Thursday, May 12, 2016

Libre Data Interpretation (continued - and probably final for parameters)

In the previous post, I left you with this approximate calibration curve which served me well for a couple of months.

However, there were occasional hiccups, that I spent a couple of months investigating. If you have a sharp eye, you will have noticed that it contains two outliers (highlighted in the second to bottom graph).

Similar outliers were also identified vs blood tests. Excluding reader errors, that could only mean one thing - the outliers were the results of an algorithm. In April 2015, I had enough outlier samples to reach significance and summarized my findings here.

 In short (graphs from the above post).

The Libre predicted highs that did not materialize and rewrote them after the fact.

 The BG Meter often agreed with a direct interpretation of the raw data in those cases (implying the outlier spot checks were algorithmic)

I spent some time looking at problematic cases (above) and less problematic ones (below)

That allowed me to detect outliers, remove them, and fine tune the data I used for the calibration slope parameters. Here are the resulting parameters, when blatant outliers are removed. You can see that, suddenly the correlation and confidence level improve tremendously.

The math and the data sets told me that I was really close to the real thing. On individual runs, my "private experimental" algorithm started tracking the Abbott data very very closely. (to be honest, I do not remember precisely if I had already started at algorithmic issues when I generated this chart as I was doing many things in parallel.)

At that point, in April 2015, I moved to 181 intercept and 7.26 slope. While the difference in numbers may seem large, it does not make a huge difference as you see below.

If my profound indignation at discovering the "divide by ten" surprised you, consider that I spent two months (not continuously of course ;)) waiting for special cases to examine and understand to go from the parameters  of 01/2015 to those of 04/2015, which I consider (because the math tell me so, and the subsequent runs confirmed) very significantly better.

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