insulin type and starting dose, titration period/end date, SMBG data sufficiency requirements, and to configure fasting target range and adjustment paradigms. On the other end, SMBG readings are synced remotely via the Glooko mobile app, which reminds the patient to take fasting blood glucose readings and inject insulin daily. Every three days, a “Dose Adjustment Check” will determine, based on the clinician-configured plan, whether a dosage change is needed. Both interfaces look simple and intuitive. Last we heard, MIDS was piloting at a small number of US clinics—we wonder if this has since expanded.

23. Beta Bionics Gen 4 iLet Insulin-Only Pivotal Enrollment “Nearly Completed”; Home-Use Study Shows +1.9 Hours/Day TIR, Time <54 mg/dl From 0.6% to 0.2% With Bi-Hormonal vs. Insulin-Only

Dr. Jordan Sherwood (Massachusetts General Hospital) provided a detailed breakdown of positive results from Beta Bionics’ first home-use trial using liquid-stable dasiglucagon. Results from the 14-day crossover study were first announced via press release about one year ago. Towards the end of his presentation, Dr. Sherwood also shared that the insulin-only pivotal trial for Beta Bionics’ Gen 4 iLet is “currently underway,” with enrollment “nearly completed.” Assuming the trial will commence soon, it will come at a ~three-month delay from previous expectations to start in 1Q20; the goal is for insulin-only iLet to launch in ~early/mid-2021.

  • The small, home-use study randomized 10 adults with type 1 diabetes with pump and CGM experience to insulin-only and bi-hormonal iLet configurations. After one week, participants crossed over to the other configuration. The bi-hormonal configuration drove 1.9 more hours/day in-range (71% vs. 79%; p=0.002), reduced time <54 mg/dl by 6 min/day (0.6% vs. 0.2%; p=0.15), and reduced mean glucose from 149 mg/dl to 139 mg/dl, compared to the insulin-only configuration. Though not mentioned today, time <70 mg/dl was 2.4% during the bi-hormonal period and 3.6% during the insulin-only period – a difference of ~17 minutes. Notably, participants initiated therapy by entering only their body weight into the device (and no other parameters).

  • New from today, Dr. Sherwood also provided a look at subject-level differences in the insulin-only vs. bi-hormonal configurations. As shown below, nine of the ten participants saw lower mean glucose with the bi-hormonal iLet; this difference was significant (p<0.05) for eight of the ten participants. Additionally, Dr. Sherwood noted that during the bi-hormonal phase, 9/10 of participants had mean glucose below 154 mg/dl, which would correspond to an A1c ~7%; half of participants had mean glucose <154 mg/dl during the insulin-only phase. Time <54 mg/dl was low in both arms, though 8/10 participants saw improvements with the bi-hormonal configuration. With insulin-only iLet, 6/10 participants reached the consensus target for <1% time <54 mg/dl, compared to 9/10 participants with bi-hormonal iLet.

24. Bi-hormonal Bionic Pancreas Optimal for Mean Glucose (136 mg/dl) and TIR (81%) at 100 mg/dl Set Point, with no Increase in Hypo (Compared with 115 and 130 mg/dl)

MGH’s Dr. Marwa Tuffaha presented Bionic Pancreas (Beta Bionics) data from 2015 showing that lower glucose targets reduce mean glucose and greater time-in-range without additional hypoglycemia. Since this study was conducted a few years ago, the Bionic Pancreas AID system consisted of an iPhone 4s running a control algorithm, a Dexcom G4, and two Tandem t:slim pumps (one with insulin and one with glucagon). While today’s gen 4 iLet is fully-integrated, the algorithm is the same, requiring only body mass to initialize and qualitative meal announcements. 20 participants with type 1 diabetes and wide demographic ranges (e.g., A1c 6.1%-9.3%, ages 21-78 years, BMI 20-42 kg/m2, and diabetes duration 5-54 years) were enrolled in a random-order, real-world crossover study evaluating the Bionic Pancreas at three set points (130 mg/dl, 115 mg/dl, and 100 mg/dl), plus usual care. As seen in the figure below, lower targets were associated with significantly lower mean sensor glucose (lowest: 136 mg/dl @ 100 mg/dl target) and higher mean time in 70-180 mg/dl (highest: 81% @ 100 mg/dl target). These stellar outcomes did not come at the cost of more time <60 mg/dl, which is explained by a significantly higher daily dose of glucagon—8.3 ug/kg were administered daily with the 100 mg/dl set point. Interestingly, there was no difference in total daily dose of insulin across set points, though we’d bet there was significantly more “prandial” insulin delivered with the lower set points, quickly bringing users back into range, as opposed to the highest set point where meals were likely handled more conservatively leaving the basal rate to chase highs for the rest of the day. User preference was very high across all three set points, though with significantly higher satisfaction for the 100 mg/dl set point. Note that the participants were not blinded to the system setting, so one might expect higher satisfaction ratings with the lowest set point. None of the patients in the study had DKA or severe hypoglycemia, there was no difference in hypoglycemia symptoms, interventions, or carbs administered per day, and glucagon-induced nausea was not an issue. The integrated Gen 4 iLet with Zealand Pharma’s dasiglucagon is expected to enter pivotal trials in the back half of this year (see FFL 2019) — enrollment is “nearly complete” (see above).

25. First Lilly AID Data Shows System is Safe in Small, Inpatient Feasibility Study—Negligible Hypoglycemia after Pizza/Pancakes, With or Without Premeal Bolus

Lilly’s Ms. Amy Bartee presented what we believe to be the first data Lilly has shared on its AID system (read about the system, which uses Dexcom CGM, a cool DEKA-developed pump, and the MPC algorithm from Class AP). This was a small (n=10) inpatient feasibility study that demonstrated safety in response to high carbohydrate meals with and without pre-meal boluses. Specifically, participants (mean age 52 years; mean A1c 7.1%; all pump users) were given four meal challenges over a 48-hour inpatient period: (1)  bolus with pizza (high-fat meal); (2) missed bolus with pizza; (3) bolus with pancakes (fast-acting carbohydrate meal); and (4) missed bolus with pancakes. As expected with the pizza challenge + bolus, there was a slow rise in glucose and extended postprandial period—the algorithm steadily increased basal insulin delivery to compensate, yielding <1% below 70 mg/dl and 66% time-in-range. In the pizza challenge + missed bolus, the algorithm responded with an immediate and incremental increases in basal delivery for the next four hours, delivering <1% below 70 mg/dl and 37% time-in-range. No correction boluses were given in either pizza challenge. In the pancake challenge + bolus, there was no hypoglycemia and 56% time-in-range. As expected, the pancake challenge + missed bolus was the most challenging for the algorithm, which rapidly ramped up basal insulin delivery but 60% of the next four hours were spent >250 mg/dl (no time <70 mg/dl). One patient required a correction bolus in this condition. Ultimately, the system was safe in every case, though the study emphasized the importance of bolusing for meals, particularly in the absence of automated corrections. Lilly has never shared specific timing for development of its AID system, though it has broadly said that Connected Care products will launch in stages over 2019-2021; we wouldn’t be surprised to see this timeline pushed back.

Percent time in ranges in four hours postprandial

 

Pizza with bolus

Pizza without bolus

Pancakes with bolus

Pancakes without bolus

% Time in 70-180 mg/dl

66%

37%

56%

18%

% time <70 mg/dl

0.8%

0.8%

0%

0%

% time >180 mg/dl

33%

63%

44%

82%

% time >250 mg/dl

11%

30%

11%

60%

 

26. Dr. de Bock Very Enthusiastic About 780G’s Impact on TIR in “Challenging” Adolescent Group

University of Otago’s Dr. Martin de Bock rehashed very positive MiniMed 780G CE registration trial (see Day #1 coverage), emphasizing that the greatest day-time time-in-range benefit was seen in the most challenging population: adolescents (ages 14-21). In fact, the whole population was skewed young, with a mean age of 23.5 years and 56% under 21. Said Dr. de Bock, “This is really exciting for us, especially as pediatric endocrinologists…Maybe we shouldn’t be so concerned about people who aren’t so adherent to their therapy – they are the most likely to see advanced time-in-range on [780G].” We would caution that the mean A1c in the study was 7.6% and all participants were already on a pump, so this population, though still adolescents, may not be the “most challenging of the challenging.” For reference, mean A1c in people ages 15-18 in the T1D Exchange Registry is 9.3%. Still, the signal is encouraging, and may due to the system’s improved user experience. There was only ~1 auto mode exit per week in this study, compared to ~1 per day as seen with the 670G. Similarly, the system significantly reduces alarms to 8 per day (vs. 13 per day with 640G). P-979 further elaborates ono improved satisfaction and sleep quality with 780G vs. 640G. Echoing his Friday comments, Dr. de Bock concluded: “If we had more time, we hypothesize we could’ve optimized settings…and seen overall Time in Range that was higher than reported here.”

27. Ultra-Rapid Insulins Safe and Effective in 670G & 780G, but Apparently Not More So Than Rapid-Acting Insulins; Possible Exception: 780G with Fiasp May Blunt PPG Relative to Novolog

We enjoyed back-to-back orals investigating the benefit of ultra-fast insulins in Medtronic’s (advanced) hybrid closed loop systems. In short, the benefit appears to be marginal, at best. However, it is does seem safe and feasible.

  • Atlanta Diabetes Associate’s Dr. Bruce Bode first showed in a double-blind crossover study (n=42) that there was no difference in glycemic outcomes with MiniMed 670G when participants used Lilly’s Ultra-Rapid Lispro (URLi) or Humalog. Time-in-range was 77% with Humalog and 78% with URLi, and time <70 mg/dl was ~2% in both cases. There was no difference in time-in-range between the two conditions during day or night, nor post-meal.  There were five cases of infusion site pain/reaction in the URLi condition vs. one in the Humalog condition, but overall, booth conditions were very safe and there were no discontinuations. We and a number of chat participants felt that perhaps the participants were already too well-managed on the 670G at baseline to see an impact of even faster-acting insulin. Mean baseline A1c was 7.1%, mean time-in-range was 78%, and participants were already spending ~90% in-range. Dr. Bode additionally proposed that a hybrid closed loop system that only modulates basal insulin “may not be optimized for the potential advantages of an insulin with differential effects seen primarily during bolus delivery.” Benefits may be realized, he reasoned, if it were used in a system that gives automated boluses, such as MiniMed’s AHCL (780G) system. The next oral looked at exactly that.

  • University of Melbourne’s Dr. Melissa Lee presented a similarly-designed crossover study (n=12) comparing the use of Novo Nordisk’s Fiasp and Novolog in the MiniMed 780G. Despite Dr. Bode’s hypothesis, this study found no overall benefit of Fiasp, with the potential exception of blunted postprandial spikes after breakfast. Though smaller, the study population was similar to above, with experienced pumpers with mean baseline A1c of 7.1%. In both conditions, time-in-range was ~80%, hypoglycemia was <0.5%, and mean glucose was <140 mg/dl, with no statistically significant differences but trends toward Fiasp benefit. There was no difference in CGM metrics postmeal when all meals were pooled, but when separated, incremental AUC over 2 hours was significantly lower with Fiasp only for breakfast. Breakfast-time may be unique for a whole host of reasons, said Dr. Lee: less insulin resistance in the morning, snacks/stress/exercise later in the day, or just simpler carbs at breakfast. While 780G was safe with both insulins, there were four individuals who experienced at least one mild infusion-site reaction using Fiasp vs. only one using Novolog. 

28. OHSU’s Novel Interventions in Children’s Healthcare Onboards Vulnerable Youth with Diabetes Technology While Addressing Social Determinants of Health

Dr. Kimberly Spiro (Oregon Health Sciences University) presented on the Novel Interventions in Children’s Healthcare (NICH), an innovative program at OHSU that attempts to provide access to diabetes technology supplies to underserved patients by addressing underlying social determinants. Participants, primarily children and teenagers, are paired with a trained interventionist, accessible 24/7, to solve issues that may prevent them from following medical instructions from endocrinologists and other clinical professionals. Participants receive 12 months of care on the program at no cost. The program has had major success in assisting diabetes challenged by a variety of social conditions including food insecurity, unreliable transportation, insecure housing, limited access to medical care, and Child Protective Services (CPS)/Department of Human Services (DHS) involvement. Overall, we’re excited to see interdisciplinary programs like these provide person-centered diabetes care and technology to break the intergenerational cycles of systemic inequities many in the healthcare system face. Dr. Spiro shared that in the US today, DKA serves as the number one cause of death for type 1 youth, with 65% of cases also impacting those under the age of 19. In our view, the vast majority of these deaths are preventable: just yesterday, we saw three presentations demonstrating that DKA-related hospitalizations could be greatly reduced (50%-80% reductions) by using CGM – we know this is true, and strongly believe that no patient should ever again be in the hospital for severe hypoglycemia or for DKA not associated with a diabetes diagnosis (and obviously, to reduce those dramatically is an important goal though unlikely it will get to 0% without a major increased focus on increased screening and prevention in type 1). Dr. Spiro presented several cases highlighting the sheer impact of the program. To-date, the program has helped 0ver 350 patients get access to diabetes technology which they otherwise would not have secured any assistance.

  • Case 1 – David (14 year-old, male, type 1): Prior to NICH, David had endured a variety of challenges with neglect. During his youth, he had moved around five different foster homes before moving in with an abusive aunt and uncle. At age ten, his aunt, serving as his primary contact, was diagnosed with cancer, and one year later, he was diagnosed with type 1. Shortly after, his uncle ceased all contact with his aunt, forcing David to skip school, run away from home, and miss appointments. During this time, David and his aunt’s relationship became tense. Simultaneously, his endocrinologist did not want to provide him with CGM or an insulin pump because of concern over impaired awareness of hypoglycemia. The reluctance to provide a CGM is perplexing to us. At that point, David had endured three episodes of DKA and had an A1c of 10.8%. After being referred to NICH, his interventionist addressed the potential pros and cons of CGM. David was able to open up about his concerns about being overwhelmed with data along with his concerns of his aunt being able to watch over him with remote monitoring capabilities. As a result, to ease David into technology, the interventionist onboarded him with Dario’s connected BGM system and Companion’s InPen, through which he shared data with both his aunt and interventionist. To better David’s relationship with his aunt, the interventionist used positive reinforcement behavior. For example, if David agreed on a treatment decision from the data such as taking insulin or choosing to exercise, he accumulated “points” with his aunt, which he could then use to spend time with friends. To give him a sense of personal control, his aunt started allowing him to be out with his friends as long as he checked the data and shared it with her. After using the Dario system, David agreed to begin using a Dexcom G5 CGM, during which he learned about his glucose patterns and how his behaviors affected to his diabetes management. Over time, he found the ideal alarm settings and through additional counseling to address prior trauma as recommended by his interventionist, his A1c dropped from 10.8% to 9% after the program. In a testament to the interdisciplinary effectiveness of NICH, David said the program has “opened up new resources” to him and helped “organize things in daily life.”

  • Case 2 – Samantha (13-year old, female, type 1): Prior to NICH, Samantha had endured trauma related to domestic violence. Her mother had two kids with two different partners and was briefly incarcerated, during which she lost access to her kids. After her mother’s jail time, without access to a sustainable job, Samantha faced homelessness, during which her A1c climbed all the way up to 14%! She was also unable to access any diabetes technology because of the Medicaid requirement of 4 document fingersticks/day for 90 days. However, after being referred to NICH, she worked for nine months with her interventionist and was eventually able to document 3-4 fingersticks per day. This enabled her to access the Dexcom G5 CGM, during which her interventionist taught her how to read glucose patterns. Before CGM, Samantha had refused to dose insulin before meals, but after seeing her patterns, she changed this habit. After three months on CGM, her A1c dropped from 14.1% to 10.2%, upon which she finished the program. While there is certainly more room for improvement, Dr. Spiro acknowledged that it was NICH which provided her with the problem-solving skills and confidence to use technology and create self-management practice in very difficult of circumstances.

  • Case 3 – (Matthew, 18 years old, type 1): Prior to NICH, Matthew had endured over 20 episodes of DKA. His parents endured significant mental health issues and were regularly on the run from DHS. After being diagnosed at age eight with type 1, Matthew left his house to live with his grandparents but was kicked out after having two episodes of DKA in the first three months of living with them. He eventually got a job and decided to independently live with a bicycle as his only mode of transportation but was on the verge of eviction throughout a period of insecure housing. During this time, Matthew would make anywhere from three to four emergency department visits per month. After being recommend a Dexcom G6 from an interventionist in NICH, Matthew’s hospitalization rate changed to only 1x/month while his A1c dropped from 14% to 9.4% upon completing the program.

29. Real-World Data from Control-IQ: +2.4 Hours/Day TIR, 96% Time in Closed Loop in 1,649 Early Adopters; Improvements for Both Type 1s and Type 2s

Tandem presented two posters featuring very positive real-world data from early adopters of Control-IQ. Control-IQ was cleared in December 2019 and officially launched in January 2020. The data presented in both posters came from Tandem users who had begun using Control-IQ before March 11, 2020. As of April, Tandem shared that “more than 30,000” t:slim X2 users had updated their pumps to the hybrid closed loop algorithm. See below for a summary of both posters and a comparison to the pivotal data.

  • Through the first 30-days of Control-IQ use, Time in Range was increased by 2.4 hours/day (compared to pre-Control-IQ data) and users spent a remarkable 96% of time in closed loop (95-LB). This data set included participants with at least 30 days of pre- and post-Control-IQ data in t:connect and included a total of 1,659 subjects. The Time in Range improvement was driven by a 9.5% reduction in time >180 mg/dl (-2.3 hours/day). The reduction in time <70 mg/dl was low both pre- and post-Control-IQ (1.2% before vs. 1.1% after). This result is unsurprising as most users will likely on Tandem’s predictive low glucose suspend algorithm, Basal-IQ, before going to Control-IQ. Mean glucose fell from 161 mg/dl to 148 mg/dl and GMI fell from 7.2% to 6.9% before and after Control-IQ.

  • Control-IQ significantly improved glycemic outcomes for both type 1 and type 2 users (126-LB). This second poster looked at 2,896 participants with type 1 and 144 participants with type 2 diabetes who had at least 14 days of pre- and post-Control-IQ data in t:connect. The data are summarized in the table below and both groups spent 96% of time in closed loop. Time in Range was improved by 2.1 hours/day in the type 1 subgroup, compared to a 1.4 hour/day improvement in the type 2 subgroup, though the type 2 group had a higher baseline. After two weeks on Control-IQ, participants spent an incredible 77%-79% of Time in Range! Notably, total daily dose of insulin was increased in both groups, with the type 2 group seeing a sizeable 12% increase (73 U vs. 82 U).

 

Type 1s (n=2,896)

Type 2s (n=144)

Before

After

Before

After

Time in Range

67%

77%

74%

79%

Time <70 mg/dl

1.1%

1.0%

0.2%

0.2%

Time >180 mg/dl

31%

21%

25%

20%

Total daily dose

46 U

48 U

73 U

82 U

Time in closed loop

96%

96%

  • Improvements from Control-IQ’s early adopter real-world data and the US pivotal trial are comparable, with real-world users spending even more time in closed loop (96% vs. 92%; ~1 hour/day). In the US pivotal, Time in Range was improved by +2.6 hours/day (59% to 71%); however, given the much higher baseline in the real-world users, the +2.4 hours/day improvement from Control-IQ in the real-world may be even more impressive (68% to 78%). In both trials, the vast majority of the Time in Range improvement came from reductions in hyperglycemia and presented within the first 30 days. Improvements in A1c/GMI and mean glucose were identical (-0.3% and -13 mg/dl, respectively), though from lower baselines in the real-world user group. It’s worth noting that these were early adopters of Control-IQ and likely are not representative of general t:slim X2 users and are certainly not representative of the general type 1 population; in contrast, the Control-IQ pivotal was notable for its broad inclusion criteria (no entry restrictions on A1c, severe hypo or DKA, or device experience). Finally, while the real-world results compare pre- and post-Control-IQ data (i.e., single-arm), the pivotal study randomized users to SAP vs. Control-IQ (i.e., double-arm).

 

Pivotal

Real-world data

SAP at Six Months (n=56)

Control-IQ at Six Months (n=112)

30 days before Control-IQ (n=1,659)

30 days after Control-IQ (n=1,659)

Time in Range

59%

71%

68%

78%

A1c/GMI

7.4%

7.1%

7.2%

6.9%

Time >180 mg/dl

38%

27%

31%

21%

Mean CGM

170 mg/dl

156 mg/dl

161 mg/dl

148 mg/dl

Time <70 mg/dl

1.9%

1.4%

1.2%

1.1%

Time in closed loop

--

92%

--

96%

30. Dexcom G6 With Urgent Low Soon Alerts Reduces Rebound Hyperglycemia Events by 7% After <70 mg/dl Event, 33% After <54 mg/dl Event vs. Dexcom G5

Dexcom’s Dr. Giada Acciaroli presented real-world data from 24,518 Dexcom users who transitioned from Dexcom G5 to Dexcom G6 (with urgent low soon alerts) in 2018. Results showed significant reductions in rebound hyperglycemia events and duration with Dexcom G6 users who had their predictive low glucose alerts turned on. As a reminder, G6’s “Urgent Low Soon” alert comes 20 minutes ahead of a predicted <55 mg/dl event. Rebound hyperglycemia was defined as glucose levels >180 mg/dl occurring within 2 hours of a hypoglycemic event (glucose value <70 mg/dl or <55 mg/dl). Following a hypoglycemic event <70 mg/dl, the number of rebound hyperglycemia events per week was reduced from 1.83/week to 1.7/week (p<0.001). Additionally, mean duration of these events was reduced from 214 minutes to 197 minutes (-8%; p<0.001). Following hypoglycemic events <55 mg/dl, the number of rebound hyperglycemia events per week was reduced by 33% (from 0.78/week to 0.52/week; p<0.001) and duration was reduced by 22% (from 219 min to 171 min; p<0.001).

  • Dr. Acciaroli also presented real-world Dexcom data demonstrating the correlation between rebound hyperglycemia events and glucose variability. Looking at the quartiles of Dexcom users with lowest glucose variability (%CV<31.2%) and highest glucose variability (%CV>39.1%), the unstable cohort saw 7.4x more rebound hyperglycemia events per week, 0.41 events vs. 3.03 events/week (p<0.001). This translates to a difference of one event every ~17 days in the low variability quartile vs. one event every ~2.3 days in the high variability quartile.

31. Tandem’s Basal-IQ (Predictive Low Glucose Suspend) Significantly Reduces Self-Reported Severe Hypo Paramedic Visits (-45%), ER Visits (-77%), and Hospital Admissions (-75%)

Ms. Molly McElwee-Malloy (Tandem) presented strong real-world data on reductions in severe hypoglycemia related paramedic visits, ER visits, and hospital admissions with Tandem’s predictive low glucose suspend Basal-IQ algorithm. Across all three types of adverse events, the biggest improvements were seen in type 1s who switched from MDI to Basal-IQ. The study surveyed 665 type 1s who had recently started using t:slim X2 with Basal-IQ and received a $20 gift card for their participation. Participants self-reported severe hypo-related adverse events at baseline and after six months on Basal-IQ. Participants had a mean age of 37, 15% were on MDI at baseline, and 91% used CGM at baseline.

  • The percentage of participants reporting hospital admissions related to severe hypoglycemia in the last six months was reduced from 3.7% (24/655 participants) at baseline to 0.9% (6/655 participants) with Basal-IQ. The 95 participants switching from MDI saw a dramatic reduction: at baseline, 10 participants experienced a severe hypo hospitalization in the last six months, compared to just 2 during six months of Basal-IQ.

  • The percentage of participants reporting ER visits related to severe hypoglycemia in the last six months was reduced from 5.9% (39/655 participants) at baseline to 1.4% (9/655 participants) with Basal-IQ. Once again, the 95 participants switching from MDI saw the biggest improvement: at baseline, 14 participants experienced a severe hypo ER visit in the last six months, compared to just 4 during six months of Basal-IQ.

  • Similarly, the percentage of participants reporting paramedic visits related to severe hypoglycemia in the last six months was reduced from 5.3% (35/655 participants) at baseline to 2.9% (19/655 participants) with Basal-IQ. The 95 participants switching from MDI saw the biggest improvement: at baseline, 15 participants experienced a severe hypo paramedic visit in the last six months, compared to just 6 during six months of Basal-IQ. Notably, Ms. McElwee-Malloy also shared that ~5% of all EMS calls nationally are related to hypoglycemic events. Given the very high cost of severe hypo adverse events, we loved seeing these results and would be interested in seeing a longer-term and broader cost-effectiveness analysis of Basal-IQ (and Control-IQ).

32. One Drop to Launch Long-Term (1-6 Month) Outcomes Forecasts For 30-day Average Glucose, Blood Pressure, and Weight “Within the Year”; Overnight Hypo Prediction Model Achieves AUC of 0.82 for CGM Users

This morning, One Drop announced plans to launch long-term outcomes forecasts for “diabetes-related biomarkers” and overnight hypoglycemia predictions for CGM users. The prediction capabilities are expected to launch “within the year” and are part of One Drop’s efforts to shift health management from “reactive to prospective.” The predictions for CGM users will be based on real-time CGM data – the regulatory classification for this “depends on a bunch of things,” so we’d imagine the timeline could change quite a bit. One Drop also told us that the long-term outcomes forecasts may be presented directly to users, but the primary use would be to “provide personalized self-care guidance.”

  • One Drop’s supervised learning models for predicting blood pressure, weight, and 30-day average glucose were significantly more accurate than a “naïve persistence” model (assuming no change over time) on all prediction horizons (38-LB). The study included data from ~55,000 One Drop app users and generated over 200,000 test-set predictions across blood pressure, weight, and average glucose. Root mean square error (RMSE) for systolic blood pressure was 9.4 mmHg on a 1-2 month prediction horizon (~17% better than persistence model), rising to 11.4 mmHg on a 4-6 month prediction horizon (14% better than persistence). Predicted weight RMSE was 2.1 kg on a 1-2 month horizon (6% better than persistence) and 3.9 kg on a 4-6 month horizon (7% better than persistence). RMSE for 30-day average glucose in BGM users was 34 mg/dl on a 1-2 month horizon and 44 mg/dl on a 4-6 month horizon (22% and 18% better than persistence, respectively. Predictions for CGM users were more accurate: RMSE was 14 mg/dl on a 1-2 month horizon (26% better than persistence) and 19 mg/dl (13% better than persistence).


  • Another poster showed One Drop’s model for predicting overnight hypoglycemia (<70 mg/dl) in CGM users achieved an AUC of 0.82 (14-LB). The machine learning-based model was trained on ~360,000 nights of data and tested on ~200,000 nights of data from “over 3,000” One Drop users with CGM. 86% of users in the dataset had type 1 or LADA, 8% had type 2, and 6% were unreported. The model achieved an AUC of 0.82 and appeared well-calibrated (see table below): in users with a predicted probability of hypoglycemia of 90%-100%, the actual frequency of overnight hypoglycemia was 97.5%. One Drop identified certain combinations of glucose variability, activity, food, and heart rate data that drove better predictions – these combinations were available in ~30% of the total dataset and these “high-confidence predictions” achieved an AUC of 0.87.

33. Analysis Estimates ~$300 Million in Annual Cost Savings if CGM Were Given to All Type 1 Medicaid Beneficiaries; $1.15 Billion in Reduced Costs vs. $850 Million to Cover Dexcom G6 for All Type 1 Beneficiaries

Mr. Michael Minshall (Certara Evidence & Access) presented a budget impact analysis estimating ~$300 million in net annual cost savings if CGM were given to all type 1 Medicaid beneficiaries. The analysis calculated ~$1.15 billion in total reduced costs with real-time CGM, compared to ~$850 million to cover Dexcom G6 (in lieu of SMBG) for all ~600,000 Medicaid beneficiaries with type 1 diabetes. A walkthrough for these calculations is provided in the bullets below and a summary of analysis’ findings are in the table below.

  • Mr. Minshall estimated a total of 593,378 Medicaid beneficiaries with type 1 in 2020. This came from ~72 million Medicaid and CHIP enrollees in 2019, multiplied by 13.9%, the reported prevalence of diabetes in the Medicaid population. Of those ~10 million Medicaid beneficiaries with diabetes, Mr. Minshall estimated ~585,000 with type 1 based on the prevalence of type 1 vs. type 2 diabetes in the general US population. Lastly, Mr. Minshall added on ~9,000 new type 1s in the Medicaid population, in line with historical trends.

  • Mr. Minshall estimated $396-738 million in cost savings related to A1c reductions from CGM vs. SMBG. Based on data from the 2017 DIaMonD trial (Dexcom G4 vs. SMBG in type 1 MDI adults), Mr. Minshall assumed CGM could deliver a 0.6% A1c reduction vs. SMBG. Mr. Minshall then referred to two studies estimating annual savings from reductions in A1c. One study (Gilmer et al., 2005) estimated $1,111 in savings from a 1% A1c reduction, while another (Wagner et al, 2001) estimated $2,073 in savings for the same A1c reduction. These cost savings, multiplied by 0.6 (from the 0.6% A1c reduction with CGM), formed the low and high ends of ranges used in Mr. Minshall’s cost analysis.

  • Mr. Minshall estimated $210 million in savings from reduced severe hypo hospitalizations and $207 million from reduced DKA hospitalizations. The estimated reductions in severe hypoglycemia and DKA-related hospitalizations came from the RESCUE study in Belgium, which found 73% reductions in severe hypoglycemia hospitalizations and 80% reductions in DKA hospitalizations after real-time CGM was reimbursed for type 1s. Notably, Dr. Irl Hirsch cited similar statistics in his talk on Saturday where he calculated a potential $4.6 billion in potential cost savings for the entire US around DKA-related hospitalizations. The cost per severe hypo hospitalization was estimated at $4,068 (Liu et al., 2018) and the cost per DKA hospitalization was estimated at $9,733 (Tieder et al., 2013). Mr. Minshall noted that both of these numbers were on the more conservative side of estimates.

  • Lastly, according to Mr. Minshall, moving all type 1 Medicaid beneficiaries from SMBG to real-time CGM would add ~$849 million in added annual cost. These calculations estimated total SMBG cost at $1,091/year per user ($0.39/test strip, $0.04/lancet, 7 fingersticks/day). Total cost of Dexcom G5/G6 were based on CMS’ Fee Schedule for US state Medicaid plans for 4 transmitters and 37 sensors. 

  • Limitations of this budget impact analysis include indirect costs associated with diabetes care and treatment. Mr. Minshall was careful to note the limitations of this analysis stating they only investigated the direct costs associated with A1c and hypoglycemia or diabetic ketoacidosis and therefore could be missing hidden costs. Furthermore, this budget impact analysis did not investigate indirect benefits of CGM such as increased productivity and quality of life metrics that could be important in future assessments.

34. Flash CGM Scanning Frequency Indicates that Time Until Performing a Scan After Dropping <54 mg/dl Might be Strongest Predictor for Impaired Awareness Assessment

Dr. Othmar Moser (Medical University of Graz) presented some of the first data investigating whether type 1 patients with impaired awareness of hypoglycemia and flash glucose monitoring demonstrated different “scan” behavior compared to those with regular awareness of hypoglycemia. Participants (n=92; baseline A1c: 7.3%) who had used flash CGM for at least three months along with those with Gold-, Clarke-, and Pedersen-Bjergaard scale scores indicating impaired awareness were included. Notably, the distribution of participants with normal and impaired awareness of hypoglycemia varied by scale used (see table below). Using Pederson-Bjergaard scores, the split of participants between impaired awareness and non-impaired awareness was exactly 50/50. 

  • GOLD Scale: When comparing data on participants classified via the GOLD Scale (n=18 impaired; n=74 non-impaired), significant differences were observed in scan time for those with level 1 hypoglycemia (54-69 mg/dl) and nighttime level 2 hypoglycemia (<54 mg/dl). Participants with impaired awareness took 78 minutes to perform a scan after reaching a hypoglycemia compared to 63 minutes for those with impaired awareness for daytime level 1 hypoglycemia. Similarly, those with impaired awareness took 140 minutes to make a scan relative to 96 minutes.

  • Clarke Scale: No statistical differences were seen for any hypoglycemia levels, night or day.

  • Pedersen-Bjergaard Scale: Statistical differences (n=46 impaired; n=46 non-impaired) were observed for level 1 hypoglycemia, nighttime level 1 hypoglycemia, and nighttime level 2 hypoglycemia. Participants with impaired awareness respectively took 76 minutes, 132 minutes, and 134 minutes compared to 54, 89, and 80 minutes for those with regular awareness.

  • Receiver operating characteristic (ROC) curve analysis for the time until performing a scan after reaching level 2 nocturnal hypoglycemia was done and resulted in an area under the curve of 0.79 (p<0.0001) along with a sensitivity and specificity of 73% each. While technical explanations were not provided, Dr. Moser mentioned that if an episode of level 2 hypoglycemia (<54 mg/dl) occurs at night and a patient performed a scan after 135 minutes, then this was sufficient to diagnose the individual with impaired awareness of hypoglycemia.

35. DPP + Three Whole-Population Interventions Modeled to Reduce 10-Year T2D Incidence by 17% at Cost of ~$68k/case prevented; Neat Tool Helps Policymakers Determine Prevention Path

RTI International’s Mr. Simon Neuwahl presented a simulation modeling analysis indicating that a combination of risk-based (e.g., DPP) and three whole population-based interventions (e.g., soda tax, worksite health promotion, and bike lanes) will be necessary to reduce US type 2 diabetes incidence by 17% within the next 10 years. The interventions are estimated to come with a price tag of ~$500/person – $164 billion overall – and appear to fall short of the CDC’s target of a 21% incidence reduction by 2025. For context, 1.4 million people were diagnosed with type 2 diabetes in the US in 2018, so the cocktail of interventions proposed by Mr. Neuwahl and co. would prevent ~2.4 million cases for the country at a cost of ~$68,000 per prevented case.

  • Regarding methodology, the authors used the CDC/RTI Diabetes Cost-Effectiveness Model to estimate the costs and efficacy of the National DPP and the three whole-population interventions. From these estimates, they mapped the cost of an intervention combination against the efficacy of the intervention(s) in reducing new case of type 2 diabetes, creating a useful tool for local, state, and national prevention work. According to Mr. Neuwahl, the chart of cost per person vs. percentage of diabetes cases prevented is an incredibly useful tool for determining the lowest cost intervention at all levels of 10-year diabetes prevention goals. Researchers and public health workers can start with the prevention goal they aim to achieve and find the intervention combinations that would roughly achieve that goal; alternatively, they can approximate what prevention goal is achievable within a given budget. Since the y-axis presents cost per person (not per at-risk person), the tool can be scaled for local, state or national level goals.

  • Mr. Neuwahl emphasized that these are estimates and more research is needed to investigate the cost and effectiveness of whole population-based diabetes prevention interventions. There is limited research on the effectiveness of whole population-based interventions, which hindered Dr. Neuwahl and co-researchers’ ability to predict the cost and effectiveness of whole population interventions. The main whole population-based intervention that has been study is the soda tax, which has been shown to be effective. For more on whole-population interventions, see one of our favorite talks from WCPD 2018.

  • Mr. Neuwahl added that the DPP has been shown to be an effective population-specific intervention, and DPP access could be expanded by delivering the intervention virtually. However, improvements in the virtual DPP program’s engagement and long-term impacts might be needed. A recent Kaiser Permanente study shows that at 12 months, those using virtual DPP do not maintain their weight loss (while those who engaged in the in-person program do) and that only 46% of those in the virtual arm completed at least 4 of the 16 sessions.

36. Barbara Davis Center’s PANTHER Project Provides Targeted Education to Help Patients Onboard with MiniMed 670G and Control-IQ Hybrid Closed Loop Systems

The audience-favorite Dr. Laurel Messer (Barbara Davis Center) presented on the PANTHER Project: Practical Advanced THERapies for Diabetes, a program addressing concerns and barriers with automated insulin delivery (AID) adoption. Despite huge advances in AID in the last few years, Dr. Messer highlighted a variety of barriers that prevent adoption, most notably, issues with the infusion set. According to Dr. Messer, in nearly all AID trials, the number one reason patients experience hyperglycemia or DKA is because of failures with infusion sets. Beyond infusion set issues, other barriers vary by age, further complicating the problem. At a similar talk at ISPAD 2019, Dr. Messer shared data identifying the top barriers to diabetes tech use in adolescents: hassle of wearing devices all of the time (38%), dislike having devices on the body (33%), dislike how devices look on the body (29%), nervousness that the device won’t work (25%), and not wanting to spend more time managing diabetes (20%). During today’s presentation, Dr. Messer walked through some of the experiences and successes seen onboarding patients at BDC in the PANTHER project with MiniMed 670G and Control-IQ.

  • MiniMed 670G: Patients (n=72) interested in the MiniMed 670G were first trained on using Manual Mode. Trainings were in-person for 2-3 hours and happened in groups of families to promote peer support. Five to seven days after this training, patients in smaller groups of families were given ~1-2 hours of Auto Mode training to reinforce conventional insulin pump and CGM use on the new system. Three follow-up phone calls in the first four weeks after training were done to assess system use, make insulin adjustments, and provide targeted re-education. Overall, patient engagement in these classes was high. Following perfect retention in the introduction class and the video conference, 92%, 81%, and 53% of participants responded to follow-up calls one, two, and three. Dr. Messer shared that a variety of changes were made during these sessions, with 75% of participants increasing their insulin: carbohydrate ratio and 44% altering active insulin time. Topics ranged from pre-meal blousing (65%), insulin correction doses (48%), and addressing system alerts such as alarms (45%).

  • Control-IQ: Participants (n=107; A1c: 7.5%) underwent a different process for onboarding as individuals in this program had already downloaded Control-IQ and were ready to start using it. As a result, the BDC team decided to work with the patient to pick an ideal day for starting, scheduling a follow-up call with a RN/DCES after two weeks of starting, and download their pump data. The PANTHER team created four metrics and benchmarks to assess device success in a week-long period: (i) time using Control-IQ (>5 days per week); (ii) time using the CGM (>5 day per weeks); (iii) Time in Range (>60%); and (iv) time spent <70 mg/dl (<5%). Median follow-up time for the call was 18 days, slightly more than anticipated, but ~64% of participants (n=68) met all four benchmarks. Of the 18 of 39 patients that completed an addition second follow-up call, nine ended up meeting all four benchmarks, with mean Time in Range between calls one and two increasing a remarkable 12.5% (baseline not provided). Dr. Messer noted that these results demonstrate active engagement coupled with education can promote technology uptake and improved glycemic outcomes.

 

 

37. Geisinger’s Fresh Food Farmacy Food Insecurity Intervention Drives 2% Reduction in A1c (Baseline >9%), 49% Drop in Hospital Admissions, and 13% Drop in ER Visits

Ms. Michelle Passaretti (Geisinger Health System) presented very positive data on the Fresh Food Farmacy initiative for reducing food insecurity among people with diabetes showing an A1c drop of 2% for participants with baseline A1c >9%. The initiative was developed in partnership between Central Pennsylvania Food Bank and Geisinger Health, an 11-hospital, 250+ clinic health system in Pennsylvania – we first reported on the results in early 2018 through Dr. David Feinberg’s talk at JP Morgan (he now runs Google Health). The Fresh Food Farmacy program was developed to meet the health needs of food insecure patients with diabetes in Pennsylvania and has established centers in Shamokin, Scranton, and Lewistown, providing 482,219 meals since program implementation. Fresh Food Farmacy was designed with 5 basic elements: (i) identification of food insecure individuals; (ii) food as medicine (providing only health options, i.e. fruit, vegetables, lean meats, grains, etc.) to supply 10 meals/week for the patient’s entire household; (iii) education and clinical support with meal planning, recipes, and lifestyle change support from clinicians and dieticians; (iv) care beyond health including transportation, housing, and food stamp programs; (v) and community partnerships with local grocery stores and community health assistants. Ms. Passaretti made sure to highlight participation in Fresh Food Farmacy was not a diet, but a lifestyle change, and that support for the patient’s entire household is necessary for success.

  • Impressively, participants with a baseline A1c >9% in the Fresh Food Farmacy program saw a 2% reduction in A1c (baseline and sample size not given). “Glucose measures” (e.g., fasting glucose) were reduced by 27%, cholesterol by 13%, LDL by 9% and triglycerides by 15% after one year in the program. Additionally, Fresh Food Farmacy also led to increased compliance with care plans with influenza compliance increased by 23%, annual eye exams increased by 17%, and annual foot exams increased by 33%.

  • Compared to eligible individuals who did not participate vs. participants, Fresh Food Farmacy participants saw 49% lower hospital admissions rates, 13% reduction in emergency department visits, 27% more primary care visits, and 14% more endocrinologist visits. Patient survey data also indicates significant improvements in quality of life with 31% of participants in Fresh Food Farmacy rating their overall health as very good compared with only 6% before participation. Additionally, 44% of Fresh Food Farmacy participants rate their emotional/mental health as very good compared to just 9% before participating – wow!

  • Ms. Passaretti highlighted multiple patient success stories from Fresh Food Farmacy. In one of the most memorable success stories we’ve heard at all of ADA, Ms. Passaretti spoke about a woman who started with an A1c of ~9.5%, but after losing both her job and secure housing saw her A1c spike to 19.1% (!); with the assistance of her coordinated care team she was able to find housing and take a more active role in her health reducing her A1c to 7.5% after just seven months later.

  • During Q&A, Ms. Passaretti answered questions about how Fresh Food Farmacy has adapted in the midst of the COVID-19 pandemic explaining her team was quick to develop an app for patients to order food with options for both home delivery and curb-side pick-up to accommodate patients who face transportation challenges. Ms. Passaretti said she expects these efforts to continue moving forward.

  • Near the beginning of her presentation, Ms. Passaretti stated a few statistics that are striking about so many needs of people in poor health systems. We had seen these firsthand during our visit to Geisinger in 2018 (“Food as Medicine? When the Key to a Lower A1c is Access to Healthy Food”) and were so happy to see Geisinger take the platform at ADA to report these:

    • For people with A1c 6.5%-9%, about one in five are food insecure;

    • For those with A1c >9%, this rises to one in four.

    • These rates compare to about one in eight people being food insecure in the broader US population.

  • Of course, food insecurity has an especially outsized effect on people with chronic conditions, which Ms. Passaretti characterized as “vicious” and “bidirectional.”

 

38. Cleveland Clinic’s CGM Shared Medical Appointments Reduce A1c by ~0.8% (Baseline: 8.4%-9.1%); New DATAA (Download, Assess, Time in Range, Ares to Improve, Action Plan) Model for CGM Interpretation

The Cleveland Clinic’s Dr. Diana Isaacs outlined her practice’s approach to shared medical appointments with CGM, based on a group-based care delivery model that encourages peer support, provider oversight, and discussion when interpreting CGM results. The shared medical appointments appointments consist of two parts with 4-6 patients in up to ten classes per month. Diabetes care and education specialists (DCESs, f.k.a. CDEs), nurses, dietitians, and pharmacists are all involved in promoting conversation and discussion around glucose patterns and trends, identifying behavioral health changes, and making medication dose adjustments. In the first part of the appointment, which involves 60 minutes of class times, Dr. Isaacs shared that the “do’s and don’ts” of CGM are discussed. Discussions with patients are centered around how to wear the device and maintain in addition to logging food, activity, and medications. Patients collaborate to set blood glucose range goals, identify ideal system alerts, and determine ways of avoiding hypoglycemia and hyperglycemia. In part two of the appointment, which can go up to 90 minutes, patients download devices and remove sensors while also discussing helpful (“Bright Spots”) and harmful (“Landmines”) in their ambulatory glucose profile. Dr. Isaacs shared that this phase of the appointment is where the majority of the learning curve is established as patients assess how expectations of their CGM were met, determine how specific behavioral patterns correlates to blood glucose trends, how to interpret metrics (e.g., Time in Range, %CV, time <70 mg/dl). During these sessions, significant focus is placed on the day during which Time in Range is the highest to positively reinforce activities and behavior which appear to have the highest impact on blood sugar. The valued peer support CGM shared medical appointments provide at the Cleveland Clinic is simultaneously rooted in positive clinical outcomes. While the specific timeframe was not mentioned, both types 1 (n=45; baseline A1c: 9.1%) and 2 patients (n=119; A1c: 8.6%) on average have seen 0.8% reductions in their A1c levels. Qualitative data also illustrates that participants touch on a variety of diabetes care practices during these discussions. Among a subset of the same participants (n=132), 57% reported changing nutrition goals,24% self-management behaviors, 23% medication adjustments, and 20% on increasing physical activity.

  • In addition to CGM SMAs, Dr. Isaacs explained “DATAA (Download, Assess, Time in Range, Ares to Improve, Action Plan),” a new model currently in press for publication on how to most effectively review CGM data. As a whole, the five-step model encourages HCPs to help patients understand specific metrics, components of the ambulatory glucose profile, Time in Range, opportunities for improvement using self-care practices, and a concrete action plan that patients actively implement. Dr. Isaacs emphasized that the overarching goal of this model is to put a positive spin on data, highlighting that the numbers exist not for judgement but for growth and learning.

    • To illustrate this in action, Dr. Isaacs presented the case of Lisa, a 68-year old type 2 patient (A1c: 12.3%), who had a Time in Range of 1% and regular episodes of hypoglycemia. During a 1:1 appointment with Lisa following CGM onboarding, several clear patterns emerged through the DATAA model. For example, while “assessing safety,” Dr. Isaacs learned that Lisa had stopped using metformin because of her hypoglycemic events and would also go low normally after a single 30-unit injection of insulin lispro despite a full meal. During the “areas to improve” phase of the model, Lisa mentioned that she had actually stopped monitoring her glucose at home because she did not enjoy seeing her numbers and that she felt “like a failure.” To best assist her, Dr. Isaacs provided a range of resources as part of a new action plan, including counseling on metformin, referral to a diabetes care and education specialist, decreasing lispro doses to 15 units with meals, and advising Lisa to check her glucose levels at least 4x/day. Six months later, Lisa had decreased her A1c to 7.2% and improved her Time in Range up to 72%, had rare episodes of hypoglycemia, and reduced her BMI from 34 kg/m2 to 32 kg/m2. She continued to meet once monthly with her CDCES and to increase physical activity, joined silver-sneakers and played pickle ball 4 days/week.

 

39. Predicting In-Patient Hypoglycemia with Machine Learning: Model’s Top 10% Highest-Risk Patients Make up Half of All Hypo Events

Dr. Michael Fralick (University of Toronto) presented promising data around using machine learning techniques to predict hypoglycemia events in the hospital. Dr. Fralick began his talk by outlining the need for such a tool: at St. Michael’s Hospital in Toronto, prevalence of severe hypoglycemia was ~10% in critical care patients, ~5% in internal medicine wards, and 2%-10% in surgical wards. Dr. Fralick’s models were trained on data from 2013-2017, tested on data from 2017-2018, and validated using data from 2018-2019. Three machine learning techniques, LASSO regression, gradient boosted trees, and recurrent neural network, were fed with data from medical records (e.g., drugs, orders, MD and nurse notes, patient characteristics, past glucose data, etc.). The dataset included ~8,500 internal medicine inpatients and ~8,000 cardiovascular surgery inpatients with a mean age of 68 years old and 80% prevalence of diagnosed diabetes. The dataset included ~2,400 total hypoglycemic events, which, on average, occurred during day 3 of the hospital stay.

  • All three machine learnings saw comparable performance, with areas under the curve (AUCs) in the 0.75-0.83 range for both hypo (<70 mg/dl) and severe hypo (<54 mg/dl) events. We found Dr. Fralick’s “cumulative gain chart” more intuitive than the AUC measure: as seen in the figure below, successful interventions with the top 10% of highest-risk patients (as predicted by the model) could reduce <70 mg/dl events by ~half.

  • This was a single-center study, limiting its generalizability. With AUCs of ~0.8, the models perform similar to those we’ve seen before – at Hospital Diabetes Meeting in April, Dr. Nestoras Mathioudakis (Johns Hopkins) presented results from his logistic regression model, which achieved an AUC of 0.87 with internal validation and 0.82-0.85 with external validation.

40. Minimally-Invasive Biolinq CGM Early Feasibility Studies (n=15 and n=10): Strong Correlation with YSI Values Through 7-Days; Clinical Studies “Later in 2020”

In a poster, San Diego-based CGM company Biolinq shared some of the first data on its minimally-invasive CGM (69-LB). The company ran two small, feasibility studies: the first was a 2-day study with 15 subjects (5 without diabetes, 10 with diabetes) and the second was a 5-7 day study with 10 subjects (all with diabetes). Types of diabetes were not specified. In addition to the small study sizes, most of the data was shared graphically, making it difficult to draw meaningful conclusions. Still, the readings from the Biolinq CGM sensor showed strong correlations with YSI (R2=0.9938). Performance of the sensor was relatively consistent across three different insertion sites (right and left forearm and left upper arm) and maintained through seven-days of wear. Of note, Biolinq is preparing a “second-generation version of its microarray CGM for clinical studies later in 2020.”

  • Biolinq’s CGM sensor is small and minimally invasive, using an array of microneedles inserted into the skin. The device is also designed to be low-cost. We got a look at the sensor at JDRF’s Mission Summit (Biolinq has investment from JDRF’s T1D Fund) and the device was very attractive with a slim form factor (about the size of a quarter and not much thicker). Biolinq is aiming to get their CGM cleared as an iCGM.