Trial Outcomes & Findings for Outpatient Pump Shutoff Pilot Feasibility and Efficacy Study (NCT NCT01591681)

NCT ID: NCT01591681

Last Updated: 2016-08-30

Results Overview

Each night is categorized as to whether hypoglycemia occurred. Hypoglycemia is defined as the occurrence of one or more CGM glucose values ≤60 mg/dL. The percentage of hypoglycemic nights will be tabulated separately with versus without the closed-loop control system in use. A repeated measures logistic regression model will be used to compare intervention versus control nights accounting for correlated data from the same subject and adjusting for the baseline (bedtime) sensor glucose.

• Recruitment status: COMPLETED
• Study phase: PHASE2
• Target enrollment: 49 participants
• Primary outcome timeframe: Overnight from system activation to deactivation in the morning upon awakening for 42 nights of system use
• Results posted on: 2016-08-30

Participant Flow

The study was conducted at Stanford University (Stanford, CA), the Barbara Davis Center (Denver, CO), and St. Joseph's Health Care (London, ON). A total of 49 subjects were enrolled between November 1, 2012 and February 20, 2013. Forty-five participants age 15-45 years old with type 1 diabetes (median HbA1c 6.8%) completed the trial.

After the run-in phase, a 42-night randomized trial was conducted in which each night was randomly assigned to have either predictive low glucose suspend system active (intervention) or inactive (control) with half of the nights being intervention and half control nights.

Participant milestones

Measure	Randomized Nights- Treatment or Control Each study night will be randomized to have either Predictive Low Glucose Suspend or to be inactive (control). On nights randomized to the intervention treatment, the study laptop will be running actively during the night and suspending the patient's pump if the algorithm predicts hypoglycemia based on the patient's continuous glucose sensor trend. (Pump suspension : The study laptop will communicate to the pump causing suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.) On control nights, the algorithm will run passively and not recommend suspensions or resumption to the patient's pump.
Overall Study STARTED	49
Overall Study COMPLETED	45
Overall Study NOT COMPLETED	4

Reasons for withdrawal

Measure	Randomized Nights- Treatment or Control Each study night will be randomized to have either Predictive Low Glucose Suspend or to be inactive (control). On nights randomized to the intervention treatment, the study laptop will be running actively during the night and suspending the patient's pump if the algorithm predicts hypoglycemia based on the patient's continuous glucose sensor trend. (Pump suspension : The study laptop will communicate to the pump causing suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.) On control nights, the algorithm will run passively and not recommend suspensions or resumption to the patient's pump.
Overall Study Didn't meet min hypoglycemia requirement	3
Overall Study Failed to use system during run-in	1

Baseline Characteristics

Outpatient Pump Shutoff Pilot Feasibility and Efficacy Study

Baseline characteristics by cohort

Measure	Randomized Nights- Treatment or Control n=45 Participants Each study night will be randomized to have either Predictive Low Glucose Suspend or to be inactive (control). On nights randomized to the intervention treatment, the study laptop will be running actively during the night and suspending the patient's pump if the algorithm predicts hypoglycemia based on the patient's continuous glucose sensor trend. (Pump suspension : The study laptop will communicate to the pump causing suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.) On control nights, the algorithm will run passively and not recommend suspensions or resumption to the patient's pump.
Age, Continuous	30 years n=5 Participants
Sex: Female, Male Female	24 Participants n=5 Participants
Sex: Female, Male Male	21 Participants n=5 Participants
Race/Ethnicity, Customized White non-Hispanic	42 participants n=5 Participants
Race/Ethnicity, Customized Hispanic	2 participants n=5 Participants
Race/Ethnicity, Customized Black	1 participants n=5 Participants
Weight	70 kg n=5 Participants
Height	172 cm n=5 Participants
Body-mass index	24 kg/m^2 n=5 Participants
Glycated hemoglobin	6.8 percent n=5 Participants
Diabetes duration	15 years n=5 Participants
Daily insulin dose	0.64 U/kg/day n=5 Participants

PRIMARY outcome

Timeframe: Overnight from system activation to deactivation in the morning upon awakening for 42 nights of system use

Each night is categorized as to whether hypoglycemia occurred. Hypoglycemia is defined as the occurrence of one or more CGM glucose values ≤60 mg/dL. The percentage of hypoglycemic nights will be tabulated separately with versus without the closed-loop control system in use. A repeated measures logistic regression model will be used to compare intervention versus control nights accounting for correlated data from the same subject and adjusting for the baseline (bedtime) sensor glucose.

Outcome measures

Measure	Pump Suspension Algorithm n=942 Number of Nights Analyzed The study laptop will be running actively during the night and suspending the patient's pump if the algorithm predicts hypoglycemia based on the patient's continuous glucose sensor trend. Pump suspension: The study laptop will communicate to the pump causing a suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=970 Number of Nights Analyzed The control algorithm will run passively and not recommend control the patient's pump.
Hypoglycemia Outcome: Percentage of Nights With Sensor Glucose Value </=60 mg/dl	21 percentage of nights	33 percentage of nights

SECONDARY outcome

Timeframe: Overnight from system activation to deactivation in the morning upon awakening for 42 nights of system use

The median percentages of the number of glucose values with values of 71-180 mg/dL overall.

Outcome measures

Measure	Pump Suspension Algorithm n=942 Number of Nights Analyzed The study laptop will be running actively during the night and suspending the patient's pump if the algorithm predicts hypoglycemia based on the patient's continuous glucose sensor trend. Pump suspension: The study laptop will communicate to the pump causing a suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=970 Number of Nights Analyzed The control algorithm will run passively and not recommend control the patient's pump.
Percentage of Sensor Glucose Values 71 to 180 mg/dL	82 percentage glucose values Interval 54.0 to 99.0	75 percentage glucose values Interval 46.0 to 93.0

SECONDARY outcome

Timeframe: Overnight from system activation to deactivation in the morning upon awakening for 42 nights of system use

Outcome measures

Measure	Pump Suspension Algorithm n=942 Number of Nights Analyzed The study laptop will be running actively during the night and suspending the patient's pump if the algorithm predicts hypoglycemia based on the patient's continuous glucose sensor trend. Pump suspension: The study laptop will communicate to the pump causing a suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=970 Number of Nights Analyzed The control algorithm will run passively and not recommend control the patient's pump.
Percentage of Nights With a Sensor Glucose Value </= 70 mg/dL	32 percentage of nights	45 percentage of nights

SECONDARY outcome

Timeframe: Overnight from system activation to deactivation in the morning upon awakening for 42 nights of system use

Outcome measures

Measure	Pump Suspension Algorithm n=942 Number of Nights Analyzed The study laptop will be running actively during the night and suspending the patient's pump if the algorithm predicts hypoglycemia based on the patient's continuous glucose sensor trend. Pump suspension: The study laptop will communicate to the pump causing a suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=970 Number of Nights Analyzed The control algorithm will run passively and not recommend control the patient's pump.
Proportion of Nights With a Sensor Glucose Value </= 50 mg/dL	10 percentage of nights	19 percentage of nights

SECONDARY outcome

Timeframe: 42 mornings following night of system use

Measured with a study home blood glucose meter.

Outcome measures

Measure	Pump Suspension Algorithm n=942 Number of Mornings Analyzed The study laptop will be running actively during the night and suspending the patient's pump if the algorithm predicts hypoglycemia based on the patient's continuous glucose sensor trend. Pump suspension: The study laptop will communicate to the pump causing a suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=970 Number of Mornings Analyzed The control algorithm will run passively and not recommend control the patient's pump.
Median Morning Blood Glucose	144 mg/dl Interval 114.0 to 186.0	129 mg/dl Interval 96.0 to 173.0

SECONDARY outcome

Timeframe: 42 mornings following night of system use

Measured with a study home blood glucose meter.

Outcome measures

Measure	Pump Suspension Algorithm n=942 Number of Mornings Analyzed The study laptop will be running actively during the night and suspending the patient's pump if the algorithm predicts hypoglycemia based on the patient's continuous glucose sensor trend. Pump suspension: The study laptop will communicate to the pump causing a suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=970 Number of Mornings Analyzed The control algorithm will run passively and not recommend control the patient's pump.
Percent of Mornings With Glucose >250 mg/dL	6 percentage of mornings	6 percentage of mornings

SECONDARY outcome

Timeframe: 42 mornings following night of system use

Blood ketones measured with a study blood ketone meter.

Outcome measures

Measure	Pump Suspension Algorithm n=942 Number of Mornings Analyzed The study laptop will be running actively during the night and suspending the patient's pump if the algorithm predicts hypoglycemia based on the patient's continuous glucose sensor trend. Pump suspension: The study laptop will communicate to the pump causing a suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=970 Number of Mornings Analyzed The control algorithm will run passively and not recommend control the patient's pump.
Percent of Mornings With Blood Ketones >1.0 mmol/L	0.1 percentage of mornings	0.3 percentage of mornings

SECONDARY outcome

Timeframe: 42 mornings following night of system use

Urine ketones measured each morning with Ketostix.

Outcome measures

Measure	Pump Suspension Algorithm n=942 Number of Mornings Analyzed The study laptop will be running actively during the night and suspending the patient's pump if the algorithm predicts hypoglycemia based on the patient's continuous glucose sensor trend. Pump suspension: The study laptop will communicate to the pump causing a suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=970 Number of Mornings Analyzed The control algorithm will run passively and not recommend control the patient's pump.
Percent of Mornings With Urine Ketones >/= 15 mg/dl	3 percentage of mornings	2 percentage of mornings

SECONDARY outcome

Timeframe: Overnight from system activation to deactivation in the morning upon awakening for 42 nights of system use

Calculated as the median of the overall mean.

Outcome measures

Measure	Pump Suspension Algorithm n=942 Number of Nights Analyzed The study laptop will be running actively during the night and suspending the patient's pump if the algorithm predicts hypoglycemia based on the patient's continuous glucose sensor trend. Pump suspension: The study laptop will communicate to the pump causing a suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=970 Number of Nights Analyzed The control algorithm will run passively and not recommend control the patient's pump.
Overall Mean Sensor Glucose Overnight	132 mg/dl Interval 110.0 to 163.0	125 mg/dl Interval 98.0 to 163.0

SECONDARY outcome

Timeframe: Overnight from system activation to deactivation in the morning upon awakening for 42 nights of system use

The measure is reporting area under the curve for glucose concentrations below 250 mg/dL and above 60 mg/dL. Overall time below and above a threshold and area under a curve was divided by total time and multiplied by 8 hours.

Outcome measures

Measure	Pump Suspension Algorithm n=942 Number of Nights Analyzed The study laptop will be running actively during the night and suspending the patient's pump if the algorithm predicts hypoglycemia based on the patient's continuous glucose sensor trend. Pump suspension: The study laptop will communicate to the pump causing a suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=970 Number of Nights Analyzed The control algorithm will run passively and not recommend control the patient's pump.
Overnight Area Under the Curve 250 mg/dl Per 8 Hour	219 mg*hr/dL Interval 72.0 to 666.0	236 mg*hr/dL Interval 83.0 to 772.0

SECONDARY outcome

Timeframe: Overnight from system activation to deactivation in the morning upon awakening for 42 nights of system use

Outcome measures

Measure	Pump Suspension Algorithm n=942 Number of Nights Analyzed The study laptop will be running actively during the night and suspending the patient's pump if the algorithm predicts hypoglycemia based on the patient's continuous glucose sensor trend. Pump suspension: The study laptop will communicate to the pump causing a suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=970 Number of Nights Analyzed The control algorithm will run passively and not recommend control the patient's pump.
Percent of Nights With Sensor Glucose >250 mg/dL	20 percentage of nights	20 percentage of nights

Adverse Events

Pump Suspension Algorithm

Serious events: 0 serious events

Other events: 0 other events

Deaths: 0 deaths

Standard of Care

Serious events: 0 serious events

Other events: 0 other events

Deaths: 0 deaths

Serious adverse events

Adverse event data not reported

Other adverse events

Adverse event data not reported

Additional Information

Roy W. Beck, M.D., Ph.D.

Jaeb Center for Health Research

Phone: 813-975-8690

Email: jdrfapp2@jaeb.org

Results disclosure agreements

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