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.
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.