US20210193279A1 - Therapeutic zone assessor - Google Patents

Therapeutic zone assessor Download PDF

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US20210193279A1
US20210193279A1 US17/123,818 US202017123818A US2021193279A1 US 20210193279 A1 US20210193279 A1 US 20210193279A1 US 202017123818 A US202017123818 A US 202017123818A US 2021193279 A1 US2021193279 A1 US 2021193279A1
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insulin
patient
strategy
data
glucose
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Stephen D. Patek
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Dexcom Inc
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Dexcom Inc
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Assigned to DEXCOM, INC. reassignment DEXCOM, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: PATEK, STEPHEN D.
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    • A61M5/1723—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body electrical or electronic using feedback of body parameters, e.g. blood-sugar, pressure
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Definitions

  • Determining the importance value of the at least one therapeutically correlated zone comprises considering at least one of the time of day or proximity of one risk profile to another risk profile.
  • the importance value is the peak value of the at least one single symptom-specific risk profile.
  • Deriving the at least one single symptom-specific risk profile is based on data above a particular credibility level.
  • Outputting the information comprises outputting at least one of numerical, alphanumerical, or graphical information.
  • Outputting the information comprises outputting at least one of behavioral changes or therapeutic changes to a therapeutic zone of time to decrease a single symptom in a time window.
  • Outputting the information comprises outputting the information to a connected insulin pump or insulin pen, or into a bolus calculator.
  • Outputting the information comprises outputting a graphical representation of at least one of risk profiles or therapeutically correlated zones, or relative importance of at least one of risk profiles or therapeutically correlated zones.
  • Performing optimization for the insulin dosing strategy determines whether the patient adheres to a known insulin strategy and analyzes the effect of percentage changes to the parameters of the identified insulin strategy.
  • the user is at least one of a clinician, a patient, or a connected device or system.
  • the output is provided by a natural language processor to describe a candidate change and an optimized risk outcome.
  • Providing the output comprises providing an output in the form of a graph illustrating the optimized insulin strategy parameters to a user interface or connected device.
  • the connected device comprises a bolus calculator.
  • FIG. 1 is a high level functional block diagram of an embodiment of the invention
  • FIG. 2 is a system diagram of an implementation of a therapeutic zone identifier
  • FIG. 16 is a flow diagram for a method of identifying, scoring, and optimizing a patient's insulin strategy
  • the therapeutic zone identifier 210 identifies an interval of the 24 hour day in which the patient's BG data suggests that the patient's medication dose/strategy needs improvement, e.g., the patient's insulin basal rate/dose and/or bolus strategies are systematically non-optimal or are putting the patient at risk of diabetic complications.
  • the therapeutic zone identifier 210 uses only blood glucose data (e.g., not insulin data), such as data from the glucose monitor 120 .
  • the therapeutic zones are assessed based on which candidate behavioral and/or therapeutic changes are predicted to decrease the single-symptom glycemic risk without a subsequent increase in another symptom.
  • a therapeutic zone may be considered a zone that may be therapeutically addressed without risk to negatively affecting symptoms in adjacent time windows.
  • the therapeutic zones may be characterized by minimum non-symptomatic, mixed symptomatic, or different single symptomatic signals.
  • the importance of the therapeutically correlated zone is quantified.
  • an importance value is determined of the therapeutically correlated zone.
  • the zone importance quantifier prioritizes which zone is more therapeutically significant or addressable.
  • the quantifier evaluates the magnitude of the risk, and may further consider the time of day and/or proximity of one risk profile to another risk profile (e.g., because in some cases two different risk profiles are related to each other as they are not isolated incidents and thus one impacts the other).
  • the quantifier may use a mathematic function of the risk profile from 320 , for example, the peak value of the glycemic risk profile, wherein that peak value could be equal to the importance of that therapeutic zone in one exemplary embodiment.
  • the therapeutic zone report generator outputs numerical, alphanumerical, and/or graphical information based on the quantified importance of the therapeutically correlated zones.
  • a visualization of the correlated zones are simply output onto a report.
  • behavioral and/or therapeutic changes to the therapeutic zone of time to decrease the single symptom in the identified recurring time window are identified and outputted.
  • the impact assessor 730 assesses impact of candidate therapy changes by estimating the impact to the risk profile of the historical glucose values.
  • candidate changes to therapy can be proposed directly in terms of, for example, carb ratios, correction factors, basal rates, and/or profiles thereof.
  • candidate changes to therapy can be proposed directly in terms of, for example, carb ratios, correction factors, basal rates, and/or profiles thereof.
  • any parameters used in diabetes management that affects diabetes outcomes may be candidates for change.
  • Parameters may be specific to insulin pumps, bolus calculators or any value associated with insulin therapy whether on type 1 or type 2 single or multiple daily injection therapy, insulin pen therapy, insulin pump therapy, artificial pancreas therapy, beta cell therapy, and/or any aspect(s) thereof.
  • the improvement in overall therapeutic risk based on the candidate changes is assessed (e.g., determined).
  • a replay-predictive function such as described in U.S. application Ser. No. 17/096,785, entitled “Joint state estimation prediction that evaluates differences in predicted vs. corresponding received data”, filed Nov. 12, 2020, inventor Stephen D. Patek, which is incorporated by reference herein in its entirety, is performed to estimate the impact of the candidate changes at the therapeutic zones on historical glucose, and the risk profiling function is run to determine a new risk profile based on the candidate changes. For example, percentage changes (5%, 10%, etc.) to historic boluses and/or basal rates are replayed during therapeutic zones and resulting risk profiles are re-assessed.
  • the relative improvement of the candidate changes may be quantified.
  • the risk profile values are compared to quantify improvement.
  • type 2 diabetes such as for basal titration acceleration, to identify and assess the risk of more or less aggressive type 2 injections (in terms of medication type, medication dosage, and/or time of injection).
  • the therapeutic improvement identifier 1507 evaluates the collated glucose and insulin data 1505 to identify areas for therapy optimization in a patient's diabetes management routine.
  • the identifier 1507 may be a user selection from a clinician or patient (e.g., wherein a user identifies a specific therapy or time of day to be optimized). The user may select, for example, a particular mealtime (e.g., lunch), a specific time of day (e.g., upon waking in the morning), a particular setting (e.g., carb ratio), or the like. Any parameter or behavior that affects insulin therapy may be selected.
  • the therapeutic improvement 1509 is identified by an algorithm, such as the therapeutic zone identifier 210 described with respect to FIG. 2 ; however, other algorithms for identifying areas for improvement are also possible as may be appreciated by one skilled in the art.
  • the therapeutic improvement opportunity identification may comprise a user selection from a clinician or patient (e.g., wherein a user identifies a specific therapy or time of day to be optimized). The user may select a particular mealtime (e.g., lunch), a specific time of day (e.g., upon waking in the morning), a particular setting (e.g., carb ratio), or the like. Any parameter or behavior that affects insulin therapy may be selected.
  • the improvement is identified by an algorithm, such as by the therapeutic zone identifier 210 , however other algorithms for identifying areas for improvement are also possible as may be appreciated by one skilled in the art.
  • the systems and methods identify the patient as a pump user and may further identify the type of usage of the pump, selected from: open loop (evaluates basal and/or bolus/timing), semi-closed loop, and closed loop (which may be further divided, for example, into artificial pancreas algorithm type A and artificial pancreas algorithm type B).
  • Other insulin pump strategies may be identified as appreciated by one skilled in the art, including programmable basal and bolus settings, both in terms of timing and amount, as well as combination basal-bolus therapies recommended by particular programs or providers.
  • the systems and methods identify whether the patient boluses, and if so, what behavioral strategy is associated with their regular bolus pattern.
  • One bolus pattern used by some patients includes a fixed time of day bolusing strategy (i.e., bolusing at specific times of day), meal-time bolusing, carb counting bolusers (e.g., wherein the patient regularly enters different carb amounts at most meals), non-carb counting bolusers (e.g., wherein the patient estimates (S/M/L)), pre-meal bolusing (dosing first and then titrating food), micro-boluser bolusing (e.g., bolusing more than x times per day on average (where x is greater than 5, 6, 7, or more)), and the like as is appreciated by one skilled in the art.
  • bolusing strategy i.e., bolusing at specific times of day
  • meal-time bolusing e.g., wherein the patient regularly enters different carb amounts at most meals
  • the optimization may use the replay-predictive function (e.g., described in U.S. application Ser. No. 17/096,785, entitled “Joint state estimation prediction that evaluates differences in predicted vs. corresponding received data”, filed Nov. 12, 2020, inventor Stephen D. Patek, which is incorporated by reference herein in its entirety) to estimate the impact on historical BG.
  • the risk profiling function may re-run on each iterative optimization and the percentage improvement/change in BG outcome metrics assessed until a certain criteria is met.
  • the insulin strategy optimizer utilizes the replay simulation (e.g., described in U.S. application Ser. No. 17/096,785, entitled “Joint state estimation prediction that evaluates differences in predicted vs. corresponding received data”, filed Nov. 12, 2020, inventor Stephen D. Patek, which is incorporated by reference herein in its entirety).
  • the replay simulates carbohydrates as historically acknowledged by the patient during data collection. Boluses are simulated only at times of acknowledged carbohydrates. Discordance between the replay simulation and historical data is expected because boluses are not simulated at times of historical boluses. Simulated doses are computed strictly according to the prevailing simulated BG, IOB (insulin on board), and acknowledged carbohydrates and the current prescription of carbohydrate ratio and correction factor. This may be referred to as compliant functional insulin therapy.
  • FIG. 19 are charts 1900 , 1950 that illustrate a comparison of risk profiles from the historical data and from replay simulation.
  • FIG. 19 are charts 1900 , 1950 that illustrate a comparison of risk profiles from the historical data and from replay simulation.
  • historical boluses may be delayed and estimates of carbohydrates and insulin on board may be inaccurate.
  • Patient compliance may be optionally scored here to determine compliance with the function insulin therapy.
  • FIG. 21 is a chart 2100 showing two hyperglycemic risk profiles 2120 . Similar to the nominal case, the zones are smaller because the higher basal done leads to less time with BG in the hyperglycemic range.
  • FIG. 22 is a chart 2200 showing two corresponding hyperglycemic therapeutic zones 2220 .
  • FIG. 23 is a chart 2300 showing optimized bolus and basal parameters.
  • the systems and methods may receive CGM, and insulin amount and time, and identify a therapeutic opportunity as afternoon hypoglycemia for a time window (e.g., time x to time y).
  • the insulin strategy is identified as fixed time of day bolusing based on a pattern identified in regular time of day of bolusing patterns.
  • the insulin strategy scorer identifies a correlation with specific times of day of 85%.
  • the insulin strategy adaptation iteratively runs percentage changes in time and amount of insulin bolusing and recommends bolusing for lunch 30 minutes sooner and/or uses 10% more insulin at normal midday bolus.
  • the report out to patient indicates that a 30 minute shift in timing and/or 10% increase in midday fixed bolus would produce a 20% reducing in hypoglycemia, and the combination of both would produce a 25% decrease in hypoglycemia.
  • Computing device 2600 may have additional features/functionality.
  • computing device 2600 may include additional storage (removable and/or non-removable) including, but not limited to, magnetic or optical disks or tape.
  • additional storage is illustrated in FIG. 26 by removable storage 2608 and non-removable storage 2610 .
  • the glucose and insulin data is received from at least one of a patient or a connected system or device. 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.
  • the parameter setting is a carb ratio.
  • the candidate changes to insulin therapy comprise percentage increases or decreases to bolus therapy or basal therapy.
  • the candidate changes to insulin therapy comprise changes to insulin delivery parameters associated with bolus therapy or basal therapy.
  • the candidate changes are in terms of carb ratios, correction factors, basal rates, or profiles.
  • the candidate changes comprise basal dose sensitivity.

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