IP Library › Granted Patent US 12,354,751
Granted Patent B2
US 12,354,751 · App. 18/340,751 · Granted Jul 8, 2025

Individualized multiple-day simulation model of type i diabetic patient decision-making for developing, testing and optimizing insulin therapies driven by glucose sensors

Inventors: Martina Vettoretti (Vallá di Riese, IT); Andrea Facchinetti (Trissino, IT); Giovanni Sparacino (Padua, IT); Claudio Cobelli (Padua, IT)
Assignee: DEXCOM, INC.
G16H50/20A61M5/1723G16H20/17G16H40/60G16H50/50A61M2205/50A61M2230/005A61M2230/201
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Quick Facts
Patent No.
US 12,354,751
App. No.
18/340,751
Granted
Jul 8, 2025
Kind
B2
Abstract

A mathematical model of type 1 diabetes (T1D) patient decision-making can be used to simulate, in silico, realistic glucose/insulin dynamics, for several days, in a variety of subjects who take therapeutic actions (e.g. insulin dosing) driven by either self-monitoring blood glucose (SMBG) or continuous glucose monitoring (CGM). The decision-making (DM) model can simulate real-life situations and everyday patient behaviors. Accurate submodels of SMBG and CGM measurement errors are incorporated in the comprehensive DM model. The DM model accounts for common errors the patients are used to doing in their diabetes management, such as miscalculations of meal carbohydrate content, early/delayed insulin administrations and missed insulin boluses. The DM model can be used to assess in silico if/when CGM can safely substitute SMBG in T1D management, to develop and test guidelines for CGM driven insulin dosing, to optimize and individualize off-line insulin therapies and to develop and test decision support systems.

Claims (28)

1. A method comprising:

generating, by a decision support module, a first therapeutic recommendation for a patient based on at least one of glucose data of the patient or insulin pump data of the patient;

determining, by the decision support module, an action taken by the patient responsive to the first therapeutic recommendation, the action comprising ignoring the first therapeutic recommendation, or taking an action similar to, but not identical to, the first therapeutic recommendation; and

generating, based at least in part on the action taken by the patient, a second therapeutic recommendation.

2. The method of claim 1 , wherein the action taken by the patient comprises ignoring the first therapeutic recommendation.

3. The method of claim 1 , wherein the action taken by the patient comprises taking the action similar to the first therapeutic recommendation.

4. The method of claim 1 , wherein the decision support module generates the first therapeutic recommendation based on the glucose data of the patient, and wherein the glucose data comprises continuous glucose monitoring data.

5. The method of claim 1 , wherein the decision support module generates the first therapeutic recommendation based on the glucose data of the patient, and wherein the glucose data comprises at least one of a glucose concentration trend, a predicted glucose concentration, a number of past hyperglycemic events, or a number of past hypoglycemic events.

6. The method of claim 1 , wherein the decision support module generates the first therapeutic recommendation based on the insulin pump data of the patient.

7. The method of claim 1 , wherein the decision support module generates the first therapeutic recommendation based at least in part on meal data of the patient.

8. The method of claim 1 , wherein the decision support module generates the first therapeutic recommendation based at least in part on exercise data of the patient.

9. The method of claim 1 , wherein at least one of the first therapeutic recommendation or the second therapeutic recommendation comprises a recommended insulin dose.

10. The method of claim 1 , wherein at least one of the first therapeutic recommendation or the second therapeutic recommendation comprises a basal insulin change.

11. A system comprising:

an insulin pump to administer insulin to a user; and

at least a memory and a processor to implement a decision support module, the decision support module configured to:

generate a first therapeutic recommendation for the user based on at least one of glucose data of the user or insulin pump data of the user;

determine an action taken by the user responsive to the first therapeutic recommendation, the action comprising ignoring the first therapeutic recommendation, or taking an action similar to, but not identical to, the first therapeutic recommendation;

generate, based at least in part on the action taken by the user, a second therapeutic recommendation; and

control the insulin pump to administer insulin to the user based on the first therapeutic recommendation and the second therapeutic recommendation.

12. The system of claim 11 , wherein the decision support module is implemented at the insulin pump.

13. The system of claim 11 , wherein the decision support module is implemented at one or more computing devices remote from the insulin pump.

14. The system of claim 11 , further comprising a glucose sensor to monitor glucose of the user.

15. The system of claim 14 , wherein the decision support module is implemented at the glucose sensor.

16. The system of claim 14 , wherein the decision support module is implemented at one or more computing devices remote from the glucose sensor.

17. The system of claim 14 , wherein the decision support module generates the first therapeutic recommendation based on the glucose data of the user obtained from the glucose sensor.

18. The system of claim 11 , wherein the decision support module generates the first therapeutic recommendation based at least in part on meal data of the user.

19. The system of claim 11 , wherein the decision support module generates the first therapeutic recommendation based at least in part on exercise data of the user.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 30, 2023
From: VETTORETTI, MARTINA; FACCHINETTI, ANDREA; SPARACINO, GIOVANNI; COBELLI, CLAUDIO
To: UNIVERSITA DEGLI STUDI DI PADOVA
Reel/Frame 064130/0706 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 30, 2023
From: UNIVERSITA DEGLI STUDI DI PADOVA
To: DEXCOM, INC.
Reel/Frame 064130/0722 →
Continuity (4)
Continuation 17451609 · Oct 20, 2021
Division 15158047 · May 18, 2016
Provisional Application 62163091 · May 18, 2015
Related Publication 20230343457A1 · Oct 26, 2023
References Cited (62)
US 5858186A · Glass · 1999 [cited by examiner]
US 6278999B1 · Knapp · 2001 [cited by examiner]
US 8548544B2 · Kircher et al. · 2013 [cited by applicant]
US 8707392B2 · Birtwhistle et al. · 2014 [cited by applicant]
US 8954373B2 · Atlas et al. · 2015 [cited by applicant]
US 9827372B2 · Dobbles et al. · 2017 [cited by applicant]
US 10561351B2 · Lucisano et al. · 2020 [cited by applicant]
US 11749408B2 · Vettoretti · 2023 [cited by examiner]
US 20030055406A1 · Lebel · 2003 [cited by examiner]
US 20050240253A1 · Tyler · 2005 [cited by examiner]
US 20060161218A1 · Danilov · 2006 [cited by examiner]
US 20060272652A1 · Stocker et al. · 2006 [cited by applicant]
US 20070055799A1 · Koehler et al. · 2007 [cited by applicant]
US 20070083335A1 · Moerman · 2007 [cited by applicant]
US 20080220403A1 · Marling et al. · 2008 [cited by applicant]
US 20090006129A1 · Thukral et al. · 2009 [cited by applicant]
US 20090164190A1 · Hayter · 2009 [cited by applicant]
US 20090164251A1 · Hayter · 2009 [cited by applicant]
US 20100179768A1 · Kovatchev et al. · 2010 [cited by applicant]
US 20100280441A1 · Wilinska et al. · 2010 [cited by applicant]
US 20100292634A1 · Kircher, Jr. et al. · 2010 [cited by applicant]
US 20110098548A1 · Budiman et al. · 2011 [cited by applicant]
US 20110106011A1 · Cinar et al. · 2011 [cited by applicant]
US 20110178499A1 · Brukalo et al. · 2011 [cited by applicant]
US 20120246106A1 · Atlas · 2012 [cited by examiner]
US 20140031786A1 · Kircher, Jr. et al. · 2014 [cited by applicant]
US 20140052095A1 · Dobbles et al. · 2014 [cited by applicant]
US 20140060145A1 · Hoss et al. · 2014 [cited by applicant]
US 20150134356A1 · Atlas et al. · 2015 [cited by applicant]
US 20150306304A1 · Schabbach · 2015 [cited by examiner]
US 20160004813A1 · Kovatchev et al. · 2016 [cited by applicant]
US 20160342754A1 · Vettoretti et al. · 2016 [cited by applicant]
WO 2007149533A2 · 2007 [cited by applicant]
WO 2008101172A2 · 2008 [cited by applicant]
WO 2012178134A2 · 2012 [cited by applicant]
Ambrosiadou B.V., et al., “Clinical Evaluation of Diabetes Expert System for Decision Support by Multiple Regimen Insulin Dose Adjustment,” Computer Methods Programs in Biomedicine, vol. 49, 1996, pp. 105-115. [cited by applicant]
Bailey T.S et al., “Clinical Accuracy of a Continuous Glucose Monitoring System with an Advanced Algorithm,” J Diabetes Science Technology, 2015, vol. 9 (2), pp. 209-214. [cited by applicant]
Benharref A., et al., “Closing this Loop from Continuous M-health Monitoring to Fuzzy Logic-based Optimized Recommendations,” Conference on the Proceedings IEEE Eng. Med. Biol. Soc. 2014, pp. 2698-1701. [cited by applicant]
Bergman R.N., et al., “Physiologic Evaluation of Factors Controlling Glucose Tolerance in Man: Measurement of Insulin Sensitivity and Beta-Cell Glucose Sensitivity from the the Response to Intravenous Glucose,” J Clin I… [cited by applicant]
Burdick J., et al., “Missed Insulin Meal Boluses and Elevated Haemoglobin A1c levels in Children Receiving Insulin Pump Therapy Pediatrics,” 2004, vol. 113 (3),pp. E221-E224. [cited by applicant]
Carrier J.M., et al., “Modeling the Adoption Patterns of New Health Care Technology with Respect to Continuous Glucose Monitoring Systems and Information Engineering Designs,” Apr. 25, 2008, pp. 249-254. [cited by applicant]
Colmegna P., et al., “Analysis of Three TIDM Simulating Models for Evaluating Robust Closed-Loop Controllers,” Computer Methods and Programs in Biomed, vol. 113 (1), 2004, pp. 371-382. [cited by applicant]
Davidson P.C., et al., “Analysis of Guidelines for Basal-Bolus Insulin Dosing: Basal Insulin, Correction Factor and Carbohydrate to Insulin Ratio,” Endocr Pract, 2008, vol. 14 (9), pp. 1095-1110. [cited by applicant]
Facchinetti A., et al., “Modeling the Glucose Sensor Error,” IEEE Trans Biomed Eng BME, vol. 61 (3), Mar. 2014, pp. 620-629. [cited by applicant]
Garg S., et al. “Improvement in Glycemic Excursions with a Transcutaneous, Real-Time Continuous Glucose Sensor: A Randomized Controlled Trial,” Diabetes Care, vol. 29(1), 2006, pp. 44-50. [cited by applicant]
Glorennec et al., “Predictive Fuzzy Model of Glycaemic Variations,” Fuzzy Logic and Soft Computing, 1995, pp. 411-420. [cited by applicant]
Hoss U., et al., “Continuous Glucose Monitoring in the Subcutaneous Tissue Over a 14-Day Sensor Wear Period,” J Diabetes Science Technology, Sep. 2013, vol. 7 (5), pp. 1210-1219. [cited by applicant]
International Preliminary Report on Patentability for Application No. PCT/US2016/033106 mailed Nov. 30, 2017, 13 pages. [cited by applicant]
International Search Report and Written opinion for Application No. PCT/US2016/033106 mailed Oct. 24, 2016, 16 pages. [cited by applicant]
JDRF CGM Study Group, “JDRF Randomized Clinical Trial to Assess the Efficacy of Real Time Continuous Glucose Monitoring in the Management of Type 1 diabetes: Research Design and Methods,” vol. 10 (4), 2008, 15 pages. [cited by applicant]
Karon B.S., et al., “Empiric Validation of Simulation Models for Estimating Glucose Meter Performance Criteria for Moderate Levels of Glycemic Control,” Diabetes Technology and Therapeutics, Dec. 2013, vol. 15 (12), pp.… [cited by applicant]
Kovatchev P.B., et al., “Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring,” Diabetes Technology Therapeutics, 2015, vol. 17 (3), pp. 177-186. [cited by applicant]
Lane E.J., et al., “Continuous Glucose Monitors: Current Status and Future Developments,” Current Opinion endocrine, Diabetes and Obesity, Apr. 2013, vol. 20 (2), 9 pages. [cited by applicant]
Merriam-Webster Online dictionary, Definition of “Estimate” available at https://www.merriam-webster.com/dictionary/estimate, accessed Dec. 5, 2020, 16 pages. [cited by applicant]
Pettus J., et al., “How Patients with Type 1 Diabetes Translate Continuous Glucose Monitoring Data into Diabetes Management Decisions,” Endocrinol Pract., vol. 21 (6), 2015, pp. 613-620. [cited by applicant]
Schwartz F.L., et al., “Evaluating the Automated Blood Glucose Pattern Detection and Case-Retrieval Modules of the 4 Diabetes Support System”, J Diabetes Science Technology, vol. 4(6), 2010, pp. 1563-1569. [cited by applicant]
Stadelmann A., et al., “DIABETEX Decision Module 2: Calculation of Insulin Dose Proposals and Situation Recognition by means of Classifiers,” Computer Methods Programs in Biomed, 1990, vol. 32, pp. 333-337. [cited by applicant]
“Use of the DirectNet Applied Treatment Algorithm (DATA) for Diabetes Management with Real-time Continuous,” Diabetes Research in Children Network (DirectNet) Study Group, Apr. 2008, vol. 9(2), pp. 142-147. [cited by applicant]
Vettoretti M. et al., “A Model of Self-monitoring Blood Glucose Measurement Error”, J Diabetes Sci Technol. 2017, vol. 11 (4), 724-735; Epub Mar. 16, 2017, 1-12. [cited by applicant]
Vettoretti M. et al., “A Stochastic Model of Self-monitoring of Blood Glucose Measurement Error: Toward a Simulator of Diabetic Patient Therapeutic Decisions”, (Abstract 381) Advanced Technologies Treatment for Diabetes… [cited by applicant]
Visentin R., et al., “Circadian Variability of Insulin Sensitivity: Physiological Input for in Silica Artificial Pancreas,” Diabetes Technology & Therapeutics, 2015, vol. 17 (1), pp. 1-7. [cited by applicant]
Zahlmann et al., “DIABETEX—A Decision Support System for Therapy of Type 1 Diabetic Patients”, Computer methods programs in biomedicine, 1990, vol. 32, pp. 297-301. [cited by applicant]