Basal titration with adaptive target glucose levels
A technology for glucose and blood sugar levels, used in applications, auto-injectors, medical devices, etc.
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example 1
[0241] Example 1 . This example provides a fasting glucose objective function where a single glucose risk metric is used and the fasting glucose objective function has the following form:
[0242]
[0243] where, for example, x 1 is the first blood glucose risk measure. Figure 10 The figure shows Fasting glucose target function. The subject's first glycemic risk measure 236 is updated (eg, as set forth in block 406 ) to obtain the value x 1 . Then, this value x 1 is used to calculate the fasting glucose objective function , and thus obtain the minimum target fasting blood glucose level 226 and the maximum target fasting blood glucose level 227 (respectively FGL min 226 and FGL max 227) to update the target fasting blood glucose level between 225.
example 2
[0244] Example 2 . In some embodiments, the fasting glucose target function is a linear or non-linear combination of glycemic risk measures. Figure 11 The graph shows an embodiment in which the non-linear fasting blood glucose objective function is based on variability in fasting blood glucose levels and basal insulin therapy adherence scores over the time course of the first data set. Alternatively, the variability in fasting glucose levels is expressed as a function of a first glycemic risk metric, and the basal adherence score over the time course of the first data set is expressed as a function of a second glycemic risk metric, where the function fits The increased FGL is expressed in terms of an increased glycemic risk measure, eg, FGL is a function of an increase in the glycemic risk measure.
[0245] exist Figure 11 In , the less variability observed in fasting glucose levels, the lower the FGL can be set. In other words, reduced variability in fasting glucose lev...
example 3
[0246] Example 3 : Using Glucose Measurements to Determine Whether Fasting Events Are Adherent to Insulin Therapy . In some embodiments, a first data set 228 including a plurality of glucose measurements is obtained. In some embodiments, glucose measurements are obtained autonomously, such as by continuous glucose monitor 102 . In this example, in addition to autonomous glucose measurements, insulin administration events are obtained in the form of insulin medication records 240 from one or more insulin pens 104 used by the subject to apply prescribed insulin therapy 212 . These insulin medication records 240 can be in any format, and indeed can be spread across multiple files or data structures. Thus, in some embodiments, the present disclosure takes advantage of recent advances in insulin administration pens, which have become "smart" in the sense that they can remember the timing and amount of insulin medication administered in the past. One example of such an insulin...
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