DIY-PS

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

by Scott Leibrand

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

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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

by Scott Leibrand

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

#DIYPS goes mobile

by Scott Leibrand

We’ve made progress to enable #DIYPS for on-the-go use.

Previously, we’ve only been able to use it when at home and able to plug the CGM in to an old laptop running Windows. Now, we can get real time predictive alerts anywhere, thanks to an unlocked Moto G that we plug the CGM into. Thanks again to John’s help and his code, the data from the CGM can now be uploaded to the cloud from the phone. We originally planned to use the Moto G on WiFi only, but because of other phone upgrades we ended up finding a cheap way to get data on the Moto G, and will be testing it with data for the next ~6 months.

One challenge for #DIYPS going mobile is that this is another device and more “stuff” a PWD has to carry around. For now, I’m using this case so I can easily drop it in or pull it out of my larger bag that I carry around on most days, while keeping the cords secure. (I’ve found the Moto G’s charger and cords don’t plug in as securely as other devices that we’re used to.)

diyps_goes_mobile

Next steps for #DIYPS mobile testing include using the Moto G and #DIYPS while out running, since I don’t have to rely on WiFi for #DIYPS. It’ll take a few more weeks before I can run regularly due to the cold, rainy Seattle weather, but this will be the most effective road test (pun intended) of the mobile capabilities, and will enable me to test #DIYPS for the first time during intense exercise.

We are also working with Brian, who’s helping us test the system and clean up the #DIYPS code.  We want to get it modular enough for more people to install and use the code more easily, and figure out a better UI (with graphs!). We also want to plug into the frameworks John and Tidepool are using, so we can take advantage of all the great work they’re doing as well.

Stay tuned for more updates.  And if you have some interest and expertise and want to help out, please reach out to us on Twitter or directly.

–Dana Lewis and Scott Leibrand

A DIY Artificial Pancreas System? Are we crazy?

by Scott Leibrand

The Do-It-Yourself Pancreas System (#DIYPS) is a tongue-in-cheek name that recognizes a serious fact: people with (type 1) diabetes (PWDs) have been acting as their own pancreas for years.  What a healthy pancreas does using subcellular machinery in islet beta cells, and a full closed-loop artificial pancreas system (APS) attempts to do automatically using medical technology, are the same things that PWDs have been doing, on their own, with whatever help they can get.  We recognize that for many PWDs the available help is not yet enough, so we are not waiting.

Medical device companies are working on new Artificial Pancreas Device System components, and researchers are conducting clinical trials of artificial pancreas systems incorporating various levels of closed-loop technology to do anything from automatically suspend basal insulin when BGs are below a pre-set threshold (available now in the Medtronic MiniMed 530G) to a fully closed-loop bionic pancreas system that automatically doses insulin (and even glucagon, in one trial) to provide full 24/7 BG control.  However, full closed loop systems are still years away, so there is a serious unmet need for better tools to help people with diabetes manage in the meantime.

The #DIYPS is designed for immediate deployment, using a device-agnostic web-based interface, and optionally ingesting data from any of a user’s existing medical devices using commodity off-the-shelf hardware (such as a Moto G Android phone connected to a Dexcom G4 via USB).  In fact, interactive use does not even require automatic data upload capabilities: once the system is configured for their insulin sensitivity and other user-specific parameters, a user with any sort of CGM or meter can manually enter BG levels, boluses/injection amounts, and carb counts and get an immediate estimate of their resulting BG levels.  In addition, the system will estimate insulin on board (IOB, meaning insulin still working in the body) and unabsorbed carbs, so a later follow-up assessment only requires updating the BG data to figure out if any additional action is required.

The #DIYPS can be used immediately to empower PWDs to prevent many instances of out-of-range BG levels.

The #DIYPS is not a substitute for a true closed-loop Artificial Pancreas*.  It is not a medical device, and does not dose insulin.  However, it does fill two currently unmet needs.  Firstly, none of the CGMs on the market currently support robust enough alerting features to awaken many patients when they experience low BG while sleeping.  Such nocturnal hypoglycemia unawareness is a serious risk for many people with diabetes, and better tools are required to deal with it.  The MiniMed 530G’s Low Glucose Suspend feature is a huge step forward there, but even it doesn’t help with the converse problem: at other times users will end up going an entire night with high BG, and never wake up to their CGM’s high BG alarms.  The #DIYPS’s alert system certainly cannot replace a CGM’s alarms (it’s not a medical device, and various interruptions in connectivity can prevent it from alarming), but it does have the ability to send notifications that can trigger alarms (i.e. on an iPad or phone) loud enough to wake the deepest sleepers. It can also function as a de facto remote alert or monitoring system, allowing family or loved ones in different locations to check and make sure a PWD is responding to alarms. Until device manufacturers get FDA approval for loud enough devices, this alone is enough reason for many people to want to put together their own alerting system like the #DIYPS – and was the original reason for building it.

Secondly, it is currently completely up to patients (with occasional help from their doctors) to determine how much insulin is required at any given time, and decide when additional carbs are required to prevent a low.  Performing such calculations manually every few hours, without ever being able to take a break, is a major problem for the quality of life of many people with diabetes.  There are very few effective tools available today to help, so such calculations are usually done in the user’s head, and are very prone to human error.  (The few tools available are also often not in one system, creating barriers for effective use.) In addition, many factors such as stress, sickness and varying activity levels can impact BG levels.  This combination of factors often results in frequent (often daily) instances of low or high BG levels.  The #DIYPS system provides a framework to support the user in making the best decision in every situation, while reducing the cognitive load on the user, and reduce the risk from harder-to-calculate factors like stress or varying activity levels.  It also helps the user to pay more attention and take action when they are at risk for hypoglycemia (predicted based on CGM, IOB, and carb data input into the system), while allowing them to continue living their life normally, with less worry about managing diabetes.

Finally, the #DIYPS system is designed to be device and data source agnostic, and to integrate with frameworks like Tidepool to allow users to do things that have never been possible before.  As new and better medical devices are released, our hope is that all of them will support methods for allowing a user to export their own data, so that users can truly take advantage of all the innovation we are starting to see in this space.

But today, the #DIYPS is just a prototype system, and has only been development tested with a single user.  In order to determine whether it can truly fulfill the unmet need described above for a wider population, additional development and testing are required.  At this point, we are looking to begin collaborating with anyone who has the skills and interest required to move the project forward.  We are not yet ready to begin allowing (and helping) a large number of users to begin testing the system themselves, but hope to open it up (and hopefully integrate it with the efforts underway at Tidepool.org, among others) in a manner that will allow as many people as possible to begin safely using the system as soon as possible.  However, this is currently only a 2-person side project, so if you think it’s worthwhile to move forward more quickly, I would encourage you to get involved (if you have technical or other skills that would be helpful) or pass along the word to others who might be interested and able to help.  (And if you haven’t already, you might also want to read How I Became a DIY Artificial Pancreas System Builder.)

P.S. A special thank you to John Costik, who graciously provided the drivers we’ve been using to pull data off the Dexcom CGM to a Windows PC, and who is working on perfecting the Android (Moto G) drivers as well.  We wouldn’t have done any of this if it wasn’t possible to get data off the Dexcom CGM in real time.  We’d also like to thank members of the diabetes online community for their support, encouragement, and feedback: we know y’all will be pivotal in driving this forward.  :-)

–Scott Leibrand and Dana Lewis

*Update in December 2014: We wrote this post in February, when #DIYPS was truly a “human in the loop” decision-assist system. However, as of December 2014, we closed the loop with #DIYPS, and also have a version of #DIYPS that is essentially a closed loop artificial pancreas. You can read more about our work on the closed loop artificial pancreas version of #DIYPS here.

How I Became a DIY Artificial Pancreas System Builder

by Scott Leibrand

As some of you may know, I’m a Network and Systems Engineering type who works in the Internet industry.  I have a B.S. in Cell and Molecular Biology, but I have been working in computer networking and for Internet companies for the last decade and a half (since I was still in school), and have had the privilege of working on a number of different types of systems in that time.  Nine months ago, I met a wonderful person who happens to have Type 1 Diabetes, and began learning just what was involved in managing the condition.

On the one hand, I was impressed at the level of data available from her CGM (glucose readings every 5 minutes), and the visibility that provided into what was going on.  But I was also very surprised to find that the state-of-the-art medical technology she uses is, in many ways, stuck in the last century.  Data is not shared between devices; her pump looks and acts like it came straight out of the early 1990s.  (But at least it’s purple!) 😉

Most importantly, it is completely up to the person with diabetes (PWD) to collect all the relevant data on their current state, do a bunch of math in their head, and decide what to do based on the data, their experience, and how they’re feeling.  And as a PWD, that’s not something you just do once in a while: it is a constant thing, every time you eat anything, every time your blood sugars go high or low (for dozens of reasons), every time you want to go exercise, etc. etc. etc.  As a PWD, you’re dealing with life-and-death decisions multiple times a day: Too much insulin will put you into hypoglycemia and can kill you.  No insulin for too long will put you into diabetic ketoacidosis and kill you.  Overreacting to a high or low blood glucose (BG) situation and correcting too far in the other direction can completely incapacitate you.  So as a PWD, you never get a break.

So, very early on, the obvious question was: why can’t we integrate all this data and make this easier?  There were promising signs that it could be done: Artificial Pancreas Systems developed by various research groups and medical device companies are in clinical trials, and are showing promising results.  In fact, she had just signed up for such a trial, and I was able to go with her to the clinic and watch just how a fully automated APS system works.  But despite these brief hints of a better future, day to day we were (usually, she was) stuck doing everything manually.

It was very clear from the beginning that the primary bottleneck to doing something better was the ability to get blood glucose (BG) data off her CGM (a Dexcom G4) in real time.  We knew it was possible to do so manually using Dexcom Studio, a Windows-only software package that is actually quite good for analyzing historical BG data, spotting trends, etc.  But unless we were going to create a Windows macro to open up Dexcom Studio, import the data, export it to CSV, and then close Studio every 5 minutes, we couldn’t get our hands on the data to do anything in real time.

Then, we discovered that John Costik had figured out how to use the Dexcom USB driver’s API to get data off his young son’s CGM in real time, and display it remotely, even on his Pebble watch, and even when his son was at day care or kindergarten.  This was the breakthrough we needed, so we contacted John, and he was able to provide us a copy of his script.

So now we were off to the races.  I cobbled together a system using Dropbox for uploading the BG data from a Windows laptop (that ended up in a bedside drawer), and once I was able to get the data onto a VM server, got working on putting together something that would actually make a difference in her quality of life.

So we worked through a number of ideas.  First off, we needed something that could wake her up at night when her BGs got too high or too low.  The Dexcom G4 is supposed to do this, but even its loudest alerts are still not loud enough to wake a sound sleeper.  People recommend sticking the G4 in a glass or in a pile of change, but that doesn’t work, either. She also had used Medtronic’s CGM system; but even its loudest, escalating “siren” alert isn’t all that loud, even if awake.  We also needed something that would allow me to see the alerts remotely, and see whether she is awake and responding to them.  This is a big deal for many people, like her, who live alone.  So, I started coding, and she started testing.  We ended up using my cracked-screen iPad (it got into a fight with Roomba) as a bedside display (it’s much easier to glance over and see BG values than to have to find the CGM and punch a button), and also to receive much louder push notification alerts.

After we got basic notifications working, we also started trying to enhance them.  We ended up with a prototype system that allows the user to enter when they bolus insulin or take carbs, and snoozes alarms appropriately.  (This enables a de facto remote alert monitoring system, especially handy for individuals living alone.)  With that info, we are also able to do the calculations required to determine how much insulin is needed at any given point (the same calculations PWDs do in their head every time they eat or have to correct for high BG), and by doing those every 5 minutes as new data came in, we were able to provide early warning if she was likely to go low, or if she needed more insulin to correct a persistent high.  Since getting all of that working, we have also developed a meal bolus feature, which takes advantage of an (as far as I know unique) ability to track how fast carbs are likely to be absorbed into the bloodstream to give the user better estimates of how much insulin is required now, vs. how much will likely be required later as the rest of the meal is digested.

Since she started began actively testing the #DIYPS (Do It Yourself Pancreas System), we have seen a decrease in average BG levels, with less time spent low, and less time spent high. It also enables her to execute “soft landings” after a high BG and prevents “rebounds” after a low BG.  We are very encouraged by what we’ve seen so far, and are continuing to iterate and add additional alerts and features.  But today, the #DIYPS is just a prototype system, and has only been development tested with a single user, so lots of additional development and testing are required.  We are not yet ready to begin allowing (and helping) a large number of users to begin testing the system themselves, but hope to open it up in a manner that will allow as many people as possible to begin safely using the system as soon as possible.  However, this is currently only a 2-person side project.  We are looking to begin collaborating with anyone who has the skills and interest required to move the project forward, so if you think it’s worthwhile to move forward more quickly, I would encourage you to get involved (if you have technical or other skills that would be helpful) or pass along the word to others who might be interested and able to help.

If you’re interested, you can also read more about why a #DIYPS is needed now (and how it compares to true closed-loop automated Artificial Pancreas Device Systems).  We’ll also be publishing a full description of how the #DIYPS prototype works shortly, so stay tuned.

–Scott Leibrand