#DIYPS, pathways to Artificial Pancreas Systems, and diabetes technology for all

#DIYPS=on path to artificial pancreas but not limited to those using newest diabetes tech. http://bit.ly/1mMS7LA @danamlewis @scottleibrand

Like many others, we’ve been reading the latest in the New York Times about the impact of diabetes technology innovation on the cost of managing the disease – not to mention the reactions to the piece, the response to the reactions, the reactions to that, etc.

We believe there is a better way forward, and #WeAreNotWaiting to make it happen.  Innovation can happen in a low-cost way, and can be scaled to support a broad patient audience, without contributing to or requiring significantly increased healthcare costs. #DIYPS for example (check out these results) was developed by two individuals, not an organization, with the goal of solving a well-known problem with an existing FDA-approved medical device. As recounted here (from Scott) and here (from Dana), we set out to figure out a way to augment continuous glucose monitor (CGM) alerts, which aren’t loud enough to wake heavy sleepers, and to alert a loved one if the patient is not responding.

We were able to solve those problems, and include additional features such as:

  •  Real-time processing of BG, insulin on board, and carbohydrate decay
  •  Customizable alerts based on CGM data and trends
  •  Real-time predictive alerts for future high or low BG states (hours in advance)
  •  Continually updated recommendations for required insulin or carbs

While #DIYPS was invented for purposes of better using a continuous glucose monitor (CGM) and initially tailored for use with an insulin pump, what we discovered is that #DIYPS can actually be used with many types of diabetes technology. It can be utilized by those with:

  • CGM and insulin pump
  • CGM and multiple daily injections (MDI) of insulin
  • no CGM (fingerstick testing with BG meter) and insulin pump
  • no CGM (fingerstick testing with BG meter) and multiple daily injections (MDI) of insulin

We think this type of device-agnostic software/technology is critical as we work on pathways to the artificial pancreas systems (APS). While we hope APS is out on the market soon (10 years ago would’ve been nice :)), we know it will take several years to a decade. And, even when it comes out, APS will be expensive. It may not be covered by insurance. Even with insurance, people may not be able to afford it. And even if everyone could afford it, some people may prefer not to use it.

We believe technology like #DIYPS can, and must, scale to take advantage of real-time data from CGMs, insulin pumps, and any other new diabetes technology, and help patients achieve the best possible health and quality-of-life outcomes while waiting for APS systems to become available.  But at the same time, we want to build these types of tools so that anyone with any combination of diabetes tools can use them to better self-manage their own particular condition. For example, availability of bolus calculator tools is often limited to those with pumps.  #DIYPS can be used as a simple bolus calculator, with the added benefit that it can keep track of carb absorption and allow the user to calculate correction accurate boluses while still digesting a meal.  Packaged into a simple web or app interface, this would allow people to do the same type of quick data input and calculations to be able to verify/support their mental math.

While #DIYPS is a very effective prototype, we don’t expect it to be the only interface that everyone with Type 1 diabetes uses.  Rather, we hope to integrate it with projects like Tidepool that will allow anyone, with any kind of meter, pump, or CGM, to easily upload their data, and then use any number of tools like #DIYPS to interact with their own data and get both real-time and historical insights from it that will let them improve their own diabetes self-care.  However, to make this possible, we need all medical device makers to open up their devices to allow patients real-time programmatic access to their own data. (A good example – there’s no access to temp basal history on Medtronic pumps!)

We need people and companies with innovative ideas to focus on making those ideas available as device-agnostic software, not solely as a proprietary feature on a single non-interoperable medical device.  And most of all, we need everyone to focus on making the fruits of innovation available as widely as possible, even to patients without the financial resources to buy cutting-edge hardware.

After all, #DIYPS is proof that low-cost innovation can have big results.

Initial findings from #DIYPS after 100 days and comparing it to a bionic pancreas

#DIYPS + @danamlewis = as good as a bionic pancreas! Reduced avg. BG & time spent <60mg/dl. Details: http://bit.ly/1qF6qRp @scottleibrand


Recently, diaTribe published a summary of results presented by Dr. Ed Damiano at #ATTD2014 showing how their bionic pancreas closed loop artificial pancreas system (APS) improved average glucose levels and halved time spent <60 mg/dl in patients with Type 1 Diabetes (T1D), compared to when those same patients were not wearing the system.  The numbers are impressive: mean glucose was reduced from 159 mg/dl to 133 mg/dl in the Beacon Hill study (n=20 adults, 5 days data per patient, for a total of 100 bionic pancreas days).  If that were sustained over the long term and converted to an A1c value, that would represent a 0.9% improvement, from 7.1% to 6.2%. As diaTribe points out, “Those results are unheard of for any diabetes drug or device.”

Given those results, we wanted to see how the bionic pancreas results compared to the results for #DIYPS, and the results were equally impressive: 90-day eAG was better than in any of the study control groups prior to using the system (before #DIYPS =146 mg/dl) and eAG was further reduced to better than any of the bionic pancreas treatment groups (after #DIYPS = 128 mg/dl) after utilizing #DIYPS.  Time spent <60 mg/dl was already lower than for any of the bionic pancreas treatment groups (before #DIYPS = 1.2%), but was reduced still further to 0.9% with #DIYPS.

@danamlewis 30 day estimated average glucose values. Note – #DIYPS began in December.

(Note that for #DIYPS, n=1 at the moment.  These results are specific to a single highly motivated individual, who was already doing everything possible to manage T1D.)

The pre-DIYPS control condition included wearing and using both a continuous glucose monitor (CGM) and an insulin pump.  It is not clear from the diaTribe article whether the subjects in the bionic pancreas study under their “Usual Care” or “Supervised Camp Care” control conditions included the use of such technology, or whether they were using the more common methods of fingerstick meters and multiple daily injections (MDI) of insulin.  If “Usual Care” included patients on MDI, much of the benefit attributed to the bionic pancreas could be attainable using CGM+pump therapy as well.  The #DIYPS data, however, shows significant improvements even compared to CGM+pump in the control condition.

While n=1 (vs. n=20 and n=32 in the two bionic pancreas studies), the #DIYPS data shows the effects of much longer term usage of the #DIYPS system.  The total time using the system (90-100 days) was equivalent between our data and the Beacon Hill study. Also, because #DIYPS has been in use for 100 days now, we have now seen the results in actual A1c values, not just eAG-calculated Projected A1c.  The improvement from pre-DIYPS A1c to the first post-DIYPS A1c data point is consistent with the improvement shown by the calculated eAG results. (Note that we plan to validate with additional data from A1c testing to show the actual improvement in A1c attributable to #DIYPS, and hopefully validating the sustainability of using the system).

Differences between the bionic pancreas & #DIYPS

Part of what makes the bionic pancreas so promising is that it is able to dose glucagon (through a second pump) to correct low or falling BGs. Because #DIYPS uses only an existing FDA-approved CGM, relies on the patient to dose their own insulin through an FDA-approved insulin pump, and is not using glucagon, #DIYPS might not be expected to be able to prevent hypoglycemia as well as the bionic pancreas. However, our data show that #DIYPS’ predictive alarms and proactive correction suggestions allow a patient to prevent hypoglycemia (BGs <60 mg/dl) even better than the bionic pancreas can do. (#DIYPS also suggests, when possible, using temporary basal rates, which often reduces the extra carbohydrates that have to be consumed to prevent or correct a low BG).  Less-motivated patients using #DIYPS may not be able to prevent low BGs quite as effectively, but this is still an important demonstration that such improvements in control are possible before new glucagon pump technology is available on the market in the future.

Differences between bionic pancreas, artificial pancreas systems (APS), and #DIYPS

And finally, and most importantly as a distinction of the difference from the bionic pancreas, #DIYPS does not automatically dose insulin.  It is solely an alerting system (with predictive alerting and real-time calculation abilities), which relies on the user to both validate any suggested actions, and to actually dose any insulin, reduce insulin, or consume any carbohydrates required to manage their BGs.  This means #DIYPS is not an artificial pancreas system (APS), and does not provide the ability  for you to “forget about diabetes” that is such a powerful promise of true APS systems like the bionic pancreas. Yet it does reduce the overall cognitive load of diabetes (and provides additional security mechanisms such as alerting loved ones if you don’t respond to alarms over time). And, #DIYPS shows that better software and alerting can result in dramatic improvements in blood glucose levels, even without automatic dosing of insulin.

What’s next for #DIYPS

As you can tell, we are excited about the promise of #DIYPS for helping people with diabetes (PWD) manage their disease.  But, as mentioned above, all of this is currently being done in our spare time, with no funding or institutional support.  We, and a small group of like-minded individuals and non-profits scattered across the Internet, have decided that #WeAreNotWaiting for research labs and medical device companies to develop a full APS system and get it approved by the FDA (which is still probably 5 years away). Instead, we are doing what we can to make progress now.

But the next step is critical: we need to make this technology available to the people whose quality of life – and possibly even whose lives – depend on it. (This is why we originally set out to build #DIYPS – to help people wake up to overnight CGM alerts who sleep through the one-size-fit-all alarms coming from the device).  Recently we received an email from someone in Europe whose MD tells her that her severe nocturnal hypoglycemia is life threatening, and sent her on a mission to find a system that can wake her up from a severe low and contact a loved one if she doesn’t respond.  In order to help people like her, we need to begin working with researchers and doctors, and hopefully even get funding to develop #DIYPS into a scalable system that can help any PWD manage their diabetes better.

If you are someone (or know anyone) who can help with any aspect of that effort, please reach out to us on Twitter or directly by email. Otherwise, please stay tuned for more updates.

Dana Lewis and Scott Leibrand




#DIYPS and Pizza, and wondering why we judge people with diabetes for how and what they eat

What’s your reaction if you read that someone with Type 1 diabetes just ate 110 carbs worth of pizza for dinner?

Go ahead, answer the question out loud (or write it down) before continuing.

No, really. Did you say it out loud? Or at least think about it?

Now, what if you find out that their BG never spiked (only rose 10 points) and then glided along in range (80<in range<150) for the rest of the night?

Were they lucky? Was it a fluke? Or was it the way that it (eating food) actually should work?

If you’re like many of us, your initial reaction (the one we asked you to say out loud so you wouldn’t mis-remember it later) was probably something along the lines of “that isn’t very responsible”. It just *makes sense* to judge someone for eating food that is “so obviously bad” for them. But, is the food bad for them? Or is what we’re trying to say (think) that the food is likely to lead to “bad” or out of range BGs, therefore it’s not a good idea to consume (or consume so much)?

Maybe we shouldn’t be blaming people with type 1 diabetes for not eating “right” or “trying hard enough” to get the health outcomes they want (and we all want for them). Maybe we all need to start working on putting together all the technology that already exists, in a way that actually allows people with T1D to live a normal life and worry less about constantly managing their BGs. The way #DIYPS does for me.

We also need to start working on changing the intuitive attitude that the problem is a lack of “compliance” (related – read this great post from Kerri on “compliance”) with diabetes management/treatment. Instead, why don’t we all work with patients to understand what is difficult for them about managing their diabetes, and what changes we might be able to make in the processes, systems, and technology they use to make it easier and more effective to do so.

(You may be wondering where this blog post came from. It’s related to #DIYPS – I tested the system one night by eating several slices of a frozen gluten-free pizza, which while convenient is often higher carb than the already-high-carb food. And, my instinct was not to talk exactly about how much and what I ate, because I’ve experienced so many times over the years a judgement from observers (with or without diabetes) about what I personally choose to consume – whether it’s a bite (or a correctly portioned serving) of dessert, pizza, or whatever someone thinks is not acceptable for someone with diabetes. Scott was surprised by the guarded way I was choosing to document and characterize this test, and this post is the result of our discussion.)

Thanks to #DIYPS, I’ve found (several times, the above scenario has proved not to be a fluke!) that I can eat large meals full of carbohydrates, and have no or minimal spike in my blood glucose. It doesn’t matter if it’s high protein, high fat, a mix, or lots of sugar (like a milkshake). And that’s changed the way I feel about talking about large-carb/”non-diabetes friendly” meals.

There’s a well-known stigma related to food for people with diabetes, but no one seems to know a way to remove the stigma. We’re wondering if tools like #DIYPS (and being able to see the data and more outcomes when someone DOES eat pizza and *is* ok BG-wise) will help change the conversation?

Dana Lewis