Wednesday, March 21, 2018

Perception bias in T1D communities: late onset type 1 diabetes

T1D communities are often very vocal when it comes to the issue of initial Type 1 Diabetes. This is especially true when a case of late diagnosis leading to a fatal or debilitating DKA goes viral. At that point, prayers and thoughts (which are as effective in preventing diabetes or treating DKA as they are in school shootings and gun control) fly and the T1D community erupts in “know the signs” memes and aggressive comments about medical professional who, at best, are accused of not doing what they should be doing, namely finger pricking every case of stomach flu they encounter. That measure that would be expensive, hard to interpret, lead to a lot of even more expensive confirmation tests. While they would probably yield a few earlier diagnosis, even fewer early stage DKA detections, the benefits – in the current state of therapies – would be nil in terms of long term prognosis and minimal in terms of DKA complications (ease of access to quality health care matters much more in terms of initial DKA prognosis, initial DKA isn’t a major factor in morbidity/mortality).

One of the latest circulating memes, recycling the “know the signs” memes, is that you can catch T1D at any age (true, although the onset is definitely skewed towards the young) and that T1D is often misdiagnosed as T2D. That’s understandable given the fact that T1D communities will have their fair share of members who have been diagnosed T1D after a misdiagnosis of T2D.

However, the T1D communities perception of “misdiagnosed” T1D is the result of multiple biases.

- Adult T2Ds far outnumber T1Ds.
- T2Ds who were initially misdiagnosed as T1Ds are unlikely to hang around in T1D communities.
- T1Ds who were initially misdiagnosed as T2Ds are likely to join T1D communities and likely to be unhappy and vocal about their misdiagnosis.

What’s the reality?

In fact, the issue is well known and well recognized

Late-onset type 1 diabetes is difficult to diagnose in people aged 31–60 years because it represents only a small minority of patients diagnosed with diabetes; its misdiagnosis as type 2 diabetes results in inappropriate treatment. (quote from the paper below)

However, given the huge discrepancy in the size of the respective populations, what actually happens in terms of diagnosis mistakes is the exact opposite of what the T1D communities are worried about.

Errors are often made when diagnosing type 1 diabetes later in life. For example, more than 50% of patients diagnosed with type 1 diabetes after age 35 years were shown to have type 2 diabetes in long-term follow-up. (quote from the paper below)

That issue is, for example, addressed in “Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetically stratified survival analysis from UK Biobank” (free full text).

One of the sensible ways to improve diagnosis accuracy as far as adult onset T1D and T2D is to take genetic predisposition factors into account.

Sunday, March 4, 2018

Another quick example of the main Libre problem: thermal compensation.

Apologies for not keeping up with the blog, real life has been interfering...

That being said, here is another example of the main drawback of the Libre: thermal compensation is really poor.

Have a look at the chart below and try to guess what it shows...

I am wearing the patch. I am a non diabetic person (neither Type 1, nor Type 2).

12:00: small meal

13:00: exercise (stationary bike starts)

14:00: bottle of sports drink as I start feeling the lack of supplies.

15:00: exercise stops, feeling really drained. My Garmin Fenix says I'll need 67 hours of recovery. My average heart rate was 142, with several spikes to my maximum FC. So far, so good, 61 might be a bit on the lowish side, but nothing incoherent.

15: 30: I do not eat or drink anything and decide to relax in a warm bath for a while...

My BG starts to climb steadily, first spot check is at 105 mg/dL, second spot check is at 157 mg/dL, around 250% of my actual value. As soon as I get out of my bath, my BG starts to drop precipitously (the reader refuses to provide values at that point) and then resumes cruising at the pre-bath value.

What if I had had to take an insulin dosing decision during that time? What if I had an AP running?

Abbott needs to fix this in the future. Fancy apps, not so much. Decent temperature compensation, yes, definitely.

On the "plus" side, at least for Abbott, the combined effect of the sub-optimal (cough, cough) temperature compensation and the delay compensation - the actual smoothed value never reached the projected high - is so bad that I stopped being motivated in reversing it long ago...

Tuesday, November 21, 2017

Trying to navigate the MDI and adolescence maze (and failing…)

Since Max does not want to use a pump, we are on Multiple Daily Injections. On the quick acting insulin side, we have always been using Novorapid pens. We are fairly happy with Novo, assuming it is pre-injected to match the carb absorption curve as much as possible. On the slow acting side, we switched from Lantus to Levemir because we couldn’t avoid a systematic low trend when Lantus picked up. Then, for a few months, Max expressed the desire to switch back to Lantus (I have absolutely no idea why he wanted to do that, but since it is HIS diabetes, he gets to choose…). After a few months, we switched back to Levemir, as the “pickup lows” resurfaced. Those aren’t completely avoided with Levemir, but they are less severe.
As far as the schema and doses are concerned, we have two peculiarities:
  • we use a single evening dose of Levemir in the evening (around 10 units) in contrast with the standard two doses 12 hours apart schema.
  • we tend to have low total insulin requirements, in the 0.25 to 0.50 U per day/kg. At times, after sports, Max can actually skip fast acting doses and have a meal. We did get a few periods (months) where a very low (<0.25U/ TDD was used. Max had an ACTH stimulation test to exclude Addison’s disease which turned out to be normal (along with insulin dosing).
As far as dosing is concerned, Max typically guesstimates his doses (teens…) without severe consequences: only one trip to the hospital for a post-sport hypo that scared the school (a slight over-reaction, but better safe than sorry), zero glucagon injection required, and 4 and 1/2 year of sub 6 HbA1c.

I you are beginning to think we have an easy ride, you are wrong…

“What hath night to do with sleep?”

(John Milton, Paradise Lost)

The biggest problem we have is that there is a huge chasm between the basal insulin theory and the practice, at least in teens. Regardless of the care we put into the injections, variability is kind. Our site rotation plan is quite decent. So is Max’s injection technique. Storage is what it should be. Doses are adapted depending on the exercise of the day. Trends are corrected the next day, etc…

Still, too often, the results seem to be a lottery.

One of the questions on my mind was to double check if we could correlate poor outcomes with injection sites. (Well, to be honest, there are many questions on my mind but I will focus on that one in this post).

Site rotation

We use the standard “front of leg” site for basal insulin. We usually plan a site sequence for the week. Unless a mistake is made, we do not use sites two days in a row and, in the worst case scenario, sites are re-used after a week. Now and then, we reset the sites rotation we use. I am aware that some app can help, but in most cases, apps are a burden and we tend to stay away from them. Max two legs are divided in three height zones (upper, middle, lower) and three sides (internal, middle and external) giving us a total of 18 injection sites to rotate through.

Outcome classification and scoring

Outcome are defined – subjectively – by examination of CGM traces as follows.

STB is a stable night, regardless of the level (we of course would correct stable low or stable high levels). This is the usual archetype of a “good basal rate”
TL and TH are nights with mild trends that you would typically associate with a slightly excessive or insufficient basal rate.

ER, MR, LR are the rises. Early in the night (before 24:00), in the middle of the night (between 00:00 and 3:00) or late in the night (between 3:00 and 6:30). These rises are characterized by a sudden relatively quick increase, steeper than trends but not as steep as meals, they are typically at the rate of roughly 30 mg/dL.hour. Obviously, as soon as the increase is confirmed (example, starts at 90 mg/dL is at 150 mg/dL after two hours) corrective measures are taken. (for example: a 30 mg/dL trend would be corrected by x units to bring the 150 mg/dL back to around 100 mg/dL plus x units to compensate the trend for the next 3 hours). That basic algorithm has never failed us on the low side (no hypo caused) but is sometimes insufficient and is typically reassessed  after 2.5 to 3 hours. Late rises are a pain, especially if they are combined with a dawn phenomenon. As a caregiver, you either have to go to sleep extremely late or wake up extremely early. Setting up CGM alarms is a no go. They aren’t flexible enough to help. 150 md/dL at 6 AM is OK, we aren’t going to be able to correct it anyway, but it would be nice to have one at 1AM in the same conditions and rising.

HYP are the severe low trends which require multiple and frequent corrections. They typically rear their ugly heads after intense sport sessions. We do of course correct those actively. While we do reduce basal in obvious situations and adapt the evening meals after such sport sessions, we never totally got rid of it.

That classification is therefore somewhat subjective and can’t be directly derived by nightly averages.

DAWN the dawn phenomenon is treated separately. It shows up in random clusters. The dawn phenomenon’s classification is again somewhat subjective. That being said, it is quite obvious when it shows up, kicking a LR into high gear above 300 mg/dL, pushing a TL situation into a TH one, etc…

Scoring is even more arbitrary (even though it is normalized for more sophisticated analysis). STB is worth 10 points, that is the ideal situation. Trends are given 6 points, they are extremely easy to assess. Early Rises are not that bad, easy to correct at a decent hour, they are worth 5 points, Middle of the night rises are still manageable and worth 4 points. Late rises are basically uncorrectable: you would have to disrupt the kid’s sleep way too early for a correction, and you might be ending up with stacking issues at breakfast. They only get 3 points. Hypo is the worst case scenario and is worth a single point. ND stands for the few nights the Dexcom wasn’t operational (we cover with the Libre) and are given a “neutral” 6 score.
def outcometoscore(outcome):
    if outcome == 'STB':
        return 10
    if outcome == 'TL':
        return 6
    if outcome == 'MR':
        return 4
    if outcome == 'ER':
        return 5
    if outcome == 'LR':
        return 3
    if outcome == 'TH':
        return 6
    if outcome == 'HYP':
        return 1
    if outcome == 'ND':
        return 6


Here  is a visualization of our actual rotation (approx 300 shots) – the diameter of each circle proportional to the number of shots (ok, the area would be better, but the differences wouldn’t be as visible)

Here is the numerical view
Comment: the human mind is not very good at generating truly random series even if it intends to. Remember that there is always a week before a site is reused though.

We favor the lower zones, possibly because they are easier to reach.

our leg distribution is quite good
So is our side randomization
Now, let’s look at outcomes
As you can see, we have less than a third stable nights, quite a few “trending low” nights (easy to fix with a few dextrose tablets when the trend is established), about 40% of sudden unexplained increases and a relatively low number of severe hypos (more about them later). Regardless of what you know, of how careful you are, the variability of nights is a pain.
We observe a fair number of dawn phenomenons, in around 25% of the cases.


Is any site really bad?
It seems there are some differences indeed. However, given the arbitrary scoring system, the small sample size for some sites and confounding factors (more below), a random distribution can’t be excluded.


Height doesn’t seem to be much of a factor either. The lower part of the leg seems to lead to very slightly better outcomes, but that is only, strictly speaking, statistically borderline significant.
Legs are equivalent. That’s a certainty.
The external side of the leg seems to lead to better outcomes (which are heavily weighed towards stable nights).
Another interesting thing to look at is the average score per week day. Saturday is clearly the worst day and that can be explained by the fact that it is often Max’s most intensive tennis day (2.5 hours) where delayed hypos are more frequent. Wednesday is also a “sport day”, with PE in the morning and tennis in the afternoon. That Saturday turns out to also be a significant confounding factor as far as the injection site is concerned: given our weekly rotation basis, the same site may end up being used frequently on Saturdays…


The impact of the exact injection site is limited in the absence of lipodystrophy.
A good site rotation prevents the appearance of lipodystrophies.
Non computer generated site rotation sequences are not optimal.
That type of basic analysis is interesting, but not tremendously helpful in the absences of lipodystrophies. I suspect that they would however be revealed by careful site tracking.
Other approaches yield slightly more interesting results, at the risk of non statistical significance. Some more informed approaches are quite interesting (post sport night sequences). We may get to that in another blog post.

Even with the best effort, in patients who have been able to maintain sub six HbA1c values for 4 1/2 year, who benefit from a favorable environment (dual CGM, reasonably informed dad on duty) T1D teen nights are highly variable.

What? no dosing info?

You may be surprised by the lack of dosing information/analysis. In practice, insulin sensitivity varies tremendously from one individual to an other. We dose “as best as we can”, following all the standard guidelines. The point of this post was simply to look at the possible impact of sites, assess our site rotation.

Wednesday, October 11, 2017

Libre: the “other” bytes (part 3)

Reminder: Max is currently wearing both the Libre and a Dexcom G4 (505 algorithm, “Share” US reader). We take advantage of the Libre speed during the day and for sports. We love the trouble free Dexcom remote transmission at night and, of course, the alerts. We are not running any third party add-on for the Libre as I remain unsatisfied with them. I am still not convinced that a reliable, independent and non infringing third party solution can emerge. I simply treat the “Libre Problem” as a hobby I come back to now and then.
The time has come for a few corrections and additional information.

First Correction

As I said in a previous post, the Libre can potentially use several temperature compensation methods, as described in their patent (1, 2 or 3 points).  To summarize the options
1 point: at least one point (skin) is certain. There’s the huge thermistor sitting in its nice well, and I can interpret its data in relative terms.
2 points: the “skin and board” option, used to estimate the temperature at the sensing site. That option was my favorite for a couple of reasons: other TI FRL processors offer thermistors and I systematically saw two temperature values move as I heated and cooled the sensor. One of those two values was always a bit ahead of the other, which fit nicely with a diffusion gradient. But there is a catch.

3 points: with skin, board and in-situ thermistors. After having looked at microscopic images of the sensing wire, I think that option can be excluded, but as usual can still be wrong.

The catch

It is simple and stupid at the same time. Back in 2014-2015, I immediately split the 6 bytes immediate Libre record into 3 words. One for BG, two moving on temperature changes, with some flags. I was aware of the existence of flags, and did not drop them as I masked the observed values. That left me with a temperature value (thermistor value) that I could interpret directly based on experimental correlation and another that I had no choice but to consider as a delta.

On the plus side
  • I really liked the 2 points compensation design.
  • I had decently working code that I could reliably use for thermal compensation.
On the minus side
  • I was forced to use one value as LSB, the other as MSB and mask and keep the flags.
  • my temperature data had a resolution of 0.7C
Blog reader Robert Gras (r/o/b/e/r/t.gras./3/3at/gmail) pointed out that I would get rid of the 0.7C quantization issue and have a simpler solution by assembling the bytes differently: the well known G value, some flags, the thermistor value, some flags). This feels much cleaner, thank you Robert!
Here’s an example of where I stand right now, with both the immediate values and the historical data.
A few comments
  • that situation doesn’t warrant temperature compensation, at least from my own algorithm point of view, therefore G and Temp compensated G match. It could require a slight tweak if the temperature remains at a lower (relative) level, but I know from experience that a small temperature change needs a few minutes to impact the glucose oxydase activity.
  • the scan and my interpretation match closely (176 vs 180) but that isn’t always the case. While a lot of my sensors match my algorithm nicely, some sensors may appear “off” if one uses a constant interpretation.
  • I have noticed significant behavior differences between my 2014-2015 sensors and the 2016-2017 ones.
  • I am aware that the condition flags would benefit from being displayed in binary form and correlated with events and situations – I have another visualization for that… Maybe later.
  • the interpretation of historical data (the eight hours stored in the FRAM of the reader) is a bit tricky, in the sense that it is delayed, post-processed (smoothed mostly) by the reader and ends up being a bit different when exported by the PC software.


Blog reader R.Z. brought to my attention an additional thermistor that I had missed on the skin temperature sensing circuit. I am told that this type of circuit is a standard, but probably will have to redo my experimental temperature measurements at some point.
In a way, one can say that I lost a thermistor (correction 1, much to my dismay) and that I gained another one….


I’ll keep logging data and situations as I enjoy the hobby.
But, to be honest, I am still depressed when I see open or semi open source solutions, sometimes even commercial add-on products, use a basic approach that I rejected in late 2014 or 2015…
A concerted strategy to make progress (legal progress that is…) would be to collect and document as many situations as possible, too hot, too warm, trending up, trending down, not available, noisy, clean etc… completely. By completely I mean
  • complete data dump (NXPTagInfo for example).
  • simultaneous official scan
  • simultaneous error log if any
However, one should keep in mind that the Libre system is actually quite flexible and that Abbott can and has changed things whenever it wants.

Monday, September 25, 2017

Libre: the "other" bytes (part 2)

Let's now have a look at the behavior of the Libre thermistor derived data in my archetypal case.

Here is the April 2015 view of the incident.

Quick reminder: in a stable BG situation, the impact of a temperature change (bath), compounded by the Libre predictive algorithm, led to a very significant error by the "official" Libre reader. Outside of outright malfunction, this is the only situation where I felt the Libre could have been dangerous.

And here is the "thermistor" informed version.
From a stable condition, jumping into a warm bath, here is what happened to my son's sensor.

T2: a thermistor value I am fairly sure of, at least in relative terms (see previous post), starts to climb. That is expected.

T1: a thermistor related value also shows a marked increase, at least in my interpretation, it is also noisier, as expected based on my understanding. Please note that it is not shown to scale in this chart. My interpretation of it is somewhat arbitrary, and so is the scaling.

RAW measured IG: the green line, starts to rise. This is most likely due to the sensing site becoming warmer as my kid lies in his bath. Since the mass of his body is huge compared to the mass of the sensor and the enzymatic reaction has its own inertia, measured IG starts to rise more slowly. In fact, it is still rising as my son steps out of the bath.

The official Libre scan gives a reading of 194 mg/dL which almost perfectly fits a basic linear prediction based on the few pas minutes (incidentally, the behavior of that prediction algorithm matches almost exactly one prediction algorithm previously documented by Abbott for the calibration of its "full" Navigator CGM. But then, many prediction algorithms would match).

The actual BG was stable. It was double checked with our BGMeter and fit perfectly with the Libre scans prior to the bath and my own interpretation of raw data.

The key question, at least for me, was what could I do with that data.

Well, I could tell when the temperature was rising, what it seemed to add to the raw BG measures, when and how the delay compensation algorithm kicked in. That may seems a lot, but it could also be summarized as "Max, please do not trust the Libre after sudden temperature changes or when the predictive algorithm shows its ugly side." In practice that is all you need to know.

As far as "standard users" are concerned, I could have come up with an algorithm that reflected what I thought an appropriate correction would be and that could have been used in third party implementations. But let's be real for a minute here:

  • I am not foolish enough to believe my algorithm wouldn't be shaky at times. I can't run clinical tests.
  • the Abbott teams are not fools. I would be delusional if I thought I could better them based on incomplete, guessed information.
  • my algorithms (I tried a few) were of course inspired a bit by my own thoughts and a lot by the literature. Even if they had worked flawlessly, I probably would have knowingly and unknowingly trampled a few patents.
  • as I understand things, covering the sensor would not have been a good thing in general.
  • I did not know, in depth, what I was doing. (and I still don't :) )
  • standard users don't care.
That being said, for a while, running a Dexcom/U. Padova inspired smart sensor algorithm on thermal compensated Libre raw data was fun, if very inconvenient. 

Sunday, September 24, 2017

Libre: the “other” bytes (well, some of them at least)

Personal comment

Back in early 2015, when I started my “running the Libre as a full CGM” experiments, I quickly became aware that the core problem was much more complex than simply figuring out the translation of the so-called Dexcom “raw” signal to human readable values. There’s a reason: in the Libre FRAM, what we are seeing is a real “raw” signal. While the measure of the glucose signal itself is fairly reliable, it is heavily post-processed by the Libre firmware. Specifically - and in no particular order – temperature compensation, delay compensation, de-noising… all play a role. That understanding and, to some extent, my MD training, led me to extreme caution and prevented me from releasing my “solution”, which I knew to be both incomplete and unable to handle some error conditions.

The main driver behind my decision was the well known “first do no harm” (primum non nocere) motto, an essential part of the Hippocratic Oath which I symbolically took. I still stick by it today.
However, by the time I came to realize the full extent of the problem, I had already released enough pointers for developers to build partial solutions upon (and have no doubt my meagre contribution would have quickly been replicated anyway, had the Libre been more widely available back then).

Today, there are a lot of add-on devices that aim to transform the Libre into a full CGM. To be honest, in general, I do not like either the results they provide or their (in)convenience. None of those I have tried delivered results that would lead to an approval by a regulatory agency, none of them were stable for long periods of time. But, apparently, patients still feel they are helpful and there is now a thriving community that aims at improving them.

That is the reason why I will release a bit more information about my own experiments. Keep in mind that I can be wrong.

Personal situation

Max is now a real teen (almost 17), with all the warts of that age. We have been running both the Dexcom G4 (with the 505 algorithm) and the Libre in parallel for more than a year now. This is both a “belt and suspenders” and an optimal results strategy. We mostly use the Dexcom at night, when it is most convenient and the Libre during activities, where we benefit from the added speed. We use what we have when one of them fails (rare) or becomes detached.

As far as we are concerned, the main conclusions shared in 2014/2015 and 2016 on this blog remain true. The Libre is faster and more accurate on the whole. An “anal” calibration strategy brings the Dexcom in the same overall accuracy range, but that strategy is now just a fond memory (teen warts…). The Libre sometimes has a mind of its own (predictive failure and poor temperature compensation. My subjective (and almost statistically significant) impression is that both systems have improved a lot in terms or reliability and post insertion period.

Let me stress that this is not gospel: the performance and the length of reliable operation of a CGM sensor has a lot to do with its eventual encapsulation as a foreign body: your mileage may vary as your macrophages fuse into giant cells and encapsulate the sensor. For some people, I am sure, the Dexcom wire will behave better.

Thermal compensation

As I have shown here before, the Libre and the Dexcom (like all enzyme based bio-sensors) are sensitive to temperature variations (and pH, and potentially other things). This is extremely basic bio-chemistry. You can see an example of this here for example (as a side note, since I am an ill tempered old fart, I quickly grew tired of arguing with non believers Winking smile). Some info on a possible Dexcom temperature compensation strategy can be found here.

That means that the raw signal of a glucose oxydase based sensor has to be compensated for temperature (and ideally pH and pO2, especially for compressions). There are several methods to measure the temperature at the sensing site.

At that point, let me say that I do not know _precisely_ which method is used in which sensor, I can only make reasonable guesses based on patent parsing, probabilities and side indicators. The Dexcom could very well use its platinum electrode wire as a RTD, for example by driving it from time to time with excess current and measuring its resistance.

The Libre thermal compensation

Some of the things I will say in this paragraph are confirmed, some of them are best guesses. Some of them, I am sure will be wrong. Bear with me. The thermal compensation of the Libre signal is described in this patent. Like all “good” patents, there is some obfuscation as many methods are described.

I have worked on the assumption that the Libre follows the 2 point calibration method given in the patent.

Very briefly, that means that the Libre relies on both a “skin thermistor” – that one and its small well in contact with the skin is clearly visible – and a board thermistor. Assuming a certain core temperature (say 34°C), you need to estimate the temperature of the sensing site (below but close to the skin, say 5mm) by measuring a skin temperature that is dependent on the core temperature and the external temperature. A second thermistor, the “board thermistor” located a bit above the skin thermistor adds another measure point that allows you to compute the gradient between the core temperature and the outside world (which can be quite close to the core temperature under clothes for example) if you know the exact distance and thermal conductivity between those two thermistors. In practice, you could also rely on a one point measure (which is what I am doing currently) but there are interesting pluses and minuses to 1, 2 and 3 points gradient estimations.
In a wider context, this method fits with
  • the Libre not having a metallic wire that can be uses as a RTD, could allow lower cost for electrode design.
  • Abbott having the Libre approved for the arm site only (core temperature is different from abdomen which is higher and stabler). (see senseonics troubles with the wrist for additional pointers)
  • Abbott discouraging the covering and not replacing misbehaving sensors that were covered (it potentially messes up the gradient temperature computations)
and a bunch of other anecdotal pieces of evidence.

I can still be wrong, of course:

Abbott could be using one point compensation, but I believe they do not because I see data fitting a 2 thermistors scenario.

Abbott could be using three point compensation with a metallic wire I missed or some other fancy property of their sensing wire they could use as a RTD.


At this point you may begin to think “It sounds great, but where is the data to back this”? Let me show you. But first, let’s have a thought for our sensor 0M0000U0Q68 that suffered a quick and painful death thanks to an impromptu meeting with a door frame.

We’ll be assuming T2 is one of the temperature of interest. Let’s pick the sensor up and put it in my jeans pocket. There will be no glucose measure as the sensor wire is now out the body. Ah, it seems my jeans pocket is warmer than the air outside… (dataset: warmedinpocket)


Let’s drop it on my “lab” table. 21C ambient, should be about right. (dataset: postremovalsuite)

Oops, I forgot it for a while, but now comes a week-end. Let’s take a heating bed and put it at 40C (dataset: 41c)


And let’s slowly bring the temperature down to 36C (dataset 36c)

then 32C (dataset 32c)

then back to room temperature (around 20-21) (dataset 21c)

and finally outside (9C reported by external thermometer dataset 9c4)

Those measures and data sets clearly show there is some validity to the interpretation.
Things to keep in mind: amateur temperature measurement are a pain – breathing on the setup, the height of the table, etc… have an impact. The values are relative, they fit and track the circumstances, but the Libre doesn’t necessarily see them that way. We are not talking “skin” “board” or even delta between skin and board here. Just ambient as a whole.


Let’s look again at what happens during the most drastic change, when the sensor was placed on the heat bed, this time with a bit of code

Mandatory disagreable note: at this point, the reader is expected to know which 2 bytes I am talking about. If he doesn’t know, he just looks them up in the data dump.

t2, high first [12417, 12417, 12417, 12417, 7040, 4992, 4224, 4224, 4224, 4480, 4480, 4736, 4736, 4992, 4992, 4992]
for i in sortedimmediatevalues:
    r2 = hex_str_to_int(i[3])
    r2m = r2 & bitmask14
comment: interesting, values go down as temperature increases as expected fromresistance based thermistors. 

t2 inverted [3966, 3966, 3966, 3966, 9343, 11391, 12159, 12159, 12159, 11903, 11903, 11647, 11647, 11391, 11391, 11391]
temperatures2inverted = [16383-x for x in t2h]
comment: but I am a human, and want them to go up. (dirty hack!)

Temperatures 2, TI [13.83, 13.83, 13.83, 13.83, 30.76, 36.77, 38.97, 38.97, 38.97, 38.24, 38.24, 37.51, 37.51, 36.77, 36.77, 36.77]
def ConvertTemperatureTISpec(counts):
    a = 1
    b = 273
    c = -counts
    d = (b**2)-(4*a*c)
    sol1 = (-b+cmath.sqrt(d))/(2*a)
    return round(abs(sol1), 2)
comment: the TI FRL thermistor formula gives reasonable looking results but my room is definitely not that cold

Temperatures 2 final [20.51, 20.51, 20.51, 20.51, 35.4, 41.07, 43.2, 43.2, 43.2, 42.49, 42.49, 41.78, 41.78, 41.07, 41.07, 41.07] 
def ConvertTemperature(counts):
    sol1 = counts*0.0027689+9.53
    return round(abs(sol1), 2)
comment: that works much better for my purpose… 
This works with my setup, in the temperature range I am interested in. I have exactly zero idea if that is how the FAL sees things. The result I am using could very well be totally off in terms of absolute values. I could be in the linear part of some complex spline or a dangerously exponential function that I would not know about it. I am not an electrical engineer, just a tinkerer. I am particularly concerned about the bottom range of the temperatures: did I hit a hard limit? Not sure. At some point, but I don’t know precisely where, the Libre reader just reports “too cold”. And please note that, in order to avoid a shutdown on no decent glucose values, I could not use the official reader during the experiment.


Yes, I have ideas about the other two bytes. But they are noisy (as the patent hints they would be) and I am currently considering (but not using) them as a delta. I’d rather not talk about them in public. It is easy to see patterns when there are none.

Finally: I am not currently actively looking at the Libre anymore. I just decided to share past data in the hope more competent people could have a look at it.

 download dataset

Wednesday, August 2, 2017

Clean, but shorter, Dexcom G4 (505) Freestyle Libre comparison

Since my previous post has triggered a few private reactions. Here’s another comparison on a fairly standard situation, with clean data: clocks are in perfect synchronisation, there are climbs (pre-game carb loading) and falls, including a severe low (delayed hypo).

On the left, the data as downloaded. On the right, the data shifted for the best correlation (which basically means that the Dexcom data is rolled back in time to erase the delay). That post-mortem analysis is both realistic and a bit unfair to the Dexcom. Realistic because the Libre raw data matches historical data quite well. A bit unfair because the Libre only provides delayed and adjusted historical data. Adjusted relative to what? The spot checks. As I have shown many times on this blog, spot checks are typically even faster than the Dexcom in practice, with the drawback that they are really inaccurate at times, especially on the high side.


In this case, the best correlation is found with a shift of 5-6 minutes (Libre ahead of the Dexcom by 5-6 minutes). This is fairly typical of what we see with the Libre vs the 505, when everything works well for both sensors. That’s the tricky part in practice of course: adhesion issues, desynchronisation between insertions (ie comparing a fresh Dexcom to a Libre in its second week) all play a role.

Broadly speaking, the sensors see the same thing. The 505 data is a bit more bumpy: that is a consequence of the adaptive 505 algorithm and, of course, of the smoothing introduced by the Libre historical data.

One important point: as you can see in the left Bland Altman plot, two well working sensors can show very significant differences based on timing and rate of change.

Regardless of the absolute magnitude of the differences, a consistent behavior emerges: the Libre overshoots highs compared to the Dexcom and undershoots lows to a lesser (absolute) extent. This type of behavior could be the consequence of the calibration slope of the BGM used to calibrate the Dexcom, but we have observed the same behaviors with different BGMs (Menarini Glucomen LX, Roche Accucheck Mobile, Abbott’s Libre BGM). If you are interested in that behavior, the 2014 and 2015 posts on this blog provide additional insight.

The third screen is a log/log plot privately suggested by L. and is basically a Bland Altman on steroids that amplifies the visualization of the differences in behavior in a way that is less dependent on absolute differences. (I am sure I will be corrected if I didn’t get that right).

Beautifying the data

Now, let’s look at the old Clarke plot of the Dexcom vs the Libre. (yes, I know, Clarke plots are out of fashion, but I have had the function for ages, so why not…

First the un-shifted data plot.


Quite decent match, you would not have killed yourself by relying on either device.

Now, the delay corrected data plot.

Isn’t that something? We have gained almost 8% in the A zone.

Now, this doesn’t mean anything in absolute terms. For all we know, the Dexcom could have been right and the Libre could have been overshooting. Only one thing is certain: the delay.

But this tells us something else: it is extremely easy to tweek test results to your liking. Something as simple as asking patients to tests 2 hours after a meal vs asking them to test 1.5 hours after a meal, something seemingly as innocuous as using standard meals or standard sport sessions can have a drastic impact on the numbers. In a market where T1D fanboys love to argue about the 1% MARD advantage of their sensor (while at the same time losing 10% MARD or more through home made hacks), a couple of percent of differences can mean a huge amount of good publicity…