Trial Outcomes & Findings for An Outpatient Pump Shutoff Pilot Feasibility and Safety Study (NCT NCT01736930)

Results Overview

The primary safety outcome will be evaluated by comparing the intervention and control nights and nights with vs. without actual shut-off of the pump for several measures of hyperglycemia such as mean morning blood glucose (mg/dL). An objective was to evaluate and refine the control algorithm. The data were reviewed periodically during the study with the pre-stated goal of determining whether any changes should be made in the control algorithm. Algorithm 1 was used for the first 105 nights of the study (38 Control nights and 67 Intervention nights). The horizon prediction time of algorithm 1 was set at 70 minutes.

Recruitment status

COMPLETED

Study phase

PHASE2

Target enrollment

20 participants

Primary outcome timeframe

21 study nights

Results posted on

2016-08-30

Participant Flow

The study was conducted at Stanford University and the Barbara Davis Center. A total of 20 subjects were enrolled between January 1, 2012 and June 30, 2012. Nineteen participants 18-56 years old with type 1 diabetes (HbA1c 6.0%-7.7%) completed the trial.

After the run-in phase, a 21-night randomized trial was conducted in which each night was randomly assigned 2:1 to have either the predictive low glucose suspend system active (intervention) or inactive (control). Participants were current users of the MiniMed Paradigm® REAL-Time Revel™ System and Sof-sensor® glucose sensor at time of enrollment.

Participant milestones

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	20
Overall Study COMPLETED	19
Overall Study NOT COMPLETED	1

Reasons for withdrawal

Withdrawal data not reported

Baseline Characteristics

An Outpatient Pump Shutoff Pilot Feasibility and Safety Study

Baseline characteristics by cohort

Baseline characteristics by cohort
Measure	Randomized Nights- Treatment or Control n=20 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, Categorical <=18 years	0 Participants n=5 Participants
Age, Categorical Between 18 and 65 years	20 Participants n=5 Participants
Age, Categorical >=65 years	0 Participants n=5 Participants
Sex: Female, Male Female	11 Participants n=5 Participants
Sex: Female, Male Male	9 Participants n=5 Participants
Race (NIH/OMB) American Indian or Alaska Native	0 Participants n=5 Participants
Race (NIH/OMB) Asian	0 Participants n=5 Participants
Race (NIH/OMB) Native Hawaiian or Other Pacific Islander	0 Participants n=5 Participants
Race (NIH/OMB) Black or African American	0 Participants n=5 Participants
Race (NIH/OMB) White	19 Participants n=5 Participants
Race (NIH/OMB) More than one race	0 Participants n=5 Participants
Race (NIH/OMB) Unknown or Not Reported	1 Participants n=5 Participants

PRIMARY outcome

Timeframe: 21 study nights

The primary safety outcome will be evaluated by comparing the intervention and control nights and nights with vs. without actual shut-off of the pump for several measures of hyperglycemia such as mean morning blood glucose (mg/dL). An objective was to evaluate and refine the control algorithm. The data were reviewed periodically during the study with the pre-stated goal of determining whether any changes should be made in the control algorithm. Algorithm 1 was used for the first 105 nights of the study (38 Control nights and 67 Intervention nights). The horizon prediction time of algorithm 1 was set at 70 minutes.

Outcome measures

Outcome measures
Measure	Predictive Pump Suspension Algorithm n=67 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 suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=38 Number of Mornings Analyzed The control algorithm will run passively and not recommend suspensions or resumption to the patient's pump.
Mean Morning Blood Glucose (mg/dL)- Algorithm 1	158 mg/dl Standard Deviation 52	125 mg/dl Standard Deviation 53

PRIMARY outcome

Timeframe: 21 study nights

The primary safety outcome will be evaluated by comparing the intervention and control nights and nights with vs. without actual shut-off of the pump for several measures of hyperglycemia such as mean morning blood glucose (mg/dL). Algorithm 1 was modified to reduce the hypoglycemia prediction horizon from 70 minutes to 50 minutes, to suspend the pump only when the continuous glucose monitor sensor glucose value was ≤ 230 mg/dl, not suspend if there was a drop of \>40 mg/dl in consecutive sensor glucose readings, and to resume insulin delivery at the first rise in sensor glucose following a suspension. There was 156 nights of study data collected (48 Control nights and 108 Intervention nights) using algorithm 2.

Outcome measures

Outcome measures
Measure	Predictive Pump Suspension Algorithm n=108 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 suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=48 Number of Mornings Analyzed The control algorithm will run passively and not recommend suspensions or resumption to the patient's pump.
Mean Morning Blood Glucose (mg/dL)- Algorithm 2	151 mg/dl Standard Deviation 57	138 mg/dl Standard Deviation 63

PRIMARY outcome

Timeframe: 21 study nights

The primary safety outcome will be evaluated by comparing the intervention and control nights and nights with vs. without actual shut-off of the pump for several measures of hyperglycemia such as mean morning blood glucose (mg/dL). The hypoglycemia prediction horizon was reduced further in algorithm 3 to 30 minutes. A total of 114 study nights (37 Control nights and 77 Intervention nights) using algorithm 3.

Outcome measures

Outcome measures
Measure	Predictive Pump Suspension Algorithm n=77 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 suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=37 Number of Mornings Analyzed The control algorithm will run passively and not recommend suspensions or resumption to the patient's pump.
Mean Morning Blood Glucose (mg/dL)- Algorithm 3	144 mg/dl Standard Deviation 48	133 mg/dl Standard Deviation 57

PRIMARY outcome

Timeframe: 21 days

The primary safety outcome will be evaluated by comparing the intervention and control nights and nights with vs. without actual shut-off of the pump for several measures of hyperglycemia such as the overall percentage of mornings glucose measured with home glucose meter \>250 mg/dL.

Outcome measures

Outcome measures
Measure	Predictive Pump Suspension Algorithm n=67 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 suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=38 Number of Mornings Analyzed The control algorithm will run passively and not recommend suspensions or resumption to the patient's pump.
Percent Morning Blood Glucose >250 mg/dL - Algorithm 1	3 percentage of mornings	3 percentage of mornings

PRIMARY outcome

Timeframe: 21 days

The primary safety outcome will be evaluated by comparing the intervention and control nights and nights with vs. without actual shut-off of the pump for several measures of hyperglycemia such as overall percentage of mornings glucose measured with home glucose meter \>250 mg/dL.

Outcome measures

Outcome measures
Measure	Predictive Pump Suspension Algorithm n=108 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 suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=48 Number of Mornings Analyzed The control algorithm will run passively and not recommend suspensions or resumption to the patient's pump.
Percent Morning Blood Glucose >250 mg/dL - Algorithm 2	6 percentage of mornings	8 percentage of mornings

PRIMARY outcome

Timeframe: 21 days

The primary safety outcome will be evaluated by comparing the intervention and control nights and nights with vs. without actual shut-off of the pump for several measures of hyperglycemia such as overall percentage of mornings glucose measured with home glucose meter \>250 mg/dL.

Outcome measures

Outcome measures
Measure	Predictive Pump Suspension Algorithm n=77 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 suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=37 Number of Mornings Analyzed The control algorithm will run passively and not recommend suspensions or resumption to the patient's pump.
Percent Morning Blood Glucose >250 mg/dL - Algorithm 3	1 percentage of mornings	3 percentage of mornings

PRIMARY outcome

Timeframe: 21 days

The primary safety outcome will be evaluated by comparing the intervention and control nights and nights with vs. without actual shut-off of the pump for several measures of hyperglycemia such as number of mornings with blood ketones \>0.6 mmol/L.

Outcome measures

Outcome measures
Measure	Predictive Pump Suspension Algorithm n=67 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 suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=38 Number of Mornings Analyzed The control algorithm will run passively and not recommend suspensions or resumption to the patient's pump.
Mornings With Blood Ketones >0.6 mmol/L - Algorithm 1	0 number of mornings	0 number of mornings

PRIMARY outcome

Timeframe: 21 days

The primary safety outcome will be evaluated by comparing the intervention and control nights and nights with vs. without actual shut-off of the pump for several measures of hyperglycemia such as number of mornings with blood ketones \>0.6 mmol/L.

Outcome measures

Outcome measures
Measure	Predictive Pump Suspension Algorithm n=108 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 suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=48 Number of Mornings Analyzed The control algorithm will run passively and not recommend suspensions or resumption to the patient's pump.
Mornings With Blood Ketones >0.6 mmol/L - Algorithm 2	3 number of mornings	1 number of mornings

PRIMARY outcome

Timeframe: 21 days

The primary safety outcome will be evaluated by comparing the intervention and control nights and nights with vs. without actual shut-off of the pump for several measures of hyperglycemia such as number of mornings with blood ketones \>0.6 mmol/L.

Outcome measures

Outcome measures
Measure	Predictive Pump Suspension Algorithm n=77 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 suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=37 Number of Mornings Analyzed The control algorithm will run passively and not recommend suspensions or resumption to the patient's pump.
Mornings With Blood Ketones >0.6 mmol/L - Algorithm 3	1 number of mornings	0 number of mornings

PRIMARY outcome

Timeframe: 21 days

The primary safety outcome will be evaluated by comparing the intervention and control nights and nights with vs. without actual shut-off of the pump for several measures of hyperglycemia such as number of mornings with urine ketones characterized as moderate or large based on measurement results of a urine dipstick test taken using Ketostix. Moderate is considered approximately 30 - 40 mg/dL and Large \>80 mg/dL.

Outcome measures

Outcome measures
Measure	Predictive Pump Suspension Algorithm n=67 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 suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=38 Number of Mornings Analyzed The control algorithm will run passively and not recommend suspensions or resumption to the patient's pump.
Mornings With Urine Ketones Characterized as Moderate or Large - Algorithm 1	0 number of mornings	1 number of mornings

PRIMARY outcome

Timeframe: 21 days

The primary safety outcome will be evaluated by comparing the intervention and control nights and nights with vs. without actual shut-off of the pump for several measures of hyperglycemia such as number of mornings with urine ketones characterized as moderate or large based on measurement results of a urine dipstick test taken using Ketostix. Moderate is considered approximately 30 - 40 mg/dL and Large \>80 mg/dL.

Outcome measures

Outcome measures
Measure	Predictive Pump Suspension Algorithm n=108 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 suspension based on output from the algorithm which predicts hypoglycemia based on the continuous glucose sensor trend.	Standard of Care n=48 Number of Mornings Analyzed The control algorithm will run passively and not recommend suspensions or resumption to the patient's pump.
Mornings With Urine Ketones Characterized as Moderate or Large - Algorithm 2	7 number of mornings	0 number of mornings

PRIMARY outcome

Timeframe: 21 days

The primary safety outcome will be evaluated by comparing the intervention and control nights and nights with vs. without actual shut-off of the pump for several measures of hyperglycemia such as number of mornings with urine ketones characterized as moderate or large based on measurement results of a urine dipstick test taken using Ketostix. Moderate is considered approximately 30 - 40 mg/dL and Large \>80 mg/dL.