DTM 2019 (Diabetes Technology Meeting)

November 14-16, 2019; Rockville, MD; Day #2 Highlights - Draft

Executive Highlights

  • Friday was a busy day in Rockville, MD, as DTM 2019 ran full steam ahead! See below for our top highlights from sessions on automated insulin delivery, regulatory, and CGM and time-in-range.

  • Highly anticipated data from the ten-month (!) Project Nightlight trial headlined in automated insulin delivery, showing an impressive Time in Range benefit of +2.6 hours/day with Control-IQ vs. sensor-augmented pump (SAP). The unique study (n=80) tested study participants across four eight-week phases: sensor-augmented pump, evening/night-only closed loop (twice), and 24/7 closed loop. Time-in-range was 70% during 24/7 closed loop phases compared to 59% during SAP phases; time below 70 mg/dl was ~2% during 24/7 closed loop compared to 4% with SAP. The study also showed significant glycemic and patient satisfaction improvements with the pump-embedded Control-IQ algorithm with Dexcom G6, compared to the research platform with the algorithm running on a smartphone with Dexcom G4. These data are a big win for Tandem and Dexcom, aligning nicely with the pivotal data (ADA 2019, NEJM).

  • We heard from six (!) FDA speakers throughout the day, touching on the fast-moving regulatory environment. Dr. Courtney Lias called for the development of CGM-specific decision support tools (e.g., bolus calculators) and discussed needed research in the time-in-range movement. Ms. Naomi Schwartz discussed the FDA’s stance on cybersecurity issues (“we’d prefer all devices … have the ability to be updated.”). We also heard Mr. Bakul Patel’s update on PreCert and Dr. Yiduo Wu on using real-world evidence as part of a regulatory submission.

  • CGM and Time in Range dominated much of the afternoon discussion, headlined by a powerhouse panel on expanding sensor uptake with Drs. Roy Beck (Jaeb Center), Bruce Buckingham (Stanford), David Price (Dexcom), Francine Kaufman (Senseonics), Andreas Stuhr (Ascensia), and Robert Vigersky (Medtronic). Dr. Lutz Heinemann gave a more critical look at Time in Range, asking whether its growing acceptance was being driven by “evidence or eminence?” Dr. Boris Kovatchev presented a promising new mathematical model linking Time in Range with A1c (correlations >0.9), while Dr. Richard Bergenstal added to the lexicon with his “Texas two-step” approach to CGM analysis (“slow, slow, quick, quick”): both (i) long-term, retrospective review; and (ii) short-term, corrective action.

Friday was a busy day at the Bethesda North Marriott, with highlights in automated insulin delivery (AID), regulatory, and CGM and Time in Range (TIR)!

Day #1 Highlights - Novo Nordisk durable smart pens to launch 2Q20, reusable attachment in 2021; Dexcom G6 modified algorithm, improved adhesive; Medtronic 14-day adhesive; Senseonics 365-Day Wear

Table of Contents 

Automated Insulin Delivery (AID) Highlights

1. Project Nightlight Trial: +2.6 Hours/Day TIR with Control-IQ vs. SAP, Time <70 mg/dl Halved; 93% Time in Closed Loop with Embedded Control-IQ Algorithm vs. 87% with mobile inControl Algorithm

On the heels of the NEJM publication of Tandem’s Control-IQ pivotal trial, Dr. Sue Brown (University of Virginia) presented highly anticipated results from the unique, ten-month Project Nightlight study (n=80). Participants alternated across 8-week phases of: (i) sensor-augmented pump (SAP); (ii) evening-night-only closed loop (start between dinner and sleep, stop at wake-up); and (iii) 24/7 closed loop. There were two-week washout periods in between. A1c and psychometrics were performed at baseline and during each wash-out period. Of the 80 participants, 35 used TypeZero’s inControl running on a smartphone with Dexcom G4 and Roche Spirit Combo Pump. In 2018, that was replaced with the Control-IQ algorithm embedded in Tandem’s t:slim X2 pump and Dexcom G6 (i.e., the commercial product); 41 participants used Control-IQ and the remaining four used both systems. Participants’ mean age was 42 (range: 18-69 years), with A1c of 7.4% at baseline. 80% of participants were CGM users prior to beginning the study. Time in Range improved from 59% with sensor-augmented pump (SAP) to ~68%-70% with closed loop; time <70 mg/dl was halved from 4% to ~2%. These outcomes are almost identical to the Control-IQ pivotal, where time-in-range improved from 59% to 71% – nicely confirmatory data from another long term study of the under-FDA-review product.

Results

 

SAP

Evening-Night CL

24/7 CL

Time-in-range

59%

68%

70%

Time <70 mg/dl

4%

2.3%

1.8%

Time >180 mg/dl

37%

30%

29%

  • Time-in-range during 24/7 closed loop phases was 70% vs. 59% in SAP phases (p<0.001) – an 11% difference or 2.6 hours/day. With the vast majority of time-in-range improvement coming at night, time-in-range was only slightly higher in the 24/7 closed loop phases compared to the evening-night closed loop phases. (Time <54 or >250 was not reported, though presumably will be when this is published.)

  • Time below 70 mg/dl was cut in half during the 24/7 closed loop phases (1.8%) compared to SAP phases (4%; p<0.001). Evening-night closed loop showed a similar, but slightly lower, improvement in time <70 mg/dl.

  • A1c was around 7%-7.1% for both evening-night closed loop and 24/7 closed loop phases. Depending on order of study phases, one group recorded a mean A1c of 7.6% at baseline and 7.3% during SAP, while the other group recorded a mean A1c of 7.2% at baseline and 7.1% during SAP. In other words, A1c appears to have improved between 0.1%-0.3%, ending up around 7% with closed loop – similar to the Control-IQ pivotal, and not surprising considering the quite-low starting A1c at baseline. It’s unclear whether these differences were statistically significant.

  • Greater improvements in time-in-range were seen for the Control-IQ subgroup (+13%; 3.1 hours/day) on the embedded Tandem/Dexcom G6 system compared to the mobile inControl subgroup (+7.8%; 1.9 hours/day; p<0.01). This was an exploratory analysis, but a very cool one! Similarly, time >180 mg/dl was reduced significantly more in the Control-IQ group (-10.9%) vs. the inControl group (-5.4%), p<0.01. Lastly, there were no significant differences in time <70 mg/dl improvement.

  • Closed loop use for the Control-IQ (t:slim X2) subgroup was strikingly high at 93%, compared to 87% for the inControl (smartphone) subgroup. Median time in closed loop with Control-IQ was consistently high across all participants, ranging from 86% to 98%. Time in closed loop ranged from 59% to 97% for the inControl participants – far more variable. It’s not surprising that participants spent more time in closed loop with the embedded Control-IQ algorithm – the G6/t:slim X2 commercial product is more reliable than the precursor research platform (smartphone with inControl algorithm, Roche pump, Dexcom G4/G5). It will be interesting to compare outcomes once more smartphone-focused systems come out – e.g., algorithm on smartphone vs. algorithm embedded in pump. Both approaches have pros and cons.

 

Time <70 mg/dl

Time-in-range

Time >180 mg/dl

inControl group, SAP

4.3%

60%

36%

inControl group, 24/7

1.9%

68%

30%

inControl difference

-2.4%; -35 min/day

+8%; +112 min/day

-6%; -78 min/day

Control-IQ group, SAP

3.9%

59%

37%

Control-IQ group, 24/7

1.9%

72%

27%

Control-IQ difference

-2%; -29 min/day

+13%; +186 min/day

-10%; -157 min/day

p-value

p=0.45

p<0.01

p<0.01

  • Preliminary data from a technology acceptance survey showed strong patient preference for the embedded Control-IQ system. “Ease of use” was rated 4.6/5 for the embedded system vs. 3.8/5 for the mobile-based inControl system. The embedded system was rated 4.4 for “usefulness” and 4.1 for “trust,” compared to 3.9 for both “usefulness” and “trust” for the mobile-based system. Lastly, based on structured interviews with participants, Control-IQ users rated probability of long-term use 5/5 (“Extremely Likely”), compared to 3.3 for the inControl system users. Structured interviews also revealed that participants favored 24/7 use of closed loop over evening-night-only use. We believe this trial was envisioned back when overnight-only closed-loop was more seriously considered as a first commercial product; the field has since settled on 24/7 closed loop as the default.

    • The technology acceptance data mirror the results from Control-IQ’s pivotal trial (ADA 2019). Users rated Control-IQ ease of use 4.7/5, usefulness 4.6/5, trust 4.5/5, and desire to continue using was an impressive 4.8/5.

  • In total, only one serious adverse glycemic event was recorded during closed loop use: one participant experienced a severe hypoglycemic event (requiring third-party assistance) “following physical activity and delay of hypo treatment.” (This is a good reminder for the field – AID makes insulin therapy safer, but not zero-risk. Patients may also feel safer generally, which could make them less likely to move quickly on problems that deserve fast fixes, like severe hypo brought on by exercise.) Four severe hypoglycemic events were reported during open-loop use. Control-IQ was suspended for up to four weeks in eight participants in March (Tandem patched a bug through the Device Updater), though there was no impact on outcomes.

2. Dr. Boris Kovatchev Compares iDCL1 and iDCL3: Form Factor and No-Calibrations Translate to Improved Glycemic Outcomes; Calls for Looser Inclusion Criteria in Closed Loop Studies

In an overview of recent closed loop studies, Dr. Boris Kovatchev (University of Virginia) compared results from iDCL protocol 1 (smartphone-embedded inControl algorithm, Roche Spirit Combo pump, Dexcom G4 or G5) and iDCL protocol 3 (Control-IQ algorithm embedded in Tandem t:slim X2, Dexcom G6; i.e., the pivotal trial we saw at ADA). As stated by Dr. Kovatchev, direct comparisons between the two trials have validity, because both studies’ control groups received the same treatment (sensor-augmented pump). Control-IQ/G6 delivered an 11% absolute increase in Time in Range compared to SAP vs. a ~5% improvement seen with inControl running on the phone. Similarly, time >180 mg/dl was reduced by 10% (2.4 hours) with Control-IQ vs. 3% (~0.7 hours) with inControl. Differences in time below 70 mg/dl were not significantly different. Given that Control-IQ and inControl are the same algorithm, the results suggest embedding the control algorithm into the pump drives higher time in closed loop (i.e., less communication failure points, less stuff to carry) and using the no-cal Dexcom G6 produced significant improvements in glycemic outcomes.

 

inControl (n=125)

Mobile System

Control-IQ (n=168)

Embedded in Pump

p-value

Time <70 mg/dl

-1.7%

-0.9%

n.s.

Time in Range

+5%

+11%

p<0.01

Time >180 mg/dl

-3%

-10%

p<0.01

Mean glucose

-2 mg/dl

-13 mg/dl

p=0.01

  • Dr. Kovatchev also called for looser inclusion criteria for closed loop studies, particularly when using time-in-range as an outcome. Dr. Kovatchev referred to the iDCL 3 (Control-IQ pivotal) trial, which had no entry restrictions on A1c and showed Control-IQ delivered improved time-in-range across all baseline A1cs. Dr. Kovatchev noted how Time in Range varied in the intervention group by baseline A1c from 85% for baseline A1c ≤6.5% to 60% for baseline A1c ≥8.1%. By setting tight inclusion criteria (e.g., baseline A1c ≤8%), a closed-loop study can show very high time-in-range. He recommended that all studies have a chart like the one below, to demonstrate how baseline A1c affected Time in Range outcomes. We love the idea that AID can be a great equalizer – helping people improve Time in Range regardless of the starting baseline.

  • A feasibility study for Control-IQ in 2-6 years olds has completed. The study was conducted in 15 participants at UVA, Barbara Davis, and Stanford with a 48-hour supervised hotel stay and training with 72-hours of home use. The French FreeLife Kid AP study with 120 participants ages 6-13 reported partial results at ADA 2019. Lastly, a pivotal for children ages 6-14 for Control-IQ has finished recruiting (ClinicalTrials.gov) and is on track for FDA submission in 1Q20.

Regulatory Highlights

1. FDA’s Dr. Courtney Lias Calls for CGM-Specific Decision Support Tools; Says 5% Time-in-Range Improvement Still Needs to be Demonstrated to be Meaningful

Closing out a session on Time in Range, FDA’s Dr. Courtney Lias expressed her concerns about using CGM point values (ignoring trend arrow information), especially when using dose calculators that are designed for BGM users. While the value of CGM is undeniable, Dr. Lias noted that many existing tools are designed for use with BGMs and may or may not be appropriate for direct use with CGM. In particular, “emerging data” suggest that using traditional insulin bolus calculators with CGM values may put patients at risk, due to slightly less accuracy in CGM point values. In order to manage these limitations, Dr. Lias called for the development of CGM-based tools that are able to utilize the powerful data provided by CGMs, including trend arrows. This is of course a tricky regulatory issue, since: (i) many BGMs that people routinely use to dose bolus insulin are less accurate than CGMs (JDST 2016); (ii) real-world accuracy of BGMs can be much lower than studied accuracy, as many don’t wash hands (real-world CGM accuracy is also lower than studied accuracy); and (iii) many CGM users in real-world use already take their CGM value and use it for bolus calculation (though would hopefully consider the trend and other info, rather than just populating the calculator with a single point value). Between Abbott, Dexcom (TypeZero), Lilly, Novo Nordisk, Companion Medical, Bigfoot, DreaMed, and others, there should be quite a few players willing to work with the FDA to develop these tools. We’re not sure if clinical trials will be required, though we’ve been waiting a long time for CGM-based bolus calculators that integrate the new trend arrow adjustment guidelines (FreeStyle Libre, Dexcom). Regulation of CGM-based decision support tools is also still an open question; class II seems possible for non-adjunctive iCGMs, as that is where bolus calculators within pumps and apps fall (to our knowledge).

  • During her discussion on Time in Range (TIR) as a regulatory endpoint, Dr. Lias listed a number of open questions, beginning with definitions of what glucose ranges and differences are meaningful. In the new consensus targets for CGM metrics, a 5% TIR improvement is considered clinically meaningful, but Dr. Lias stated that must be demonstrated in a study. Additionally, Dr. Lias noted that different CGMs have different performance levels at various glucose concentrations. She did not specify a specific brand/model, but this is a good point for the field to remember. (One comparison that comes to mind is FreeStyle Libre vs. Dexcom – FreeStyle Libre does seem to report more time in hypoglycemia than G5; see poster at EASD 2019.)

  • To begin her presentation, Dr. Lias provided a brief update on a recent reorganization of CDRH. The changes are designed, in part, to centralize the review of diabetes devices, but will not change the regulatory review process. Most notably, the change will bring decision support and insulin pumps into the same division as that of glucose monitors, the Division of Chemistry and Toxicology. We view that as very good news! Insulin pens will still be led in the hospital group, though the diabetes group will be consulted.

2. Ms. Naomi Schwartz: FDA Would Prefer All Devices Coming in Now Have the Option to be Updated

FDA In Vitro Diagnostics and Radiological Health scientific reviewer Ms. Naomi Schwartz expressed the Agency’s preference that cybersecurity issues be disclosed in coordination with the FDA and that “all devices that are coming in now have the ability to be updated.” The Agency has put considerable thought and effort into thinking cybersecurity issues, which has traditionally not been part of the “safety and effectiveness” mandate. During a Patient Engagement Advisory Committee meeting in September, it was clear that automated insulin delivery is one of the biggest areas the FDA is considering with regards to cybersecurity. Highlighting the difficulty in cybersecurity regulations, Ms. Schwartz noted that estimating the likelihood of a cyber-attack is nearly impossible and that with increasing connectivity and interoperability, a single attack may be able to affect large swaths of users. Additionally, she pointed out that while a device’s features are “positive,” i.e., things the device must be able to do, device security is “negative” and “infinite,” i.e., things the device must not do. Lastly, manufacturers have traditionally had a “passive” approach to cybersecurity, not patching security vulnerabilities until the next-generation product (the example that comes to mind is Medtronic pumps). This 4-5 year updating process was no longer safe or practical in an era where security vulnerabilities are discovered all the time – just last month, the FDA put out a safety communication regarding the “URGENT/11” security vulnerabilities. Manufacturers need to make devices that are updateable, and quickly updateable, e.g., “we can’t take six months for updates to be pushed to hundreds of thousands of devices.” This will soon be true with all insulin pumps, once Medtronic gets the MiniMed 780G out (Tandem’s t:slim X2 is the leader here, and we’d assume Insulet’s Omnipod Dash could be updated over Bluetooth or WiFi).

  • Ms. Schwartz highlighted the disclosure of the Animas Ping vulnerability (2016) as a good example of coordinated efforts by J&J and the FDA. The vulnerability was discussed with the FDA “early and often,” allowing the groups to assess risk and share mitigation strategies early on. Renowned diabetes hacker Mr. Jay Radcliffe, who actually discovered the Animas Ping vulnerability, has also credited that effort as an example of how manufacturers have improved at dealing with cybersecurity communications. Ms. Schwartz also brought up Abbott/St. Jude’s pacemaker security vulnerability communications (2017) as an example of poor coordination that resulted in delayed assessment and confusion.

    • In the FDA’s September meeting on cybersecurity, the issue of disclosure timing was a point of disagreement among Committee members. While some found it “paternalistic” to withhold information from patients, others maintained that patients shouldn’t be unduly burdened, especially when the scale of the risk is unknown or when mitigation steps are not available. The Medtronic pump cybersecurity issue is a case in point – it was not communicated by FDA and Medtronic until eight years after the issue was discovered. Ultimately, it’s clear that this is a multi-dimensional issue, but any communication should always be carefully considered with patient perspectives first – including numbers on the probable risk of harm and consideration of the risks of discontinuing a therapy.

  • Ms. Schwartz emphasized a multi-stakeholder approach to creation of regulatory guidelines, involving the FDA (and other international regulatory agencies), manufacturers, clinicians, and patients. We think patients could play a role in the regulatory process. While some aspects of manufacturers’ devices and algorithms may need to be hidden for commercial reasons, the paradigm of “security by obscurity” is no longer practical. By making the pre-market review of a device or algorithm more open, allowing the public to examine a product, any future issues and confusion could be reduced. We’ve heard Stanford’s Dr. Rayhan Lal express a similar sentiment towards the safety of DIY systems, which are open-source and public for anyone to examine.

  • Following the FDA’s 2018 premarket draft guidance, the Agency is working with MITRE to develop a common vulnerability scoring system (CVSS) to help assess future vulnerabilities. The International Medical Device Regulators Forum has also issued a new cybersecurity work item, which is being co-led by the FDA and Health Canada. The FDA is also working with the Department of Commerce and Patient Engagement Advisory Committee.

3. FDA’s Pre-Certification Working Model for Digital Health is Feasible; “Time Will Tell” Whether it Needs to be Adapted to be Used Prospectively

Mr. Bakul Patel (FDA) once again presented his vision for the FDA’s Pre-Certification model for modernizing digital health software regulation. Early on, Mr. Patel highlighted the need for a new approach to regulation: the FDA currently receives ~4,000 510(k) submissions per year, but as new digital health and software products continue to develop, the number of submissions will grow exponentially. Additionally, given growing concerns about cybersecurity (see above), the need for a more iterative and continuous approach to regulation is obvious – i.e., the hallmark of great software is not “perfection” at launch, but continuous improvement and iteration based on real-world data. The FDA’s Precertification program (“PreCert”) was developed in 2017 as a new organization-based approach to regulating software as a medical device, sort of like “TSA Pre-Check” – once pre-certified, companies will get a streamlined digital health software review. Nine pilot participants are in the program, seven of whom work in diabetes in some way. A working model for PreCert was created in 2018 (see our coverage of the meeting) and the feasibility of the model has been tested, applying the model in parallel and retrospectively with submissions that have gone through the traditional regulatory process. According to a mid-year update from the FDA, the working model has performed well, demonstrating that the company “excellence appraisals” (i.e., are you a great software company? How good are your processes?) and “streamlined review” were sufficient to make regulatory decisions. Mr. Patel stated today that feasibility testing will continue through “this year and a little next year” with alpha-testing of the program starting “soon,” to test the PreCert approach with submissions from the program’s nine pilot participants – this could have the most immediate diabetes implications for Tidepool Loop, the automated insulin delivery iPhone app. The alpha-testing will help the FDA determine whether the PreCert program can work prospectively (“time will tell”) and whether the program is scalable.

  • “You’ve seen many updates, and that’s on purpose. The message is, ‘We don’t know all the answers for this vision to come true, but we want to get help from people and want to share how we’re progressing, so that when we actually put this program out to test, we can actually know it can actually work.’ … What we’re talking about is moving the oversight mechanisms and the information we get to different parts of the life cycle of product delivery. How can we provide benefits for the FDA and for industry and for users?” – Mr. Patel

4. Real-World Evidence Can Serve as Control Arms in Trials, Support Indication for Expansion; Evidence Must be “Fit for Purpose”; FDA Developing Standards for Real-World Data

Dr. Yiduo Wu (FDA) kicked off his presentation on the role of real-world evidence in regulatory decision making by defining the difference between real-world data and evidence: real-world evidence (RWE) is clinical evidence supporting potential benefits/risks of using a product derived from analysis of real-world data. Dr. Wu outlined many potential uses for RWE in a product’s life cycle, from product development to post-market surveillance. RWE could help inform hypothesis generation, inform trial design, serve as the control arm in a clinical trial, or support indication expansion for products. Dr. Wu also noted that sometimes real-world evidence collection is a mandatory condition of approval for products. However, to take advantage of real-world evidence, the data collected must be “fit for purpose,” which Dr. Wu defined as meaning the data must be “complete, consistent, accurate, and contain all critical data elements needed to evaluate a medical device and its claims.” To improve reliability of real-world data collection, Dr. Wu suggested manufacturers pre-specify the data elements to be collected, unambiguous definitions of those data elements, methods for aggregating and documenting data, and a timeframe for the data to be collected. To help organizations with this process, Dr. Wu shared that the FDA has been working with the CDC, NIH, CMS, device manufacturers, EHR vendors, standards developers, and others to develop standards for data and data interoperability.

  • As an example of RWE use cases, Dr. Wu noted that RWE was considered as part of the non-adjunctive indication expansion for Dexcom G5. The real-world evidence considered included comments and documentation from patients, caregivers, and providers who used the G5 off-label non-adjunctively, as well as data collected during real-world routine use. An upcoming test of real-world data could be Tidepool Loop’s FDA submission – it is using an observational, real-world, virtual study (Jaeb) to show safety. See Howard Look’s update at Diabetes Mine last week.

  • Dr. Wu provided a list of questions for manufacturers to ask when submitting RWE to the FDA: (i) were appropriate variables collected?; (ii) was the endpoint meaningful and defined in a consistent way?; (iii) was the period of data collection appropriate for the chosen endpoint(s)?; (iv) was the chosen population appropriate and representative?; and (v) was the analysis of the real-world data appropriate for the question?

CGM and Time in Range Highlights

1. Quotable Quotes from a Powerhouse Panel on Increasing CGM Use: Drs. Roy Beck, Bruce Buckingham, Francine Kaufman, David Price, Andreas Stuhr, Robert Vigersky

On CGM in Type 2s

  • “I think the use of CGM in type 2 is probably the greatest unmet need there is right now. A patient who was in [a study] came to see me about 2 years later, and I had never seen him personally. He said, ‘Dr. Vigersky, I was in your trial.’ I asked, ‘How’d it go?’ He said, ‘This [CGM] was an amazing device. I’m Italian, and I love bread. I saw what bread did to my blood sugar, so I cut out bread. I lost 30 pounds, my A1c went down from 9% to 7%, and I’ve stayed there ever since.’ To me, that was an example of the power of what this technology can do. I think it has that power in type 1, but to me, I think we can do an awful lot of good for our type 2 population.” – Dr. Vigersky

  • “At the Jaeb Center, we had the DIAMOND study which was not only a type 1 study, but also a parallel type 2 diabetes study with adults on MDI. Going into it, I didn't really have any idea whether we were going to show benefit or not with this group or whether they’d use CGM or not. It was a pretty low-touch protocol. There were a pretty minimal number of visits between 3-6 months. Like [Dr. Vigersky] said, there was not only a substantial reduction in A1c compared to the control arm, but patient satisfaction was incredibly high. The median amount of CGM use at six months was about 90%. Patients really adhered to it and demonstrated high satisfaction and benefit. I think we’ve just kind of touched the surface in type 2.” – Dr. Beck

  • “We don’t know the exact frequency or how long to do intermittent monitoring. It may be just a couple of weeks every 3 or 4 months, and you might get the desired effect. I call on someone in the audience to do this study so that we can get what the frequency of this is and the cost-effectiveness.” – Dr. Vigersky

On Expanding CGM Uptake

  • “It’s not any longer about proving that the technology works, but about how we can get more people to adopt this technology we know that works. That’s why I’m so excited about it. There are probably many roadblocks, but the one I like to focus on is the value people see this technology provides…We did market research and asked people before and after what they expected to achieve when adopting this technology. Most people originally said better A1c and better Time in Range. After half a year, half of the people said their expectations were not met – was this because the technology didn’t work, or maybe because they don’t know how to use it in the most effective way? I believe that education on how to use it and what to do with the information would be one area to address. Clearly, access is a major roadblock, but even in countries where there is access and limitations are not a problem, there is still not a majority of people with type 1 using it...We’ve heard in the past about the annoyance of false alarms or being overwhelmed by the data, but people who have managed diabetes without CGM need to unlearn things and need to learn new things in order to get the benefit out of CGM.” – Dr. Stuhr

  • “Earlier generation CGMs expectations’ were not at the right level. Providers didn’t give [the right expectations] to patients, and there were false expectations. In general, now, a big issue is awareness among adults. In this country, the majority of adults with type 1 diabetes are being cared for in primary care settings and not endocrinologists. Either awareness of CGM is not there in primary care, or they're so busy with so many things that they don’t want to deal with it. It would perhaps be a surprise to this audience, but there are a lot of adults who may have heard of CGM but don’t know anything about it. We did a feasibility study of a direct-to-patient approach to starting CGM. In Wisconsin, we partnered with a primary care research network throughout the state that integrated primary care practices. We decided that we would try to identify adults with type 1 diabetes who had not seen an endocrinologist and see if they wanted to try CGM and do it all remotely. We sent out a letter to patients, and beyond that, we had a website they could go to with information on CGM. If they were interested, they elected to consent and gave them information. We used the Abbott FreeStyle Libre and Dexcom G6, and they select which one they wanted. We sent it to them, had a CDE do a virtual visit with them to give them some training, and then they were on their own for a bit. There was some other contact, after they used it for a little, they got some advice on how to use it in their day-to-day management. We started with 34, mostly type 1, but there were about seven type 2s. It was remarkable. Just putting it on in the first week, time-in-range went up by 2.5 hours, and that’s really without instructions about how to use it ... At the end of three months, all 34 were still using it, and actually, CGM use was about 95% over the whole time. A1c dropped from where they started at 8.3% to 7.2%. We didn’t have a control group, and this was a group naive to CGM and didn’t know much about it from the start, but it just shows the simplicity of the devices now and the lack of burden that used to be there. We’re about to embark on a much larger national study to do this.” – Dr. Beck; we might see the full data on this Jaeb/Cecilia Health pilot study (“Geek Squad”) at ATTD 2020

  • “I think that the Abbott Libre direct-to-patient advertising has really helped all the CGM companies in terms of awareness. I know people with diabetes, and they’ve asked me about what I think. They would have never heard about it before, so I actually think that what Abbott did going down that road as far as awareness of CGM was a public service. I think though that there is more to go for awareness.” – Dr. Beck

  • “[Dexcom is] doing some [direct-to-consumer advertising], and as we are able to ramp our production, we’ll do a lot more. Another real barrier is access. People who are poor can’t access CGM, and that’s really a problem. To overcome access issues, people need to meet with payers and present clinical and health economics. There needs to be work with advocacy because we need to be able to get it and particularly poor people.” – Dr. Price

On Remote Monitoring

  • If Rayhan Lal is here, he had a new-onset on CGM for a few months and got a call at ~1 AM. The mother said the CGM stopped working, and that she didn’t know what [her son’s] glucose was. She asked, “what should I do?” He suggested, ‘take a fingerstick,’ and she said, ‘How barbaric!’ It’s totally changed the way that people manage their diabetes.” – Dr. Buckingham; FFL 2019 was the first time we heard this story!

  • “We asked who people wanted to share the data with, and the pediatric population was pretty clear. [Dr. Buckingham] is correct about the adolescents as there’s not much accounting for what an adolescent may or may not want to do. However, the adults really surprised me. They didn’t want to share, for the most part, with anybody other than either their HCP, which I thought was quite generous, or an emergency medical responder. The person they didn’t want to share it with the most was their spouse.” – Dr. Kaufman

  •  “We're putting people recently diagnosed on CGM from the beginning, and people are just hooked on it. It’s how they get sleep at night, having alarms when something goes low. It’s not been a problem. With adolescents, they sometimes need a little bit of space, so when we start them up, we always ask for terms of engagement like when someone wants to be notified and allowing them to set the limits.” – Dr. Buckingham

On CGMs in Inpatient Care

  • “We need the right kind of clinical trial data to show that it performs to whatever claim it is that we are trying to make, whether it’s in the ICU or general wards, so it’s going to be a little while. Hopefully, it will happen soon because the need is there.” – Dr. Price; see a review of Dexcom’s hospital studies here

  • Wouldn’t it be nice if the patient coming into the hospital with a CGM could keep it? I think one of the bigger problems is right in the practice guidelines, getting those with diabetes who we know need CGM are allowed to continue it and maybe extend that to closed loop. I think we need to get there first before we can start putting it on people who are not using it.” – Dr. Kaufman; read Adam’s experience Looping and using CGM in the hospital here

  • “This is a little bit cynical, but I think that it’ll be ubiquitous when hospital administrators recognize it improves outcomes and reduces length of stay … We have to show that this device would at least be offsetting that additional cost, but hopefully, reducing overall costs.” – Dr. Vigersky

2. Dr. Lutz Heinemann’s Critical Appraisal of TIR: Is Its Growing Acceptance Being Driven by “Evidence or Eminence?”; TIR is a Meaningful Supplement to A1c, but Not a Replacement in Clinical Practice

Dr. Lutz Heinemann (Science and Co.) presented a critical look at time-in-range (TIR), arguing that while TIR is a valuable addition to A1c in clinical care settings, it is not a replacement for it. Noting the quick, ~six-month turnaround for consensus guidelines around CGM metrics to be developed and then incorporated in ADA Standards of Care, Dr. Heinemann asked the rhetorical question, “Was this driven by evidence or eminence [of the people on the consensus committee]?” Presenting an argument outlined in Dr. Heinemann’s recent publication in JDST, Dr. Heinemann emphasized the lack of a reference standard or quality control for TIR. As TIR is a parameter calculated from data from CGMs, biases in CGM values can significantly affect TIR values. Underscoring this point, Dr. Heinemann showed data presented on a poster at EASD 2019, finding that FreeStyle Libre reported twice as much time below 70 mg/dl as the Dexcom G5 when worn on the same 24 subjects. Additionally, Dr. Heinemann pointed at the relatively weak evidence linking TIR with long-term complications and expressed skepticism that a large-scale study like DCCT could ever be done again. Still, he conceded that smaller, less-convincing studies have already been done linking TIR with long-term complications (e.g., Beck et al., Diabetes Care 2018).

  • Dr. Heinemann admitted that time-in-range was still an incredibly valuable metric, particularly for use as an education tool. As TIR is much more reflective of day-to-day glucose variations, it is a very powerful tool to help patients visualize and understand their glucose patterns and decision making. Additionally, Dr. Heinemann noted several physiological factors that can affect A1c, which TIR is not affected by. For this reason, he highlighted a particularly strong use case for TIR in pregnant women.

 

Table taken from Dr. Heinemann’s publication in JDST.

3. Dr. Boris Kovatchev: >0.9 Correlation Between A1c and TIR Using a Differential Equation Model

University of Virginia’s Dr. Boris Kovatchev presented a new differential equation model correlating A1c with time-in-range (TIR) that could accurately estimate A1c with a correlation coefficient of 0.93 between estimated A1c and actual lab A1c. Notably, the equation estimates A1c daily, using TIR derived from CGM daily profile – very cool. The model is based on the premise that both TIR and A1c reflect the same underlying phenomenon (i.e., blood glucose), but that interpersonal variability in glycation rates drive the discrepancy seen between two people with the same TIR and different A1cs. The differential equation he proposes contains three fixed population-based parameters (i.e., biological constants for all humans) and a fourth variable, representing individual glycation rates, that is different for each person. Using the dataset from iDCL 1 (mobile-enabled inControl, Dexcom G4/G5, Roche Spirit Combo; n=120), the three fixed population constants were calculated. Then, individuals’ A1c values from month three of iDCL 3 (Control-IQ embedded in t:slim X2, Dexcom G6; n=168) were used to calibrate the individual glycation rate variable for each study participant. The equation was used to estimate participants’ A1c daily and then compared to reference lab A1c at months 6 and 9 of iDCL 3 with impressive accuracy.

  • Estimated A1c and lab A1c values were strongly correlated (r=0.93) 3-months and 6-months after “calibration” to individuals’ glycation rates. After 3 months, 98% of estimated A1c readings were within 10% of the lab measured A1c; after 6 months, this percentage deteriorated only slightly, to 96%. For context, the traditionally used linear models correlating TIR with A1c from Drs. Roy Beck (2019) and Robert Vigersky (2019) have correlation coefficients around 0.6-0.7. Of course, Dr. Kovatchev’s differential model has a trade-off in that it requires one individualized calibration point.

    • The picture below shows the difference between Dr. Kovatchev’s model and a linear model. The blue dots represent estimated A1c and lab A1c scatter using a linear correlation, while the red dots represent the differential model. Note that the red dots are bunched much more closely around the perfectly correlated eA1c = lab A1c line.

In the differential equation above, the three population constants are represented by tau, a, and b. The individualized glycation rate parameter is represented by gamma. TIR and eA1c represent daily time-in-range and estimated A1c, respectively.

4. Dr. Richard Bergenstal Shows Off His “Texas Two Step” of CGM: Quick, Corrective Actions + Slow, Retrospective Review

The engaging Dr. Richard Bergenstal (International Diabetes Center) added another catchy aphorism to the time-in-range lexicon, dubbing the need for both (i) long-term, retrospective review of CGM data and (ii) short-term, corrective actions the “Texas Two Step” of diabetes management (i.e., “slow, slow, quick quick”). Self-admittedly, Dr. Bergenstal’s new phrase is a rework of his “slow” and “fast” CGM thinking concept, based off of Nobel Laureate economist Dr. Daniel Kahneman’s book Thinking Fast and Slow, shared at ENDO 2018 and ATTD 2019. The “quick step” of the dance refers to making in-the-moment therapeutic or behavioral changes based on real-time CGM numbers or trends – for example, adjusting postprandial insulin based on a CGM trend arrow. For the “slow step,” Dr. Bergenstal used deliberative analysis of an AGP as an example. Using AGP, clinicians can work with patients to look for patterns of hypoglycemia or hyperglycemia, in conjunction with principles from the Nine Steps to Interpreting an AGP. Overall, Dr. Bergenstal called for greater use and education surrounding both “slow” and “quick” data.

  • Dr. Bergenstal also brought back two of his most popular AGP acronyms: “MGLR” and “FNIR.” Patients should strive for an AGP that is “Flat Narrow and In-Range” and a TIR chart that has “More Green, Less Red.”

  • GMI (Glucose Management Indicator), one of the ten standardized CGM metrics for clinical care, was highlighted as a measure that is becoming “more and more important.” As a reminder, GMI is the rebranded “estimated A1c,” converting mean glucose from CGM or fingerstick readings to an A1c-like %. For clinicians, any mismatch between GMI and lab A1c can provide insights into how a patient is doing more recently – e.g., Dr. Anne Peters gave an example at her talk on moving beyond A1c at CMHC 2019, stating, “I use GMI to say to patients, ‘Your measured A1c is 7.6%, but your GMI is 6.8%, so it shows that your last two weeks were better and you’re trending in the right direction.’” A difference between GMI and A1c would also individual variation in glycation – i.e., a lab-measured A1c that is over- or under-reporting actual measured glucose. 

  • Dr. Bergenstal countered the critique he often hears that ‘patients will never [look at an AGP] over and over again’ or use trend arrows consistently with an interesting NYT article entitled “The Unexpected Joy of Repeat Experiences.” We loved this read! As behavioral psychologist Dr. Ed O’Brien states in the piece, “when an experience has many layers of information to unveil, it’s probably a good bet to repeat it” – we definitely think AGP qualifies here!  

Selected Questions and Answers

Q: On using trend arrows to adjustment insulin doses, I know a lot of patients with type 2 diabetes are now using CGM. Can people with type 2 diabetes use the recommended insulin adjustments? Or, are the amounts just recommended for type 1s?

A: I think the first honest answer is that we’re not sure. We need a ton more CGM data in people with type 2, which is just starting to be collected now. We need to run those analyses. My guess is that the current tables that are published are actually going to be pretty close, but to be honest, we need a lot more data in type 2.

Q: You’ve looked at time <70 mg/dl, but how would you define a serious hypoglycemic event?

A: We’re realizing that while we’re doing studies now, we need to collect a series of hypoglycemic datapoints, so we can figure out what best correlates with things like emergency room visits, etc. Not only do we need to study the time <54 mg/dl, but further than that – is it the duration of time in hypoglycemia? Does it have to be a prolonged period? Do you need to be <54 mg/dL for, say, two hours to have a seizure? We’re collecting all those metrics: two hours <54 mg/dL, 30 minutes <54 mg/dL, two hours <70 mg/dL, 30 minutes <70 mg/dL.

5. Addressing the “Diet Challenge” with Machine Learning: Early-Stage “DietHabit” Project Attempts to Predict Meals Eaten Based on CGM Data

PATHS-UP’s Dr. Ashutosh Sabharwal (Rice University) presented on fascinating new AI-based models to tackle the “diet challenge” – the difficult task demanded of patients with diabetes to estimate the nutritional content of food (i.e., estimating carbs, fats, and proteins) and accurately dose for it. As Dr. Sabharwal explained, the challenge is so difficult because people have to replace how they once thought about food (simple labels like “pizza”) with an entirely new way (“how many carbs does this pizza have? How much protein or fat?”). Currently, Dr. Sabharwal’s own lab is attempting to create a model, entitled “DietHabit,” that predicts what exact food has been eaten based off of CGM data. Quickly realizing this was too ambitious, the group decided to look integrate real-world food log data and generate predictions from a personalized “food bank.” As we are creatures of habit, Dr. Sabharwal’s early work suggests that people have a limited set of meals that they commonly eat. They developed a clustering model to unify inconsistent food diary wording (“pasta with chicken” grouped with “chicken pasta”) and found that most participants had fairly reliable breakfast meals. However, lunch and dinner consistency tended to depend on the person while snacks were inconsistent for almost everyone. We think this would pair really nicely with an app like UndermyFork, which does a good job of tagging meals based on photos and pairing it with CGM traces.

  • Further along, Dr. Ricardo Gutierrez-Osuna’s team at Texas A&M University recently published a pilot study in IEEE, aimed at predicting macronutrient content if given a CGM trace, creating what Dr. Sabharwal refers to as a  “Continuous Diet Monitor” using data from CGM. In the study, 15 patients with a “healthy BMI,” between 60-85 years old were provided meals with known carbohydrate, fat, and protein content after eight hours of fasting. CGM traces for nine meals with varying macronutrient ratios were measured for each patient. Although the general glucose response for carbs (larger and longer) and fats/proteins (dulled amplitude, extended duration of recovery to baseline) could be easily predicted, subject-to-subject variability was extremely high, reducing the generalizability of results.

    • More recently, the researchers then developed a machine learning model to better counteract variability. Using input from fasting glucose levels, subject body weight, and “important features regarding the glucose response shape and duration,” the neural network was able to predict carbs, fats, and protein content with correlation coefficients of 0.76, 0.57, and 0.37, respectively. Estimating macronutrient amounts in general categories (low, medium, or high) further improved accuracy of 95%, 65%, and 65%, respectively. Results from that study are expected to be published soon.

  • Moving forward, Dr. Sabharwal has a new partnership with Sansum Diabetes Research Institute in the works. The collaboration will focus on integrating CGM readings, food photography, and smartwatch outputs into a single data collection platform. The project seeks to develop strategies for personalized dietary understanding and recommendations.

  • For context, PATHS-UP (The Precise Advanced Technologies and Health Systems for Underserved Populations) is an NSF-funded Engineering Research Center started in 2017. The 10-year, $14 million center’s mission is to “develop next-generation devices for people with chronic disease and underserved populations.” Collaborators include Texas A&M, UCLA, Rice University, and Florida International University.

 

--by Ani Gururaj, Rhea Teng, Albert Cai, and Kelly Close