New Strategies for Postprandial Glycemic Control Using Insulin Pump Therapy
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|ClinicalTrials.gov Identifier: NCT01550809|
Recruitment Status : Completed
First Posted : March 12, 2012
Results First Posted : August 29, 2012
Last Update Posted : August 29, 2012
Achieving near-normoglycemia has been established as the main objective for most patients with type 1 diabetes (T1DM). However, insulin dosing is an empirical process and its success is highly dependent on the patients' and physicians' skills, either with multiple daily injections (MDI) or with continuous subcutaneous insulin infusion (CSII, the gold standard of insulin treatment).
Postprandial glucose control is one of the most challenging issues in the everyday diabetes care. Indeed, postprandial glucose excursions are the major contributors to plasma glucose (PG) variability of subjects with (T1DM) and the poor reproducibility of postprandial glucose response is burdensome for both patients and healthcare professionals.
During the past 10-15 years, there has been an exponentially increasing intrusion of technology into diabetes care with the expectation of making life easier for patients with diabetes. Some tools have been developed to aid patients in the prandial bolus decision-making process, i.e. "bolus advisors", which have been implemented in insulin pumps and more recently in the newest generations of glucometers. Currently, the availability of continuous glucose monitoring (CGM) has opened new scenarios for improving glycemic control and increasing understanding of post-prandial glycemic response in patients with diabetes.
Results from clinical studies suggest that sensor-augmented pumps (SAP)may be effective in improving metabolic control, especially when included as part of structured educational programs resulting in patients' empowerment. Similarly, preliminary results from pilot studies indicate that automated glycemic control, especially during nighttime,based on information from CGM is feasible. However, automatic management of meal bolus is currently one of the main challenges found in clinical validations of the few existing prototypes of an artificial pancreas. Indeed, fully closed-loop systems where information about meals size and timing is not given to the system have shown poor performance, with postprandial glucose higher and post meal nadir glucose lower than desired. This has promoted other less-ambitious approaches, where prandial insulin is administered following meal announcement (semi closed-loop). However, despite the use of meal announcement, currently used algorithms for glucose control (the so-called PID and MPC), show results that are not yet satisfactory due to the risk of producing hypoglycemia.
One of the limitations of the current open-loop (bolus advisors) and closed-loop control strategies is that glycemic variability is not taken into account. As an example, settings of CSII consider inter-individual variation of the parameters (insulin/carbohydrates ratio, correction dose, etc.) but disregard the day-to-day intra-individual variability of postprandial glucose response. Availability of massive amount of information from CGM, together with mathematic tools, may allow for the characterization of the individual variability and the development of strategies to cope with the uncertainty of the glycemic response to a meal.
In this project, a rigorous clinical testing of a CGM-based, user-independent algorithm for prandial insulin administration will be carried out in type 1 diabetic patients treated with insulin CSII.
First of all, an individual patient's model characterizing a 5-hour postprandial period will be obtained from a 6-day CGM period. The model will account for a 20% uncertainty in insulin sensitivity and 10% variability in the estimation of the ingested carbohydrates. Based on this model (derived from CGM), a mealtime insulin dose will be calculated (referred as iBolus). Then, the same subjects will undergo standardized meal test studies comparing the administration of a traditional bolus (tBolus, based on insulin to CHO ratio, correction factor, etc.) with the CGM-based prandial insulin delivery (iBolus).
Significant advances in postprandial control are expected. Should its efficiency be demonstrated clinically, the method could be incorporated in advanced sensor augmented pumps as well as feedforward action in closed-loop control algorithms for the artificial pancreas, in future work.
|Condition or disease||Intervention/treatment||Phase|
|Type 1 Diabetes||Other: iBolus Other: tBolus (traditional bolus)||Phase 3|
Show Detailed Description
|Study Type :||Interventional (Clinical Trial)|
|Actual Enrollment :||12 participants|
|Intervention Model:||Crossover Assignment|
|Masking:||Double (Participant, Investigator)|
|Official Title:||New Strategies for Postprandial Glycemic Control Using Insulin Pump Therapy: Feasibility of Insulin Dosing Based on Information From Continuous Glucose Monitoring|
|Study Start Date :||February 2010|
|Actual Primary Completion Date :||June 2011|
|Actual Study Completion Date :||June 2011|
Active Comparator: tBolus (traditional bolus)
Traditional mealtime insulin bolus based on the individual insulin-to-CHO ratio
Other: tBolus (traditional bolus)
Insulin bolus dose calculated using the standard procedure based on the insulin-to-carbohydrate ratio
Experimental: iBolus (CGM-based insulin administration)
This is a CGM-based algorithm for prandial insulin administration. An individual patient's model characterizing a 5-hour postprandial period (0-5h PP) is obtained from a 6-day CGM period. A model with interval parameters accounting for patient's variability is calculated considering 20% uncertainty in insulin sensitivity and 10% in carbohydrates (CHO) estimation. Based on this model, constraints on plasma glucose are posed and a set-inversion problem lead to a set of solutions (the iBolus) that contains a bolus insulin dose, a specific mealtime basal insulin dose and the time for restoration of basal to baseline values.
Insulin bolus calculated from data obtained through CGM
- The Area Under the Curve (AUC) of Plasma Glucose (PG) Concentrations During the 5-hour Postprandial Period (AUC-PG0-5 h). [ Time Frame: The whole experiment, i.e. 5 hours ]
AUC-PG0-5 h (5-hour postprandial glucose following the mixed meal test) is a measure of the overall glucose-lowering efficacy of the insulin bolus. The lower the AUC-PG0-5 h without hypoglycemia, the greater the effectiveness of the prandial insulin administration to control the meal related glucose excursion.
Plasma glucose (PG) for calculation of AUC-PG was measured every 15 minutes following the insulin administration and during the whole 5-hour postprandial period (300 minutes).
- The Area Under the Curve (AUC) of the Glucose Infusion Rate (GIR) During the 5-hour Postprandial Period (AUC-GIR0-5h). [ Time Frame: The whole experiment, i.e. 5 hours. ]
The amount of glucose infused during the 5-hour postprandial period (AUC-GIR0-5h) is a measure of the hypoglycemic exposure associated with the modality of prandial insulin administration. Indeed, glucose will be infused only when patients are under a predefined blood glucose values (80 mg/dl) with a descending trend.
Glucose infusion rate (GIR) for calculation of AUC-GIR was measured every minute following the insulin administration and during the whole 5-hour postprandial period (300 minutes).
- The Area Under the Curve (AUC) of Plasma Glucose (PG) Above the Threshold of 140 mg/dl (AUC-PG>140). [ Time Frame: The whole experiment, i.e. the 5-hour postprandial period ]
The AUC-PG>140 during the 5-hour period following the meal test represents the hyperglycemic risk related to the modality of prandial insulin administration.
Plasma glucose (PG) for calculation of AUC-PG>140 was measured every 15 minutes following the insulin administration and during the whole 5-hour postprandial period (300 minutes).
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT01550809
|Hospital Clínico Universitario|
|Valencia, Spain, 46010|
|Principal Investigator:||Francisco Javier Ampudia-Blasco, MD, PhD||Fundación INCLIVA, Hospital Clínico Universitario de Valencia|