Trial Outcomes & Findings for Two Way Crossover Closed Loop Study R-AP vs MPC (NCT NCT05083559)

NCT ID: NCT05083559

Last Updated: 2023-01-20

Results Overview

Incremental AUC of postprandial glucose in the 4 hours following the start of first meal. AUC (mg/dL\*hr) will be calculated using a trapezoidal method, which sums all CGM values taken every 5 minutes in the 4 hour period following the meal above the starting glucose. This yields a maximum of 48 data points for the calculation.

• Recruitment status: COMPLETED
• Study phase: NA
• Target enrollment: 15 participants
• Primary outcome timeframe: 4 hour period following the first meal
• Results posted on: 2023-01-20

Participant Flow

Participant milestones

Measure	MPC to R-AP The randomization order for participants in this arm was Model Predictive Control (MPC) followed by Robust-Artificial Pancreas (R-AP). Each visit was about 8 hours long.	R-AP to MPC The randomization order for participants in this arm was Robust-Artificial Pancreas (R-AP followed by Model Predictive Control (MPC). Each visit was about 8 hours long.
Dexcom Training Visit (30 Minutes) STARTED	7	8
Dexcom Training Visit (30 Minutes) COMPLETED	7	8
Dexcom Training Visit (30 Minutes) NOT COMPLETED	0	0
First Rest Period (1-12 Weeks) STARTED	7	8
First Rest Period (1-12 Weeks) COMPLETED	7	8
First Rest Period (1-12 Weeks) NOT COMPLETED	0	0
First Intervention (~8 Hours) STARTED	7	8
First Intervention (~8 Hours) COMPLETED	3	7
First Intervention (~8 Hours) NOT COMPLETED	4	1
Second at Home Period (3-30 Days) STARTED	7	8
Second at Home Period (3-30 Days) COMPLETED	5	8
Second at Home Period (3-30 Days) NOT COMPLETED	2	0
Second Intervention Visit (~8 Hours) STARTED	5	8
Second Intervention Visit (~8 Hours) COMPLETED	4	8
Second Intervention Visit (~8 Hours) NOT COMPLETED	1	0

Reasons for withdrawal

Measure	MPC to R-AP The randomization order for participants in this arm was Model Predictive Control (MPC) followed by Robust-Artificial Pancreas (R-AP). Each visit was about 8 hours long.	R-AP to MPC The randomization order for participants in this arm was Robust-Artificial Pancreas (R-AP followed by Model Predictive Control (MPC). Each visit was about 8 hours long.
First Intervention (~8 Hours) hit stopping rules for hyperglycemia	4	1
Second at Home Period (3-30 Days) Withdrawal by Subject	2	0
Second Intervention Visit (~8 Hours) hit stopping rules for hyperglycemia	1	0

Baseline Characteristics

Two Way Crossover Closed Loop Study R-AP vs MPC

Baseline characteristics by cohort

Measure	MPC to R-AP n=7 Participants The randomization order for participants in this arm was Model Predictive Control (MPC) followed by Robust-Artificial Pancreas (R-AP). Each visit was about 72 hours and included a 12 hour inpatient and 60 hour outpatient portion.	R-AP to MPC n=8 Participants The randomization order for participants in this arm was Robust-Artificial Pancreas (R-AP followed by Model Predictive Control (MPC). Each visit was about 72 hours and included a 12 hour inpatient and 60 hour outpatient portion.	Total n=15 Participants Total of all reporting groups
Age, Categorical <=18 years	0 Participants n=5 Participants	0 Participants n=7 Participants	0 Participants n=5 Participants
Age, Categorical Between 18 and 65 years	7 Participants n=5 Participants	8 Participants n=7 Participants	15 Participants n=5 Participants
Age, Categorical >=65 years	0 Participants n=5 Participants	0 Participants n=7 Participants	0 Participants n=5 Participants
Age, Continuous	41.6 years STANDARD_DEVIATION 10.1 • n=5 Participants	34.1 years STANDARD_DEVIATION 9.9 • n=7 Participants	37.6 years STANDARD_DEVIATION 10.4 • n=5 Participants
Sex: Female, Male Female	3 Participants n=5 Participants	6 Participants n=7 Participants	9 Participants n=5 Participants
Sex: Female, Male Male	4 Participants n=5 Participants	2 Participants n=7 Participants	6 Participants n=5 Participants
Ethnicity (NIH/OMB) Hispanic or Latino	0 Participants n=5 Participants	0 Participants n=7 Participants	0 Participants n=5 Participants
Ethnicity (NIH/OMB) Not Hispanic or Latino	7 Participants n=5 Participants	8 Participants n=7 Participants	15 Participants n=5 Participants
Ethnicity (NIH/OMB) Unknown or Not Reported	0 Participants n=5 Participants	0 Participants n=7 Participants	0 Participants n=5 Participants
Race (NIH/OMB) American Indian or Alaska Native	0 Participants n=5 Participants	1 Participants n=7 Participants	1 Participants n=5 Participants
Race (NIH/OMB) Asian	0 Participants n=5 Participants	0 Participants n=7 Participants	0 Participants n=5 Participants
Race (NIH/OMB) Native Hawaiian or Other Pacific Islander	0 Participants n=5 Participants	0 Participants n=7 Participants	0 Participants n=5 Participants
Race (NIH/OMB) Black or African American	0 Participants n=5 Participants	1 Participants n=7 Participants	1 Participants n=5 Participants
Race (NIH/OMB) White	7 Participants n=5 Participants	6 Participants n=7 Participants	13 Participants n=5 Participants
Race (NIH/OMB) More than one race	0 Participants n=5 Participants	0 Participants n=7 Participants	0 Participants n=5 Participants
Race (NIH/OMB) Unknown or Not Reported	0 Participants n=5 Participants	0 Participants n=7 Participants	0 Participants n=5 Participants
Region of Enrollment United States	7 participants n=5 Participants	8 participants n=7 Participants	15 participants n=5 Participants

PRIMARY outcome

Timeframe: 4 hour period following the first meal

Incremental AUC of postprandial glucose in the 4 hours following the start of first meal. AUC (mg/dL*hr) will be calculated using a trapezoidal method, which sums all CGM values taken every 5 minutes in the 4 hour period following the meal above the starting glucose. This yields a maximum of 48 data points for the calculation.

Outcome measures

Measure	MPC AP System n=13 Participants Participants will use the MPC AP system for automated insulin delivery for a 9 hour study visit. MPC AP algorithm: The Model Predictive Control (MPC) insulin infusion algorithm contains a model within the controller that takes as an input the aerobic metabolic expenditure in addition to the CGM and meal inputs. The algorithm uses heart rate and accelerometer data collected on the patient's body to calculate metabolic expenditure. The metabolic expenditure then acts on the model for the insulin dynamics, whereby more energy expenditure and longer duration exercise can lead to a more substantial effect of insulin on the CGM.	Robust R-AP System n=13 Participants Participants will use the Robust R-AP system for automated insulin delivery for a 9 hour study visit. Robust R-AP algorithm: The R-AP is a modified MPC algorithm. A new feature in the algorithm includes a model for missed meal insulin detection. The model includes estimations for carbohydrate consumption based glucose patterns to determine if that person has consumed a meal without announcing it to the system.
Area Under the Curve (AUC) of Postprandial Glucose	277.5 mg/dL*hr Standard Deviation 159.4	253.9 mg/dL*hr Standard Deviation 105.9

PRIMARY outcome

Timeframe: 4 hour period following the first meal

Assess the percent of time that the Dexcom G6 reported sensor glucose values between 70-180 mg/dl using Dexcom sensor for the four hour period following the first meal.

Outcome measures

Measure	MPC AP System n=13 Participants Participants will use the MPC AP system for automated insulin delivery for a 9 hour study visit. MPC AP algorithm: The Model Predictive Control (MPC) insulin infusion algorithm contains a model within the controller that takes as an input the aerobic metabolic expenditure in addition to the CGM and meal inputs. The algorithm uses heart rate and accelerometer data collected on the patient's body to calculate metabolic expenditure. The metabolic expenditure then acts on the model for the insulin dynamics, whereby more energy expenditure and longer duration exercise can lead to a more substantial effect of insulin on the CGM.	Robust R-AP System n=13 Participants Participants will use the Robust R-AP system for automated insulin delivery for a 9 hour study visit. Robust R-AP algorithm: The R-AP is a modified MPC algorithm. A new feature in the algorithm includes a model for missed meal insulin detection. The model includes estimations for carbohydrate consumption based glucose patterns to determine if that person has consumed a meal without announcing it to the system.
Percent of Time With Sensed Glucose Between 70-180 mg/dl	24.3 percentage of time Standard Deviation 20.1	33.4 percentage of time Standard Deviation 22.8

SECONDARY outcome

Timeframe: 4 hour period following the first meal

Assess the percent of time that the Dexcom G6 reported sensor glucose values less than 70 mg/dl using Dexcom sensor for the 4 hour period following the first meal.

Outcome measures

Measure	MPC AP System n=13 Participants Participants will use the MPC AP system for automated insulin delivery for a 9 hour study visit. MPC AP algorithm: The Model Predictive Control (MPC) insulin infusion algorithm contains a model within the controller that takes as an input the aerobic metabolic expenditure in addition to the CGM and meal inputs. The algorithm uses heart rate and accelerometer data collected on the patient's body to calculate metabolic expenditure. The metabolic expenditure then acts on the model for the insulin dynamics, whereby more energy expenditure and longer duration exercise can lead to a more substantial effect of insulin on the CGM.	Robust R-AP System n=13 Participants Participants will use the Robust R-AP system for automated insulin delivery for a 9 hour study visit. Robust R-AP algorithm: The R-AP is a modified MPC algorithm. A new feature in the algorithm includes a model for missed meal insulin detection. The model includes estimations for carbohydrate consumption based glucose patterns to determine if that person has consumed a meal without announcing it to the system.
Percent of Time With Sensed Glucose <70 mg/dl	0 percentage of time Standard Deviation 0	1.7 percentage of time Standard Deviation 6.0

SECONDARY outcome

Timeframe: 4 hour period following the first meal

Assess the cumulative number of carbohydrate treatments (defined as 15 or 20 grams of carbohydrate) for the four hour period following the first meal.

Outcome measures

Measure	MPC AP System n=13 Participants Participants will use the MPC AP system for automated insulin delivery for a 9 hour study visit. MPC AP algorithm: The Model Predictive Control (MPC) insulin infusion algorithm contains a model within the controller that takes as an input the aerobic metabolic expenditure in addition to the CGM and meal inputs. The algorithm uses heart rate and accelerometer data collected on the patient's body to calculate metabolic expenditure. The metabolic expenditure then acts on the model for the insulin dynamics, whereby more energy expenditure and longer duration exercise can lead to a more substantial effect of insulin on the CGM.	Robust R-AP System n=13 Participants Participants will use the Robust R-AP system for automated insulin delivery for a 9 hour study visit. Robust R-AP algorithm: The R-AP is a modified MPC algorithm. A new feature in the algorithm includes a model for missed meal insulin detection. The model includes estimations for carbohydrate consumption based glucose patterns to determine if that person has consumed a meal without announcing it to the system.
Number of Carbohydrate Treatments	0 carbohydrate treatments	2 carbohydrate treatments

SECONDARY outcome

Timeframe: 4 hour period following the first meal

Assess the cumulative number of provider-administered insulin injections to treat hyperglycemia in the 4 hour period following the first meal.

Outcome measures

Measure	MPC AP System n=13 Participants Participants will use the MPC AP system for automated insulin delivery for a 9 hour study visit. MPC AP algorithm: The Model Predictive Control (MPC) insulin infusion algorithm contains a model within the controller that takes as an input the aerobic metabolic expenditure in addition to the CGM and meal inputs. The algorithm uses heart rate and accelerometer data collected on the patient's body to calculate metabolic expenditure. The metabolic expenditure then acts on the model for the insulin dynamics, whereby more energy expenditure and longer duration exercise can lead to a more substantial effect of insulin on the CGM.	Robust R-AP System n=13 Participants Participants will use the Robust R-AP system for automated insulin delivery for a 9 hour study visit. Robust R-AP algorithm: The R-AP is a modified MPC algorithm. A new feature in the algorithm includes a model for missed meal insulin detection. The model includes estimations for carbohydrate consumption based glucose patterns to determine if that person has consumed a meal without announcing it to the system.
Number of Provider-administered Insulin Injections	4 insulin injections	2 insulin injections

SECONDARY outcome

Timeframe: 4 hour period following the first meal

Assess the mean sensed glucose from the Dexcom G6 reported sensor glucose values for the four hour period following the first meal.

Outcome measures

Measure	MPC AP System n=13 Participants Participants will use the MPC AP system for automated insulin delivery for a 9 hour study visit. MPC AP algorithm: The Model Predictive Control (MPC) insulin infusion algorithm contains a model within the controller that takes as an input the aerobic metabolic expenditure in addition to the CGM and meal inputs. The algorithm uses heart rate and accelerometer data collected on the patient's body to calculate metabolic expenditure. The metabolic expenditure then acts on the model for the insulin dynamics, whereby more energy expenditure and longer duration exercise can lead to a more substantial effect of insulin on the CGM.	Robust R-AP System n=13 Participants Participants will use the Robust R-AP system for automated insulin delivery for a 9 hour study visit. Robust R-AP algorithm: The R-AP is a modified MPC algorithm. A new feature in the algorithm includes a model for missed meal insulin detection. The model includes estimations for carbohydrate consumption based glucose patterns to determine if that person has consumed a meal without announcing it to the system.
Mean Sensed Glucose	230.8 mg/dL Standard Deviation 43.6	212.6 mg/dL Standard Deviation 49.6

SECONDARY outcome

Timeframe: 4 hour period following the first meal

Assess the percent of time that the Dexcom G6 reported sensor glucose values less than 54 mg/dl using Dexcom sensor for the four hour period following the first meal.

Outcome measures

Measure	MPC AP System n=13 Participants Participants will use the MPC AP system for automated insulin delivery for a 9 hour study visit. MPC AP algorithm: The Model Predictive Control (MPC) insulin infusion algorithm contains a model within the controller that takes as an input the aerobic metabolic expenditure in addition to the CGM and meal inputs. The algorithm uses heart rate and accelerometer data collected on the patient's body to calculate metabolic expenditure. The metabolic expenditure then acts on the model for the insulin dynamics, whereby more energy expenditure and longer duration exercise can lead to a more substantial effect of insulin on the CGM.	Robust R-AP System n=13 Participants Participants will use the Robust R-AP system for automated insulin delivery for a 9 hour study visit. Robust R-AP algorithm: The R-AP is a modified MPC algorithm. A new feature in the algorithm includes a model for missed meal insulin detection. The model includes estimations for carbohydrate consumption based glucose patterns to determine if that person has consumed a meal without announcing it to the system.
Percent of Time With Sensed Glucose <54 mg/dl	0 percentage of time Standard Deviation 0	0.8 percentage of time Standard Deviation 2.7

SECONDARY outcome

Timeframe: 4 hour period following the first meal

Assess the percent of time that the Dexcom G6 reported sensor glucose values greater than 180 mg/dl using Dexcom sensor for the four hour period following the first meal.

Outcome measures

Measure	MPC AP System n=13 Participants Participants will use the MPC AP system for automated insulin delivery for a 9 hour study visit. MPC AP algorithm: The Model Predictive Control (MPC) insulin infusion algorithm contains a model within the controller that takes as an input the aerobic metabolic expenditure in addition to the CGM and meal inputs. The algorithm uses heart rate and accelerometer data collected on the patient's body to calculate metabolic expenditure. The metabolic expenditure then acts on the model for the insulin dynamics, whereby more energy expenditure and longer duration exercise can lead to a more substantial effect of insulin on the CGM.	Robust R-AP System n=13 Participants Participants will use the Robust R-AP system for automated insulin delivery for a 9 hour study visit. Robust R-AP algorithm: The R-AP is a modified MPC algorithm. A new feature in the algorithm includes a model for missed meal insulin detection. The model includes estimations for carbohydrate consumption based glucose patterns to determine if that person has consumed a meal without announcing it to the system.
Percent of Time With Sensed Glucose >180 mg/dl	75.7 percentage of time Standard Deviation 20.2	64.8 percentage of time Standard Deviation 24.1

SECONDARY outcome

Timeframe: 4 hour period following the first meal

Assess the percent of time that the Dexcom G6 reported sensor glucose values greater than 250 mg/dl using Dexcom sensor for the four hour period following the first meal.

Outcome measures

Measure	MPC AP System n=13 Participants Participants will use the MPC AP system for automated insulin delivery for a 9 hour study visit. MPC AP algorithm: The Model Predictive Control (MPC) insulin infusion algorithm contains a model within the controller that takes as an input the aerobic metabolic expenditure in addition to the CGM and meal inputs. The algorithm uses heart rate and accelerometer data collected on the patient's body to calculate metabolic expenditure. The metabolic expenditure then acts on the model for the insulin dynamics, whereby more energy expenditure and longer duration exercise can lead to a more substantial effect of insulin on the CGM.	Robust R-AP System n=13 Participants Participants will use the Robust R-AP system for automated insulin delivery for a 9 hour study visit. Robust R-AP algorithm: The R-AP is a modified MPC algorithm. A new feature in the algorithm includes a model for missed meal insulin detection. The model includes estimations for carbohydrate consumption based glucose patterns to determine if that person has consumed a meal without announcing it to the system.
Percent of Time With Sensed Glucose >250 mg/dl	36.9 percentage of time Standard Deviation 26.5	31.6 percentage of time Standard Deviation 26.0

SECONDARY outcome

Timeframe: 4 hour period following the first meal

Assess the mean amount of insulin delivered per day by the Omnipod through the AP system study in units for the four hour period following the first meal.

Outcome measures

Measure	MPC AP System n=13 Participants Participants will use the MPC AP system for automated insulin delivery for a 9 hour study visit. MPC AP algorithm: The Model Predictive Control (MPC) insulin infusion algorithm contains a model within the controller that takes as an input the aerobic metabolic expenditure in addition to the CGM and meal inputs. The algorithm uses heart rate and accelerometer data collected on the patient's body to calculate metabolic expenditure. The metabolic expenditure then acts on the model for the insulin dynamics, whereby more energy expenditure and longer duration exercise can lead to a more substantial effect of insulin on the CGM.	Robust R-AP System n=13 Participants Participants will use the Robust R-AP system for automated insulin delivery for a 9 hour study visit. Robust R-AP algorithm: The R-AP is a modified MPC algorithm. A new feature in the algorithm includes a model for missed meal insulin detection. The model includes estimations for carbohydrate consumption based glucose patterns to determine if that person has consumed a meal without announcing it to the system.
Mean Amount of Insulin Delivered Per Day (in Units)	7.6 units Standard Deviation 3.3	8.7 units Standard Deviation 3.2

Adverse Events

MPC AP System

Serious events: 0 serious events

Other events: 1 other events

Deaths: 0 deaths

Robust R-AP System

Serious events: 0 serious events

Other events: 0 other events

Deaths: 0 deaths

Serious adverse events

Adverse event data not reported

Other adverse events

Measure	MPC AP System n=15 participants at risk Participants will use the MPC AP system for automated insulin delivery for a 9 hour study visit.	Robust R-AP System n=13 participants at risk Participants will use the Robust R-AP system for automated insulin delivery for a 9 hour study visit.
Blood and lymphatic system disorders High ketones	6.7% 1/15 • Number of events 1 • 3 months All participants that began/started the Dexcom training visit or a MPC or R-AP arm are included in the adverse events. The numbers from the participant flow indicate that 15 subjects began the Dexcom training visit, 15 subjects began an MPC visit and 15 subjects began an R-AP visit.	0.00% 0/13 • 3 months All participants that began/started the Dexcom training visit or a MPC or R-AP arm are included in the adverse events. The numbers from the participant flow indicate that 15 subjects began the Dexcom training visit, 15 subjects began an MPC visit and 15 subjects began an R-AP visit.

Additional Information

Jessica Castle

Oregon Health and Science University

Phone: 503-494-7072

Email: castleje@ohsu.edu

Results disclosure agreements

• Principal investigator is a sponsor employee
• Publication restrictions are in place