Empirical Representation of Blood Glucose Variability in a Compartmental Model

Patek, Stephen D.; Lv, Dayu; Ortiz, Edward A.; Hughes-Karvetski, Colleen; Kulkarni, Sandip; Zhang, Qian; Breton, Marc D.

doi:10.1007/978-3-319-25913-0_8

Cited by 36 publications

(41 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The final module of the deployed DSS consists in a CGM/ insulin/meal-based, automated, insulin treatment parameters' optimization procedure. Based on net effect resimulation technology, 39 this module identifies systematic risk for hypoand hyperglycemia, and modulates basal insulin (rate patterns for CSII or total dose and timing for MDI), carbohydrate to insulin ratios (CR), and ISFs. Each day of the collected data is first extended from 8-h before to 4-h after midnight (total of 36 h) to avoid border effects.…”

Section: Automated Insulin Titrationmentioning

confidence: 99%

Continuous Glucose Monitoring and Insulin Informed Advisory System with Automated Titration and Dosing of Insulin Reduces Glucose Variability in Type 1 Diabetes Mellitus

Breton

Patek

et al. 2018

Diabetes Technology & Therapeutics

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Automated Insulin Titrationmentioning

confidence: 99%

Continuous Glucose Monitoring and Insulin Informed Advisory System with Automated Titration and Dosing of Insulin Reduces Glucose Variability in Type 1 Diabetes Mellitus

Breton

Patek

et al. 2018

Diabetes Technology & Therapeutics

Self Cite

View full text Add to dashboard Cite

show abstract

“…For the design and testing of the AID system, we used an extended version of the Subcutaneous Oral Glucose Minimal Model (SOGMM) presented in [62] with the equations presented in Section "Simulation Platform for Islet Transplantation".…”

Section: Discussionmentioning

confidence: 99%

“…Thus, model individualization/identification will be needed to tailor the AID algorithm to individual patient's physiology and to individual degrees of success of islet transplantation. All numerical results are obtained with a modified version of the Subcutaneous Oral Glucose Minimal Model (SOGMM) presented in [62] with the equations introduced in Section "Simulation Platform for Islet Transplantation" presented in the Appendix.…”

Section: Figurementioning

confidence: 99%

Role of Automated Insulin Delivery (Artificial Pancreas) in Islet Transplantation: An In Silico Assessment

Lv¹,

García-Tirado²,

Fabris³

2018

obm transplant

Self Cite

View full text Add to dashboard Cite

Background: Human pancreatic islet transplantation is a minimally-invasive procedure that is gaining recognition for the treatment of type 1 diabetes (T1D). Selected patients with unstable T1D, hypoglycemia unawareness, history of severe hypoglycemia, and glycemic lability, not successfully stabilized with intensive insulin treatment, can be offered this alternative therapy that has been shown to provide long-term glycemic control with nearnormalization of hemoglobin A1c in the absence of severe hypoglycemia. Today, downsides of pancreatic islet transplantation include the need for chronic recipient immunosuppression and the limited supply of pancreatic islets. In addition, attaining long-term insulin independence remains a challenge. In this context, stabilization of a patient's metabolic system with islet transplantation that is augmented by automated insulin delivery (AID) technology could be of significant interest. In this manuscript, we want to frame and illustrate the problem of developing a combined bio-artificial system that includes an islet graft and mechanical AID. Methods: Our discussion will propose modeling approaches potentially deployable in describing glucose homeostasis after islet transplantation and will be supported by a series

show abstract

“…Only Kovatchev et al [47] explicitly addressed this problem and tried to give some indications on this topic. By modifying retrospectively some CGM traces using the so-called “net-effect” model [48], the authors reached the preliminary conclusion that CGM sensors with MARD

\leq 10 %

could be suitable for insulin dosing decisions. The work also highlighted that, in order to draw more solid conclusions, additional investigation is needed, and, in particular, the focus should be on that part of CGM sensors presenting a bad accuracy (i.e., those with high MARD).…”

Section: The Present: the Nonadjunctive Cgm Usementioning

confidence: 99%

Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges

Facchinetti

2016

Sensors

147

View full text Add to dashboard Cite

Continuous glucose monitoring (CGM) sensors are portable devices that allow measuring and visualizing the glucose concentration in real time almost continuously for several days and are provided with hypo/hyperglycemic alerts and glucose trend information. CGM sensors have revolutionized Type 1 diabetes (T1D) management, improving glucose control when used adjunctively to self-monitoring blood glucose systems. Furthermore, CGM devices have stimulated the development of applications that were impossible to create without a continuous-time glucose signal, e.g., real-time predictive alerts of hypo/hyperglycemic episodes based on the prediction of future glucose concentration, automatic basal insulin attenuation methods for hypoglycemia prevention, and the artificial pancreas. However, CGM sensors’ lack of accuracy and reliability limited their usability in the clinical practice, calling upon the academic community for the development of suitable signal processing methods to improve CGM performance. The aim of this paper is to review the past and present algorithmic challenges of CGM sensors, to show how they have been tackled by our research group, and to identify the possible future ones.

show abstract

Empirical Representation of Blood Glucose Variability in a Compartmental Model

Cited by 36 publications

References 15 publications

Continuous Glucose Monitoring and Insulin Informed Advisory System with Automated Titration and Dosing of Insulin Reduces Glucose Variability in Type 1 Diabetes Mellitus

Continuous Glucose Monitoring and Insulin Informed Advisory System with Automated Titration and Dosing of Insulin Reduces Glucose Variability in Type 1 Diabetes Mellitus

Role of Automated Insulin Delivery (Artificial Pancreas) in Islet Transplantation: An In Silico Assessment

Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges

Contact Info

Product

Resources

About