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230    CHAPTER 11 Retrofitting CGM traces




                                  Clarke’s Error Grid Analysis        Rate Grid − CGEGA
                            100                               100
                           Percent in zone A [%]  80         Percent in zone Ar [%]  80
                                                               90

                            60
                                                               70
                            40


                                   CGM       Retrofitted CGM   60    CGM       Retrofitted CGM
                         FIGURE 11.4
                         Evaluation of the retrofitting method on test-set data. Boxplot of percent of points in zone
                         A (Clarke’s error grid, left) and zone Ar (rate grid, right). Each gray dot represents one
                         patient admission.
                         in each admission (each gray dot represents a patient admission). Results confirm the
                         improvement provided by the retrofitting method.
                            Fig. 11.4 reports the percentage of points falling in zone A of Clarke’s error grid
                         (accurate measurements) [22]. The percentage was computed for each patient
                         admission and depicted in a boxplot (each gray dot represents a patient admission).
                         Improvement is significant, with the retrofitting achieving more than 90% points in
                         zone A in more than 75% of the patient admissions, while the same percentage in
                         zone A is achieved in less than 25% of the admissions by CGM. Analogously, right
                         panel of Fig. 11.4 shows the percentage of points falling in zone Ar of the rate grid
                         (accurate glucose rate) of the rate error grid of the continuous glucose error grid
                         analysis [23]. Also in this case the percentage was evaluated for each patient admis-
                         sion and depicted in a boxplot. Rate analysis confirms superiority of retrofitted traces
                         with respect to the unprocessed ones.
                            In conclusion, we showed that retrofitting enhances precision and accuracy of a
                         CGM collected in outpatient-like setups. Collecting CGM data and then retrofitting
                         them is a viable alternative for reducing the frequency of blood glucose sampling
                         without losing temporal resolution.




                         Retrofitting real-life adjunctive data
                         In this section, we show that the retrofitting method is effective in improving the
                         accuracy of Dexcom sensor (Dexcom G5) when used in real life as adjunctive treat-
                         ment to SMBG. This newer sensor reached the 1-digit precision, and it is currently
                         one of the most accurate CGM on the market. The scenario considered in this section
                         is substantially more challenging for the retrofitting algorithm with respect to the
                         one considered in the previous section, since it offers less (wfive SMBGs per diurnal
                         session) and less accurate references (SMBG rather than YSI). An in-depth analysis
                         of this setup can be found in Ref. [24].
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