Actionable Information

 

InSpark has developed software tools that analyze blood glucose and other data to provide feedback to people with diabetes when they need it most. The algorithms that are being developed to power InSpark's software tools are described below:

 

Hypoglycemia Risk Indicator:  An alert that indicates when someone is at a greater risk of a severe hypoglycemic episode in the next 24 hours.

 

Pattern Messaging System:  A system that messages people about upcoming daily patterns of hyperglycemia (high blood glucose), hypoglycemia (low blood glucose), glucose variability and testing deficiencies when they test.

 

Average Daily Risk Range (ADRR): A measure of glycemic variability that has been shown to be highly correlated with future high and low blood glucose excursions.  

 

HbA1c Estimation:  A software tool that generates regular estimates of HbA1c values, providing a more sophisticated method of evaluating long term glucose control compared to using reference tables for average glucose.

 

Addressing Important Needs

These tools have the potential to help people with diabetes:

  • Better track glucose control with supportive “report cards” over the weeks and months between doctor's visits
  • Correct undesirable daily glucose trends and patterns
  • Dose insulin more accurately
  • Lower the risk of severe hypoglycemia and ketoacidosis
  • Lower the risk of long term complications

 

With a Robust Scientific Foundation

InSpark's software tools have been developed using a collaborative process involving people with diabetes, physicians, behavioral psychologists, engineers and mathematicians. The algorithms were first conceived to address specific clinical needs, then tested and enhanced in large blood glucose data sets obtained from hundreds of people with diabetes. A number of these software tools have been incorporated in a mobile health ecosystem named Vigilant that demonstrated a significant reduction severe hypoglycemia (50% reduction in BG<40 mg/dL) and strong engagement in an early study (n=46). Vigilant was developed under a quality system and design control procedures compliant with FDA CFR part 820 for medical devices.

 

Below are some of the related scientific publications supporting these efforts. The software tools and underlying algorithms are also protected by a broad portfolio of pending and issued patents.

 

Supporting Journal & Poster Publication References

Otto EA, Tannan V, Shadforth, I, A Novel Pattern Recognition Algorithm for Alerting to Risk of Severe Hypoglycemia in the Next 24 Hours.  Poster presentation at the Diabetes Technology Meeting, Oct 2015

Otto EA, Tannan V.  Evaluation of the Utility of a Glycemic Pattern Identification System. J Diabetes Sci Technol, July 2014 Vol 8, No. 4, p 830-838

Otto EA, Tannan V. Characterizing Poor Glycemic Control Following Periods of Infrequent Testing.  Poster Presented at Diabetes Technology Meeting, November 2013

Otto EA, Tannan V. Optimizing Pattern Messaging Frequency to Improve Interventional Outcomes. Poster Presented at Diabetes Technology Meeting, November 2013

Patton SR, Clements MA. Average Daily Risk Range as a Measure for Clinical Research and Routine Care, J Diabetes Sci Technol 2013;7(5):1370-1375

Farhy LS, Ortiz EA, Kovatchev BP, Mora AG, Wolf SE, Wade CE. Average Daily Risk Range as a Measure of Glycemic Risk is Associated with Mortality in the Intensive Care Unit: A Retrospective Study in a Burn Intensive Care Unit. Journal of Diabetes Science and technology, Sep 2012, Vol 5, Issue 5, p 1087-1098

Kovatchev BP, Mendosa P, Anderson S, Hawley JS, Ritterband LM, Gonder-Frederick L, Effect of automated bio-behavioral feedback on the control of Type 1 diabetes, Diabetes Care Feb 2011; 34 (2): p 302-7

Pitsillides AN, Anderson SM, Kovatchev BP. Hypoglycemia Risk and Glucose Variability Indices Derived from Routine Self-Monitoring of Glucose Are Related to Laboratory Measures of Insulin Sensitivity and Epinephrine Counterregulation.  Diabetes Tech Ther Vol 13, No 1, 2011 p 11-17

McCall AL, Cox DJ, Brodows R, Crean J, Johns D, Kovatchev BP. Reduced Daily Risk of Glycemic Variability: Comparison of Exanatide with Insulin Glargine, Diabetes Technology & Therapeutics,  2009, Vol 22, Num 6, p 339-344

McCall AL, Kovatchev BP, The Median is Not the Only Message: A Clinician’s Perspective on Mathematical Analysis of Glycemic Variability and Modeling in Diabetes Mellitus, Journal of Diabetes Science and Technology, Jan 2009; 3 (1): p 3-11

Cox DJ, Gonder-Frederick L, Ritterband L, Clarke W, Kovatchev BP, Prediction of severe hypoglycemia, Diabetes care, Jul 2007; 30(6): p 1370-6

Kovatchev BP, Otto E, Cox D, Gonder-Frederick L, Clarke W, Evaluation of a new measure of blood glucose variability in diabetes, Diabetes Care, Nov 2006; 29(11): p 2433-8

Kovatchev BP, Anderson SM, Otto E, Field Glucose Variability is Related to Laboratory Measures of Insulin Sensitivity and Hypoglycemia Counterregulation. Poster presented at the European Association for the Study of Diabetes annual meeting, 2006

Kovatchev BP, Cox DJ, Kumar, A, Otto, E, Gonder-Frederick LA, Clarke WL, Validation of an Algorithm for Estimating HbA1c from Self-Monitoring of Blood Glucose Data.  Poster presented at American Diabetes Association Annual Professional Meeting, June 2004

Kovatchev PB, Cox DJ, Numerical estimation of Hba1c from routine self-monitoring data in people with type 1 and type 2 diabetes mellitus. Methods in enzymology, Feb 2004; 384: p 94-106

Kovatchev BP, Cox DJ, Gonder-Frederick L, Clarke WL.  Predicting occurrence of moderate and severe hypoglycemia in the next 24 hours. Poster presented at American Diabetes Association Annual Professional Meeting , June 2003

Kovatchev BP, Cox DJ, Gonder-Frederick L, Clarke WL.  Approximate Evaluation of HbA1c rom Routine Self-Monitoring Blood Glucose Data. Poster presented at American Diabetes Association Annual Professional Meeting , June 2003

Kovatchev BP, Cox DJ, Kumar A, Gonder-Frederich L, Clarke WL, Algorithmic evaluation of metabolic control and risk of severe hypoglycemia in type 1 and type 2 diabetes using self-monitoring blood glucose data. Diabetes technology and therapeutics, Jan 2003; 5(5): p 817-828

Kovatchev BP, Cox DJ, Gonder-Frederick L, Clarke WL, Methods for quantifying self-monitoring blood glucose profiles exemplified by an examination of blood glucose patterns in patients with type 1 and type 2 diabetes, Diabetes technology and therapeutics, Jan 2002: 4(3): p 295-303

Kovatchev BP, Straume M, Cox DJ, Farhy, LS, Risk Analysis of Blood Glucose Data: A Quantitative Approach to Optimizing the Control of Insulin Dependent Diabetes, J of Theoretical Medicine, 2001, 3: p 1-10

Kovatchev BP, Cox DJ, Farhy LS, Straume M, Gonder-Frederick L and Clarke WL, Episodes of Severe Hypoglycemia in Type 1 Diabetes Are Preceded and Followed within 48 Hours by Measurable Disturbances in Blood Glucose, The Journal of Clinical Endocrinology & Metabolism, 2000, Vol 85, No 11: p 4287-4292

Kovatchev BP, Farhy LS, Cox DJ, Straume M, Yankov VI, Gonder-Frederick LA, Clarke W. Modeling of Insulin-Glucose Dynamics during Insulin Induced Hypoglycemia. Evaluation of Glucose Counterregulation. Journal of Theoretical Medicine, 1999, Vol 1, Issue 4, p 313-323.

Kovatchev BP, Cox DJ, Gonder-Frederick LA, Young-Hyman D, Schlundt D, Clarke W, Assessment of Risk for Severe Hypoglycemia Among Adults with IDDM, Validation of the low blood glucose index, Diabetes Care, Nov 1998, Vol 21, No 11: p 1870-1875

Kovathev BP, Cox DJ, Gonder-Frederick LA and Clarke WL, Symmetization of the Blood Glucose Measurement Scale and its Applications, Diabetes Care, 1997, 20, p 1655-1658