Therapeutic zone assessor
Systems and methods are provided for identifying therapeutic zones where there is glycemic dysfunction of a specific type that can be addressed by making strategic changes to behavior and/or therapy parameters. Systems and methods described herein evaluate large historical data sets to: identify a therapeutic zone or zones with glycemic dysfunction that are most readily addressable; quantify the glycemic impact of a plurality of different therapeutic adjustments in terms of either adjustments to historical doses or the parameters of a prospective dosing strategy to determine the highest possible improvement; and/or identify patient dosing strategies to provide therapy recommendations adapted for the patient's preferred behavioral dosing strategy.
1 . A method for reducing glycemic dysfunction, comprising:
receiving, via an interface circuit, continuous glucose monitoring (CGM) data and corresponding insulin delivery data;
automatically, with at least one processor executing stored instructions, identifying a therapeutic improvement opportunity by retrospectively analyzing a historical glucose dataset generated from the received glucose data, the therapeutic improvement opportunity occurring during one or more time zones where there is glycemic dysfunction;
determining, by the processor, a plurality of candidate changes to at least one insulin delivery parameter for the one or more time zones that have been identified;
executing a replay simulation that applies each candidate change to the historical glucose dataset to generate a respective simulated glycemic-risk profile;
computing, for each simulated glycemic-risk profile, a quantitative risk-reduction metric and selecting, by the processor, at least one candidate change that provides a largest reduction in the risk-reduction metric;
generating, by the processor, pump-specific, machine-readable control instructions that encode the selected at least one candidate change;
outputting the control instructions via a wired or wireless transceiver operably coupled to an insulin infusion pump; and
administering the insulin therapy by automatically adjusting parameters and/or timing of insulin therapy in the insulin infusion pump in real time in response to the transmitted control instructions, thereby implementing the at least one candidate change that is outputted.
2 . The method of claim 1 , wherein the glucose and insulin data is received from at least one of a patient or a connected system or device, and wherein multiple simulated glycemic-risk profiles are associated with the one or more time zones, and wherein implementing the at least one candidate change addresses multiple correlated glycemic risks in the one or more time zones.
3 . The method of claim 1 , wherein identifying the therapeutic improvement opportunity comprises receiving a user selection identifying a specific insulin therapy or time of day to be optimized, wherein the user selection is at least one of a mealtime, a time of day, or a parameter setting.
4 . The method of claim 3 , wherein the parameter setting is a carb ratio.
5 . The method of claim 1 , wherein the candidate changes to insulin therapy comprise percentage increases or decreases to bolus therapy or basal therapy.
6 . The method of claim 1 , wherein the candidate changes to insulin therapy comprise changes to insulin delivery parameters associated with bolus therapy or basal therapy.
7 . The method of claim 1 , wherein the candidate changes are in terms of carb ratios, correction factors, basal rates, or profiles.
8 . The method of claim 1 , wherein the candidate changes comprise basal dose sensitivity.
9 . The method of claim 1 , wherein the candidate changes comprise percentage change to basal or bolus doses in therapeutic zones.
10 . The method of claim 1 , wherein quantifying the improvement of the candidate changes comprises comparing risk profile values.
11 . The method of claim 1 , wherein outputting at least one of the candidate changes based on the improvement comprises outputting the candidate change that provides the optimized risk profile.
12 . The method of claim 1 , wherein outputting at least one of the candidate changes comprises providing an output in the form of a graph illustrating at least one of a candidate change or an optimized risk output to a user interface or connected device.
13 . The method of claim 12 , wherein the connected device comprises a bolus calculator.
14 . The method of claim 12 , wherein the output is provided by a natural language processor to describe a candidate change and an optimized risk outcome.
15 . The method of claim 12 , wherein the output identifies which therapeutic zones or zone groups have been optimized.
16 . The method of claim 1 wherein the historical glucose dataset is collected over a period of one week.
17 . A system comprising:
at least one processor; and
a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to:
receive, via an interface circuit, continuous glucose monitoring (CGM) data and corresponding insulin delivery data;
automatically, with at least one processor executing stored instructions, identify a therapeutic improvement opportunity by retrospectively analyzing a historical glucose dataset generated from the received glucose data, the therapeutic improvement opportunity occurring during one or more time zones where there is glycemic dysfunction;
determine, by the processor, a plurality of candidate changes to at least one insulin delivery parameter for the one or more time zones that have been identified;
execute a replay simulation that applies each candidate change to the historical glucose dataset to generate a respective simulated glycemic-risk profile;
compute, for each simulated glycemic-risk profile, a quantitative risk-reduction metric and select, by the processor, at least one candidate change that provides a largest reduction in the risk-reduction metric;
generate, by the processor, pump-specific, machine-readable control instructions that encode the selected at least one candidate change;
output the control instructions via a wired or wireless transceiver operably coupled to an insulin infusion pump; and
administer the insulin therapy by automatically adjusting parameters and/or timing of insulin therapy in the insulin infusion pump in real time in response to the transmitted control instructions, thereby implementing the at least one candidate change that is outputted.
18 . The system of claim 17 , wherein the glucose and insulin data is received from at least one of a patient or a connected system or device.
19 . The system of claim 17 , wherein identifying the therapeutic improvement opportunity comprises receiving a user selection of at least one of a mealtime, a time of day, or a parameter setting.
20 . The system of claim 19 , wherein the parameter setting is a carb ratio.
21 . The system of claim 17 , wherein the candidate changes to insulin therapy comprise percentage increases or decreases to bolus therapy or basal therapy.
22 . The system of claim 17 , wherein the candidate changes to insulin therapy comprise changes to insulin delivery parameters associated with bolus therapy or basal therapy.
23 . The system of claim 17 , wherein the candidate changes are in terms of carb ratios, correction factors, basal rates, or profiles.
24 . The system of claim 17 , wherein the candidate changes comprise basal dose sensitivity.
25 . The system of claim 17 , wherein the candidate changes comprise percentage change to basal or bolus doses in therapeutic zones.
26 . The system of claim 17 , wherein quantifying the improvement of the candidate changes comprises comparing risk profile values.
27 . The system of claim 17 , wherein outputting at least one of the candidate changes based on the improvement comprises outputting the candidate change that provides the optimized risk profile.
28 . The system of claim 17 , wherein outputting at least one of the candidate changes comprises providing an output in the form of a graph illustrating at least one of a candidate change or an optimized risk output to a user interface or connected device.
29 . The system of claim 28 , wherein the connected device comprises a bolus calculator.
30 . The system of claim 28 , wherein the output is provided by a natural language processor to describe a candidate change and an optimized risk outcome.
31 . The system of claim 28 , wherein the output identifies which therapeutic zones or zone groups have been optimized.