artificial pancreas system

#DIYPS Closed Loop – One month of data & it still works great

by Dana Lewis

My name is Dana Lewis and I have a closed loop artificial pancreas (that I built) that I use every night. Also see: woot!

Tweet that says "I love you #DIYPS closed loop"

We closed the loop in December (click here for more details about the first week of data & basics on how the closed loop works; or here to learn more about the basic non-closed loop #DIYPS and how far we’ve come in a single year), and I’ve been using it almost nonstop since.

When to loop – or not

I’ve only been limited in using the closed loop for overnight when I inadvertently fried the SD card in the Raspberry Pi (a known issue that’s related to the number of times you plug/unplug it from power source). We promptly ordered a second Pi as a backup, and by the time it arrived, Scott managed to revive the first one, so I now have two ready to run at any time. (Because of course you need emergency backups of your essential organs!)

When I first started looping in December, I originally intended to only use it at night, but it was hard to not loop during the day, too. The idea that I could sit at my desk and work for 3-4 hours straight, and not worry about my BGs, is so appealing!

However, it’s not really practical for me to want to use 24/7 every day…yet. This is where the versions of APS that will come to market in 3+ years will be great, because it will be all-in-one (or at least limited in excess pieces you have to carry). To wear it 24/7, I have to have enough batteries to power the raspberry pi and keep it in range, which means constantly picking it up and moving it around, carrying it in my bag from meeting to meeting, etc. I’ve decided it’s not worth it for every day, but it is awesome to have the choice when sitting through an important interview to have it running and have more security and peace of mind.

Back to using the closed loop for overnights

When we first showed our closed loop efforts off, we had about a week’s worth of data that already showed improved overnight blood glucose (eAG) and time in range, plus reduced number of #DIYPS alarms.

After a month, the results of using a DIY closed loop artificial pancreas system overnight continue to look promising:


We also recently added another screen to #DIYPS so I could access and review “what happened last night”, or what was the output of the closed loop. I usually look at the net impact per hour (how much above or below my normal basal rates I ended up getting), but also at the individual numbers of how much above and how much below I ended up getting every hour. Here’s a visualization:

Tweet illustrating how DIYPS closed loop works overnight with micro corrections to keep blood glucose in range

Using closed loop activity to spot trends and change basal rates

We also built a 7 day and 30 day view of this closed loop activity history so I can look at trends by hour overnight (versus manually compiling and visualizing them, like I did in our first closed loop data post here).

When we first looked at the data for overnight closed loop net insulin activity, I could spot a trend easily – the loop temped low from midnight to 2am, leveled out, then increased with higher temps after 5am. I can easily see why it often ends up temping low after I go to sleep, because I often do a half unit or so if I’m riding “high” (130 – ha!), and feel safe doing so with the loop to catch any more drop than I desire. (This pattern was consistent at 7, 14, and 30 days, although the average net insulin amount varied slightly.)

However, when I first started looping overnight in December, I had rises around 2am that the loop had to handle by providing higher temporary basal rates than my scheduled basals. I upped my midnight basal rate as a result, but it looks like I upped it too far since the closed loop often temps low now during that time frame. My next step is to probably change my midnight basal to be lower by 0.1u, and see if that reduces the amount of low temps needed from 12-2am, without causing spikes. My theory is that this alone may also reduce the number of higher temps from 2-4am, because those may be a result of the lower temps earlier on. Thus, I am starting overnight basal tweaking with this one change and seeing how much of a ripple effect it has for the rest of the night as well.

In pursuit of…what? Or: what are we optimizing for?

My BG averages overnight (and during the day) are excellent (well within the “normal” range <120) and time in range is continuing to grow (85% & counting) with using the closed loop #DIYPS. So, beyond minute basal tweaking, what’s next?

At this point, I think the next goal is encouraging other independent researchers to get up and running on their own closed loop overnight APS.  Do we really need to wait another 3+ years for APS technology to come to market?  What exactly would #WeAreNotWaiting look like if we applied it to closed loop Artificial Pancreas technology research? What do you think?

How does a closed loop artificial pancreas work when you DIY? Or: #DIYPS closed loop is working!

by Dana Lewis

We closed the loop with #DIYPS and now have a DIY closed loop artificial pancreas running right now (for me, aka n=1.)

Here’s what a closed loop #DIYPS artificial pancreas looks like:


(A raspberry pi powered with a battery (shown) or via an outlet; a Carelink USB plugged into the Pi and in close proximity to the pump; a Medtronic insulin pump (an older version, thanks to a family from CGM in the Cloud for sharing it!); and of course my CGM and Android uploader phone that’s sending my data into the cloud/#DIYPS)

Here’s how a closed loop artificial pancreas or “#DIYPS closed loop” works:

Over the past year, we’ve been using #DIYPS to process BG, insulin on board and carbohydrate decay in real time. This yields real-time predictive alerts of three kinds: more insulin needed (X.X units of bolus insulin); less insulin needed (temp basal to zero insulin for X minutes); or carbs needed. Using this algorithm and my data entered (insulin taken or reduced; carbs ingested); we’ve seen significant results not only for the first 100 days, but also sustained over an entire year for improved average glucose, improved a1c, and improved time in range.

Closing the loop with #DIYPS

Three weeks ago, we mentioned at D-Data Exchange 2014 that we were planning to close the loop with #DIYPS…by August 1 (our wedding date :)).

The FDA was even in the room.

Two weeks later…we closed the loop.

It was this simple:

  • Thanks to Ben West’s awesome work, we plugged the Carelink USB into the Raspberry Pi. Thanks to a generous family from the CGM in the Cloud Facebook group, we acquired an old 522 insulin pump that could talk to the Carelink USB. (My current pump is too modern to work with this setup.)
  • Once we established that the Pi/USB setup could read from the pump, we started connecting #DIYPS to the system to close the loop.
  • Normally, #DIYPS provides 3 types of recommendations (see above). We reconfigured it so that while “looping”, it only enables the pump to do temporary basal rates instead of boluses. If #DIYPS says I need more insulin, it tells the pump to begin a temporary basal rate for 30 minutes above my normal basal rate. If #DIYPS says I need less insulin, it tells the pump to begin a temporary basal rate lower than my normal basal rate for 30 minutes, effectively reducing my net insulin.
  • When something changes and I need to do more or less insulin, #DIYPS tells the pump to increase or decrease the temporary basal rate, or resume my normal basal rate.
  • Note: there is a maximum basal rate that the pump can provide. This is a safety check so that #DIYPS cannot recommend a seriously dangerous basal rate. In fact, if I need more insulin than #DIYPS closed loop can safely provide using a 30 minute temp basal, the system will alarm and alert me to that state (although I usually already know about it). Same for if I was dropping fast and needed carbs in addition to reducing insulin; it would alarm and recommend carbs above and beyond reducing insulin.

I’ve been ‘looping’ (using #DIYPS closed loop) for a little over a week. Here are some of my overnight results:

As you can see, the outcomes are awesome. In fact, they are better than I expected!

#DIYPS closed loop results are significant

Here’s a break down that shows on an example night (Sunday night, 12/14 through Monday morning, 12/15) of how #DIYPS performed:

You can see that around 10:30 (not pictured, but in the graph), I was running low and needed some temp basals to zero, then later some higher temp basals to accommodate the resulting rise in BG. Later, my normal basal rates sufficed until another point overnight when I needed less insulin to prevent a low.

While this is 1/9 or so example nights, this is pretty much how it works every night. BGs drift up? Temp basal higher (usually 1.6 or 1.8, which is <=.5u above my normal). BGs drifting low? Temp basal lower or in extreme cases to zero to prevent a low.

What happens if the closed loop artificial pancreas fails/stops working?

If the loop stops working*, #DIYPS continues to work normally (processing all data and pushing recommendations), and my pump goes back to my normal basal profiles after any existing temp basal rates expire (which would be a max of 30 minutes). No big deal and no more dangerous than I would be walking around with my pump acting normally.

*I’ve found numerous ways to make the system stop working: no battery power; being too far away from the USB stick so the pump can’t communicate; no cell signal to provide wifi as a hotspot to the Raspberry Pi; if the pump battery goes low, RF gets turned off and the USB can’t talk to the pump; if there’s no cell signal or wifi at all, I can’t upload CGM data to #DIYPS anyway; if the SD card gets corrupted inside the Raspberry Pi; etc.

So, there’s many points of failure. But it doesn’t matter. #DIYPS closed loop is safer for me (disclaimer: just me! I’m the only one doing this as a personal experiment to my own body) than if I just had a pump and a CGM, because it can reduce insulin and prevent lows. And it can increase insulin slightly and prevent rebounds.

After about a week’s worth of data, here’s the results of #DIYPS (what I’ve been using for the past year) versus the week of closed loop #DIYPS:


The target BG is especially awesome, because we have the closed loop targeting a BG of 115!

It’s also worth noting the reduction in alarms. Remember, I sleep through CGM alarms, so we built #DIYPS. #DIYPS makes a lot of alarms – sometimes upwards of 40 a night (those are really bad nights!). With the closed loop nights, there are very few alarms..and they’re usually alerts about things #DIYPS closed loop is taking care of on it’s own (see all of those no-hitters above!).


What’s next with #DIYPS closed loop

We’re going to continue and iterate on the closed loop and keep making it better. I’m definitely going to keep using it overnight – it’s like having a working pancreas, the kind where you can go to sleep without fear.

We’re also adding this to the ‘things we’ve learned while working on #DIYPS’ list:

  • Calculating the carbohydrate absorption rate for an individual – how many carbohydrates your body absorbs in an hour
  • Tracking “carbs on board” or “carb decay” – how many carbohydrates are still being digested in your body and impact BG levels
  • “Net” IOB – tracking net IOB by incorporating “negative” boluses from temporary basal rates to zero
  • Preventing post-meal BG spikes/drops with early meal boluses and better understanding of insulin activity
  • … and you can control overnight BGs solely using simple adjustments of insulin rates.

Thanks to everyone who made it through a really long post, and for the MANY people who have contributed in various ways to helping us close the loop with #DIYPS! Also, let us know what questions come to mind that we haven’t managed to answer above about how the system works, results, etc.

Why #DIYPS N=1 data is significant (and #DIYPS is a year old!)

by Dana Lewis
As I’ve said many times, last year we set out to create a louder CGM alarm system. By adding “snoozes” so I didn’t drive my co-investigator crazy, we realized I might as well enter what I was doing, and be precise about it (aided by some quick bolus and quick carb buttons that made data-entry not the chore that it sounds). Thus, we had the data and the brains to realize that this made for some great predictions; much better than what you usually see in diabetes tools because they rely only on your insulin sensitivity factor (ISF) and correction rate, but don’t take into account carbs on board and their impact over time, etc.

(If you’re new to #DIYPS, read about the beginnings of it here. For more on this idea of carbs on board and the carbohydrate absorption rate and how significant it is for people with diabetes, read about that here.)

After I had spent 100 days using #DIYPS, Scott and I stopped to look and see what the impact was. For the long version, read this post about the results and the direct comparison to the bionic pancreas trial data that was available then. The short version: #DIYPS reduced my eAG and a1c significantly, reduced lows, reduced highs – aka my time in range was improved from 50% to regularly 80+%.

I have asked myself (and others have asked), are these results sustainable? Are these improved outcomes truly because of #DIYPS? It’s definitely worth noting I never changed what or how I ate. (I ate 120 grams of pizza (for science! ;)) several times to test the system, but I didn’t eat less or any healthier or otherwise change my diet.)
I can’t attribute these outcomes directly to #DIYPS alone, but I do believe they’re highly correlated. It’s hard to separate other contributing factors like the fact that I have more boluses per day using #DIYPS (which other studies have shown decreases a1c); or the fact that I spend less time high/low because with #DIYPS I actually can wake up at night and take action before I’m high or low.
So, it’d be hard to study specific factors and say “it’s all #DIYPS”. But, I’m pretty sure it’s mostly #DIYPS. Regardless, here’s the updated data about the sustainability of the results I’ve seen with #DIYPS over the past year:
Why this is significant
#DIYPS is currently n=1 (meaning one person is the study’s subject). But what is significant is that I have year’s worth of data and actual lab-tested a1cs that shows the outcome of this type of artificial pancreas work. And it (to date, but coming soon  / OMG this week!) hasn’t even been closed loop – I’m still the “human in the loop” making decisions and pushing buttons on my pump.Compared to the bionic pancreas and other artificial pancreas study trials where they have a few days and a few more (usually n=20 or so) subjects; they can look at the decrease in lows and highs and improved eAG…but they can only project what the a1c improvement is going to be.We’ve shown the improvements in lab-tested a1cs – see my graph above?

It all adds up
Scott and I are not the only ones working on a closed loop. The community of developers connected to the Nightscout community has nearly two hands full of people who are working independently on device interoperability to close the loop by freeing our data from devices and enabling us to work with our own algorithms regardless of which hardware device we use to support our diabetes management.When all of these n=1 studies add up, it matters. At some point in the near future, after we’ve closed the loop with #DIYPS (ah! this week! :)) and others have as well, we may have more n=1 hours on closed loop artificial pancreas systems than the (traditional) “researchers”. Scott and I are hoping that we can not only show the world how open source innovation and new regulatory paradigms can deliver safe and effective results for people living with T1D faster than traditional medical device development and traditional regulation; but that we can also change how all successful medical device companies approach interoperability, and how traditional medical researchers do research – possibly in partnership with patient researchers like us.

What we’ve been up to lately – closing the loop with #DIYPS and open process work

by Dana Lewis

We’ve been quiet on the blogging front (yes, I admit, I blogged!) because we’ve been busy closing the loop. (We’re always active on Twitter, though – make sure you watch #DIYPS on Twitter for more real-time updates in between blog posts, and you can follow @ScottLeibrand and @DanaMLewis.)

That’s right.

Closing the loop.

It feels good to say that :).

We’re submitting a research proposal to Medtronic to get access to their research device that will enable us to send commands to my insulin pump. These commands will be generated from #DIYPS. Because that would remove the “human in the loop” component that’s a current part of #DIYPS (i.e.  me pushing the pump buttons), that would move #DIYPS from being not an artificial pancreas to a closed loop artificial pancreas system.

This is not what we imagined we would do when we set off nearly a year ago to do this work (make a louder CGM alarm system, enable remote monitoring, etc.), but this is awesome!

And, in the true #wearenotwaiting spirit, we’re not waiting for the research device to start this phase of our work. We got started with this phase of our research by getting a Raspberry Pi and hooking it up to the Carelink USB and sending commands to a test Medtronic pump.

(Note: this pump is not hooked up to my body! There is no insulin in it! We’re just testing to see how data can be read off the pump & fed into #DIYPS. There will be many tests with a pump not connected to my body and many safety checks and stages before we would get anywhere near to allowing this to run under waking, close observation let alone under observed sleeping conditions. Stay tuned for more on this later.)

Where we are with #DIYPS and Nightscout

We’ve been having many discussions with the other developers of Nightscout, and end users in the CGM in the Cloud Facebook group around this topic. Scott and I are both passionate about the open source part of our project and being open and transparent every step of the way. We’ve been learning a lot from #DIYPS and have some key features that we’re putting back into the overall Nightscout platform as a result. We’re calling this “#DIYPS-Lite”, or the “wip/iob-cob” branch of Nightscout. (This is not what the main community is using, it’s still an advanced test branch, but enables more advanced users to implement and take advantage of some of these features, like net IOB and COB.) We’ve also been contributing to the roadmap of Nightscout; as of this past weekend, we helped map out the priority list to show what are priorities of some of the core developers (including Scott and I); what’s currently being worked on; and what’s on the ‘backlog’ as a project we’re all interested in and is important to the community, but no one’s had a chance to jump in and start it yet.

Back to the closed loop update

We won’t be releasing any closed-loop systems to the public any time soon, because that would mean distributing an untested medical device system that controls administration of a potentially lethal drug (insulin), which the FDA (and anyone in their right mind) would (rightly) frown on.

Instead, we want to open up our process of developing such systems, so that other researchers (including independent ones like us, and those inside traditional research labs and companies) have a roadmap to follow for how to safely put together and test an open-source artificial pancreas system.  The more people and companies we have developing such systems, sharing their results, and learning from each other’s work, the closer we’ll be to having a system that could indeed be proven safe and effective to the FDA’s satisfaction, and eventually released to the public.  In the end, we suspect the companies and research labs developing APS systems will be the ones to commercialize them, but we are confident that our efforts will accelerate those timelines and result in a more dynamic, innovative T1D medical device ecosystem that will provide a bridge to a true cure.

We are hoping that we can show the world how open source innovation and new regulatory paradigms can deliver safe and effective results for people living with T1D faster than traditional medical device development and traditional regulation.  By doing so, we can change how all successful medical device companies approach interoperability, and get to a world where the latest ideas, algorithms, and software can be made available to everyone with T1D, regardless of what hardware they’re using.

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(More to come next week as we mark 1 full year of #DIYPS in action (!) and we have some new data to share!)

12 years & counting – are we there yet?

by Dana Lewis

Today marks 12 years of living with diabetes. (Like everyone else, I wonder what to do on yet another diabetes anniversary – celebrate? Cry? Eat gluten-free cupcakes? All three things?) And interestingly, I’m almost at the one year mark of living with diabetes powered by #DIYPS.

When I was diagnosed, my blood glucose meter was bigger in all dimensions than the iPhone 6+.  Luckily, that’s changed, and my meter is now about the size of my Pebble smart watch – and that watch actually shows my my blood glucose readings from my CGM every 5 minutes.

Even better, in the last year, we’ve built a system that will not only give me a number, but help me track changes and push alerts so I can pay attention when needed…but otherwise live my life and not let diabetes get in the way of the things I actually want to be doing (running marathons, reading marathons, saving the world, etc.).

What hasn’t changed, though? My insulin pump. I’m pretty sure it’s a slightly different purple color than it was 11 or so years ago, but not much else has changed. For 11+ years I’ve been wanting:

  • To be able to see (on the pump) a HISTORY of any temporary basal rates
  • To be able to have “days of the week” programmable basal rates. (As you can imagine, my Tuesdays and Saturdays look a little different!)
  • To be able to have “net IOB” calculated and displayed to reflect the true amount of insulin in my body

For 11 years, I didn’t question the lack of progress, much, though. That’s just the way it was. I imagined it would probably be this way until we got a cure for diabetes in (5 or insert your magical number here) years.

I’m glad this is no longer the way it is and that I have tools like #DIYPS. But I hope that medical device companies will get out of their rut and the way they’ve been doing innovation and business for eons and do better. Because what they’re doing right now is not good enough. And #wearenotwaiting.

What is #DIYPS (Do-It-Yourself Pancreas System)?

by Dana Lewis

#DIYPS (the Do-It-Yourself Pancreas System) was created by Dana Lewis and Scott Leibrand in the fall of 2013.

Curious about building a closed loop for yourself? Head to OpenAPS.org!

#DIYPS was originally developed with the goal of solving a well-known problem with an existing FDA-approved medical device. We originally 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 blood glucose (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
  • ..and as of December 2014, we ‘closed the loop’ and have #DIYPS running as a closed loop artificial pancreas.
  • (And, as of February 2015, we also launched #OpenAPS, an open and transparent effort to make safe and effective basic Artificial Pancreas System (APS) technology widely available. For more details, check out OpenAPS.org)

You can read this post for more details about how the #DIYPS system works.

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 the concepts behind #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

Here are some frequently asked questions about #DIYPS:

  1. Q: “I love it. How can I get it?“A: #DIYPS is n=1, and because it is making recommendations based on CGM data, we previously have said that we can not publicly post the code to enable someone else to utilize #DIYPS as-is. There’s two things to know. 1) To get some of the same benefits from #DIYPS as an “open loop” system, with alerts and BG visualizations, you can get Nightscout, which includes all of the publicly-available components of #DIYPS, including the ability to upload Dexcom CGM data, view it on any web browser and on a Pebble watch, and get basic alarms for high and low BG – and depending on which features you enable, you can also get predictive alarms. Some of the other core #DIYPS features (“eating-soon mode“, and sensitivity/resistance/activity modes) are now available in Nightscout. 2) If you are interested in your own closed loop system, check out OpenAPS.org and review the available OpenAPS documentation on Github to help you determine if you have the necessary equipment and help you determine whether you want to do the work to build your own implementation.
  2. Q: “Does #DIYPS really work?”A: Yes! For N=1, using the “open loop” style system with predictive alerts and notifications, we’ve seen some great results. Click here to read a post about the results from #DIYPS after the first 100 days – it’s comparable to the bionic pancreas trial results. Or, click here to read our results after using #DIYPS for a full year. We should probably update this to talk about the second year of using #DIYPS, but the results from the first year have been sustained (yay!) and also augmented by the fact that we closed the loop and have the system auto-adjusting basal rates while I sleep.
  3. Q: “Why do you think #DIYPS works?”A: There could be some correlation with increased timed/energy spent thinking about diabetes compared to normal. (We’d love to do some small scale trials comparing people who use CGMs with easy access to time-in-range metrics and/or eAG data, to compare this effect). And, #DIYPS has also taught us some key lessons related to pre-bolusing for meals and the importance of having insulin activity in the body before a meal begins. You should read 1) this post that talks about our lessons learned from #DIYPS; 2) this post that gives a great example of how someone can eat 120 grams of carbohydrates of gluten-free pizza with minimal impact to blood glucose levels with the help of #DIYPS; 3) this post that will enable you to find out your own carbohydrate absorption rate that you can start using to help you decide when and how you bolus/inject insulin to handle large meals or 4) this post talking about how you can manually enact “eating-soon mode”. And of course, the key reason #DIYPS works (in open loop mode) is because it reduces the cognitive load for a person with diabetes by constantly providing push notifications and real time alerts and predictions about what actions a person with diabetes might need to consider taking. (Read more detail from this post about the background of the system.)
  4. Q: “Awesome!  What’s next?“A: If you haven’t read about #OpenAPS, we encourage you to check it out at OpenAPS.org. This is where we have taken the inspiration and lessons learned from using #DIYPS in manual mode (i.e. before we closed the loop), and from our first version of a closed loop and are paying it forward with a community of collaborators to make it possible for other people to close the loop! Some new features we wanted for #DIYPS that will likely still happen, but integrated with Nightscout and/or #OpenAPS include:
    • calculation of insulin activity and carb absorption curves (and from there, ISF & IC ratios, etc.) from historical data
    • better-calibrated BG predictions using those calculated absorption curves (with appropriate error bars representing predictive uncertainty)
    • recommendations for when to change basal rates, based on observed vs. predicted BG outcomes
    • integration with activity tracking and calendar data
    • closing the loop – done as of December 2014! :) and made possible for more than (n=1) with #OpenAPS as of February 2015

    We also are collaborating with medical technology and device companies, the FDA, and other projects and organizations like Tidepool, to make sure that the ideas, insights, and features inspired by our original work on #DIYPS get integrated as widely as possible. Stay tuned (follow the #DIYPS hashtag, Dana Lewis & Scott Leibrand on Twitter, and keep an eye on this blog) for more details about what we’re up to.

  5. Q: “I love it. What can I do to help the #DIYPS project? (or #openAPS)”A: #DIYPS is still a Dana-specific thing, but #OpenAPS is open source and a great place to contribute. First and foremost, if you have any ability to code (or a desire to learn), we need contributors to both the Nightscout project as well as #OpenAPS. There are many things to work on, so we need as many volunteers, with as many different types of skills, as we can get.  For those who are less technical, the CGM in the Cloud Facebook group is a great place to start. For those who are technical and/or want to close the loop for themselves, check out OpenAPS.org, join the openaps-dev google group, and hop on the #intend-to-bolus channel on Gitter. If you want to contact us directly, you can reach out to us on Twitter (@DanaMLewis @ScottLeibrand) or email us (dana@openAPS.org and scott@openAPS.org). We’d also love to know if you’re working on a similar project or if you’ve heard of something else that you think we should look into for a potential #OpenAPS feature or collaboration.

– Dana Lewis & Scott Leibrand

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

by Dana Lewis

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

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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.

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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.

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