Method, system and computer readable medium for adaptive and advisory control of diabetes

Patent metadata

Field	Value
Publication number	US-11024429-B2
Application number	US-201214241383-A
Country	US
Kind code	B2
Filing date	Aug 26, 2012
Priority date	Aug 26, 2011
Publication date	Jun 1, 2021
Grant date	Jun 1, 2021

Abstract

An Adaptive Advisory Control (AA Control) interactive process involving algorithm-based assessment and communication of physiologic and behavioral parameters and patterns assists patients with diabetes with the optimization of their glycemic control. The method and system may uses all available sources of information about the patient; (i) EO Data (e.g. self-monitoring of blood glucose (SMBG) and CMG), (ii) Insulin Data (e.g. insulin pump log files or patient treatment records), and (iii) Patient Self Reporting Data (e.g. self treatment behaviors, meals, and exercise) to: retroactively assess the risk of hypoglycemia, retroactively assess risk-based reduction of insulin delivery, and then report to the patient how a risk-based insulin reduction system would have acted consistently to prevent hypoglycemia.

First claim

We claim:

1. A processor-based method for treating a patient with insulin, the patient suffering from type 1 diabetes mellitus (T1DM), by providing a posterior assessment of a risk of hypoglycemia in the patient, the method comprising:
receiving, via a processor, historical data including a patient's absolute blood glucose (BG) levels, BG variability, insulin delivery, and activities;
determining, via said processor or another processor and via kernel density estimates of BG time series from the historical data, a parameter, R hypo (record), that is representative of the risk of hypoglycemia associated with the posterior probability of hypoglycemia, P(E hypo |record), where E hypo denotes the event of hypoglycemia in the next day and record refers to a record of the patient's historical BG levels, insulin delivery, and activities record;
comparing, via said processor or said another processor, the historical insulin delivery data with the R hypo (record) associated with the P(E hypo |record);
receiving, via said processor or said another processor, real-time BG and real-time insulin delivery data and adjusting a basal rate profile for the patient based on the comparison so as to ameliorate risk of entering hypoglycemia; and
calculating, via said processor or said another processor, a correction bolus based on the adjusted basal rate profile and delivering insulin in accordance with the correction bolus calculation.

2. The method of claim 1 , wherein the absolute BG levels and the BG variability are data derived from a continuous glucose monitoring (CGM) device and the insulin delivery is data obtained from an insulin pump device.

3. The method of claim 1 , wherein the absolute BG levels and the BG variability are data derived from a continuous glucose monitoring (CGM) device and the insulin delivery is data obtained from a manual insulin injection device.

4. The method of claim 1 , wherein the absolute BG levels and the BG variability are data derived from a self-monitoring blood glucose (SMBG) device and the insulin delivery is data obtained from an insulin pump device.

5. The method of claim 1 , wherein the absolute BG levels and the BG variability are data derived from a self-monitoring blood glucose (SMBG) device and the insulin delivery are data obtained from a manual insulin injection device.

6. The method of claim 1 , wherein the output device is configured to store the R hypo (record) in a memory.

7. A processor-based method for treating a patient by using retroactive analysis to provide a safe level of insulin dosage for the patient, the patient suffering from type 1 diabetes mellitus (T1DM), the method comprising:
receiving, via a processor, a historical record of blood glucose (BG) levels and insulin delivery for a patient;
determining, via said processor or another processor, a risk of hypoglycemia for patient activities using a symmetrization function;
determining, via said processor or said another processor, an attenuation factor that acts as a threshold when delivering insulin;
receiving, via said processor or said another processor, real-time BG and activity data and adjusting a basal rate profile for the patient to incorporate the attenuation factor so as to ameliorate risk of entering hypoglycemia, wherein adjusting the basal rate profile involves reducing temporary basal rates before meals and/or following exercise; and
calculating, via said processor or said another processor, a correction bolus based on the adjusted basal rate profile and delivering insulin in accordance with the correction bolus calculation.

8. The method of claim 7 , wherein the record of the insulin delivery are data obtained from an insulin pump device.

9. The method of claim 7 , wherein the record of the insulin delivery are data obtained from a manual insulin injection device.

10. The method of claim 7 , wherein the attenuation factor is computed as follows:

ϕ
⁡

(

R
⁡

(

t
,
τ

)

)

=

1

1
+

k
patient

⁢

R
⁡

(

t
,
τ

)

where R(t, τ) is a measure of the risk of hypoglycemia between time t and t+τ based on the historical record of BG and insulin data up to time t, based on the BG symmetrization of function and kpatient is a patient-specific aggressiveness factor.

11. A processor-based method for treating a patient with insulin, the patient suffering from type 1 diabetes mellitus (T1DM), by providing a net effect based patient adaptive model, the method comprising:
delivering insulin based on a patient's metabolic system;
computing, by a processor:
a dynamic model of the patient's metabolic system, wherein said dynamic model includes descriptive parameters of an individual physiology of the model patient;
a corresponding inferred history of a behavioral net effect model that explains the glucose variability in the historical record through the dynamic model, wherein said net effect model includes a mathematical representation of perturbations of the model patient; and
an update of the patient's physiological parameters based on both (i) the ability of the dynamic model to predict future blood glucose (BG) based on known inputs and (ii) the ability of the model to produce net effect curves that are consistent with the patient's record of the perturbations; and

estimating, via said processor or another processor, the patient's current metabolic system using the updated physiological parameters;
adjusting, via said processor or said another processor, a basal rate profile for the patient in accordance with said patient's estimated current metabolic system; and
calculating, via said processor or said another processor, a correction bolus based on the adjusted basal rate profile and delivering insulin in accordance with the correction bolus calculation.

12. The method of claim 11 , wherein said descriptive parameters include a representation of the dynamic relationship between oral carbs d (g/min), physical activity e (cal/min), subcutaneous insulin u (U/hr), and the model patient's metabolic state vector χ whose elements include glucose and insulin concentrations (mg/dl) in various compartments of the body and carbohydrate mass (mg) in the gut.

13. The method of claim 12 , wherein the glucose concentration (mg/dl) are data derived from a continuous glucose monitoring (CGM) device and the subcutaneous insulin u and the insulin concentration (mg/dl) are data obtained from an insulin pump device.

14. The method of claim 12 , wherein the glucose concentration (mg/dl) are data derived from a continuous glucos

Assignees

• Patek Stephen D
• Kovatchev Boris P
• Univ Virginia Patent Foundation

Inventors

• Patek Stephen D
• Kovatchev Boris P

Classifications

• A61B5/002

  Monitoring the patient using a local or closed circuit, e.g. in a room or building (A61B5/0017 takes precedence) · CPC title

• A61B5/4839

  combined with drug delivery · CPC title

• A61B5/14532

  for measuring glucose, e.g. by tissue impedance measurement · CPC title

• A61B5/0022

  Monitoring a patient using a global network, e.g. telephone networks, internet · CPC title

• A61B5/486

  Biofeedback (using electroencephalography [EEG] A61B5/375) · CPC title