Trial Outcomes & Findings for The Pediatric Artificial Pancreas Automated Initialization Trial (NCT NCT06017089)
NCT ID: NCT06017089
Last Updated: 2025-09-15
Results Overview
% of time below 54 mg/dL
COMPLETED
NA
33 participants
Baseline and Weeks 1-8
2025-09-15
Participant Flow
Did not meet screening eligibility (N=1)
Participant milestones
| Measure |
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
In this single-arm intervention trial, all participants will use the study system (t:slim X2 with Control-IQ Technology and Dexcom Continuous Glucose Monitor) in closed-loop mode for 8 weeks at home with periodic parameter adjustment driven by an AI-based Advisor system.
AI-based Advisor system: Tandem t:slim X2 with Control-IQ and t:connect mobile application and Dexcom G6 or G7 system, connected to University of Virginia (UVA) cloud-based Physician Dashboard with insulin pump parameters driven by an AI-based Advisor system.
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|---|---|
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Overall Study
STARTED
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32
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Overall Study
COMPLETED
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28
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Overall Study
NOT COMPLETED
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4
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Reasons for withdrawal
Withdrawal data not reported
Baseline Characteristics
The Pediatric Artificial Pancreas Automated Initialization Trial
Baseline characteristics by cohort
| Measure |
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=32 Participants
In this single-arm intervention trial, all participants will use the study system (t:slim X2 with Control-IQ Technology and Dexcom Continuous Glucose Monitor) in closed-loop mode for 8 weeks at home with periodic parameter adjustment driven by an AI-based Advisor system.
AI-based Advisor system: Tandem t:slim X2 with Control-IQ and t:connect mobile application and Dexcom G6 or G7 system, connected to UVA cloud-based Physician Dashboard with insulin pump parameters driven by an AI-based Advisor system.
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|---|---|
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Age, Customized
2 to <4 years
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9 Participants
n=5 Participants
|
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Age, Customized
4 to <6 years
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23 Participants
n=5 Participants
|
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Sex: Female, Male
Female
|
11 Participants
n=5 Participants
|
|
Sex: Female, Male
Male
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21 Participants
n=5 Participants
|
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Ethnicity (NIH/OMB)
Hispanic or Latino
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5 Participants
n=5 Participants
|
|
Ethnicity (NIH/OMB)
Not Hispanic or Latino
|
27 Participants
n=5 Participants
|
|
Ethnicity (NIH/OMB)
Unknown or Not Reported
|
0 Participants
n=5 Participants
|
|
Race (NIH/OMB)
American Indian or Alaska Native
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0 Participants
n=5 Participants
|
|
Race (NIH/OMB)
Asian
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3 Participants
n=5 Participants
|
|
Race (NIH/OMB)
Native Hawaiian or Other Pacific Islander
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0 Participants
n=5 Participants
|
|
Race (NIH/OMB)
Black or African American
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3 Participants
n=5 Participants
|
|
Race (NIH/OMB)
White
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22 Participants
n=5 Participants
|
|
Race (NIH/OMB)
More than one race
|
4 Participants
n=5 Participants
|
|
Race (NIH/OMB)
Unknown or Not Reported
|
0 Participants
n=5 Participants
|
|
Body Mass Index Percentile
|
76 percentage
n=5 Participants
|
|
Parent Education
High school graduate/diploma/GED
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4 Participants
n=5 Participants
|
|
Parent Education
Technical/Vocational
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1 Participants
n=5 Participants
|
|
Parent Education
Associate Degree (AA)
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4 Participants
n=5 Participants
|
|
Parent Education
College Graduate (Bachelor's Degree or Equivalent)
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9 Participants
n=5 Participants
|
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Parent Education
Advanced Degree (e.g. Masters, PhD, MD)
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14 Participants
n=5 Participants
|
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Annual Household Income
$35,000 to <$50,000
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1 Participants
n=5 Participants
|
|
Annual Household Income
$50,000 to <$75,000
|
5 Participants
n=5 Participants
|
|
Annual Household Income
$75,000 to <$100,000
|
4 Participants
n=5 Participants
|
|
Annual Household Income
$100,000 to <$200,000
|
10 Participants
n=5 Participants
|
|
Annual Household Income
≥$200,000
|
11 Participants
n=5 Participants
|
|
Annual Household Income
Unknown
|
1 Participants
n=5 Participants
|
|
Health Insurance
Private
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26 Participants
n=5 Participants
|
|
Health Insurance
Medicare
|
2 Participants
n=5 Participants
|
|
Health Insurance
Medicaid
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4 Participants
n=5 Participants
|
|
HbA1c
|
7.3 percentage of glycated hemoglobin
STANDARD_DEVIATION 0.9 • n=5 Participants
|
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Insulin Modality
MDI
|
20 Participants
n=5 Participants
|
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Insulin Modality
Pump
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12 Participants
n=5 Participants
|
|
Total Daily Insulin
|
0.61 U/kg/day
STANDARD_DEVIATION 0.21 • n=5 Participants
|
|
Number of Pump Boluses or Injections of Short-Acting Insulin per Day
|
5 Pump boluses per day
n=5 Participants
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|
Diabetic Ketoacidosis (DKA) in last 12 months
No episodes
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24 Participants
n=5 Participants
|
|
Diabetic Ketoacidosis (DKA) in last 12 months
One episodes
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8 Participants
n=5 Participants
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Severe hypoglycemia (SH) in Last 12 Months
None
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32 Participants
n=5 Participants
|
|
Severe hypoglycemia (SH) in Last 12 Months
1
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0 Participants
n=5 Participants
|
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Diabetes Duration
|
10 months
n=5 Participants
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PRIMARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
% of time below 54 mg/dL
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
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|---|---|---|
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Safety Endpoint (Tested for Non-inferiority Compared to Baseline) CGM Measured (a)
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0.3 percentage of time
Standard Deviation 0.3
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0.3 percentage of time
Standard Deviation 0.2
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PRIMARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
% of time above 250 mg/dL
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
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|---|---|---|
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Safety Endpoint (Tested for Non-inferiority Compared to Baseline) CGM Measured (b)
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13 percentage of time
Standard Deviation 10
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9 percentage of time
Standard Deviation 5
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PRIMARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
% of time in range 70-180 mg/dL
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
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|---|---|---|
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Hierarchical Efficacy Endpoints (Tested for Superiority Compared With Baseline) CGM Measured (a)
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63 percentage of time
Standard Deviation 15
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70 percentage of time
Standard Deviation 8
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PRIMARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
Mean glucose
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
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|---|---|---|
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Hierarchical Efficacy Endpoints (Tested for Superiority Compared With Baseline) CGM Measured (b)
|
168 mg/dl
Standard Deviation 28
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157 mg/dl
Standard Deviation 14
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PRIMARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
% of time \>250 mg/dL
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
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|---|---|---|
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Hierarchical Efficacy Endpoints (Tested for Superiority Compared With Baseline) CGM Measured (c)
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13 percentage of time
Standard Deviation 10
|
9 percentage of time
Standard Deviation 5
|
PRIMARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
% of time \<70 mg/dL
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
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|---|---|---|
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Hierarchical Efficacy Endpoints (Tested for Superiority Compared With Baseline) CGM Measured (d)
|
2 percentage of time
Standard Deviation 1.4
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1.7 percentage of time
Standard Deviation 0.9
|
PRIMARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
% of time \<54 mg/dL
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
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|---|---|---|
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Hierarchical Efficacy Endpoints (Tested for Superiority Compared With Baseline) CGM Measured (e)
|
0.3 percentage of time
Standard Deviation 0.3
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0.3 percentage of time
Standard Deviation 0.2
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
% of time spent within range 70 mg/dL-140 mg/dL.
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
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|---|---|---|
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CGM Measured Time in Range
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42 percentage of time
Standard Deviation 14
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47 percentage of time
Standard Deviation 9
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
% of time \>180 mg/dL
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
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|---|---|---|
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CGM Measured (a)
|
35 percentage of time
Standard Deviation 15
|
29 percentage of time
Standard Deviation 8
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
% of time \>300 mg/dL
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
|
|---|---|---|
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CGM Measured (b)
|
5.9 percentage of time
Standard Deviation 5.6
|
3.3 percentage of time
Standard Deviation 2.6
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
% of time \<60 mg/dL
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
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|---|---|---|
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CGM Measured (c)
|
0.7 percentage of time
Standard Deviation 0.5
|
0.6 percentage of time
Standard Deviation 0.4
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
Glucose standard deviation
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
|
|---|---|---|
|
CGM Measured (d)
|
64 mg/dl
Standard Deviation 16
|
59 mg/dl
Standard Deviation 12
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
Glucose coefficient of variation
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
|
|---|---|---|
|
CGM Measured (e)
|
38 percent
Standard Deviation 5
|
37 percent
Standard Deviation 5
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
The high blood glucose index (HBGI) is based on a nonlinear transformation of blood glucose values that corrects for the asymmetry of the glucose scale. This transformation maps glucose values into a risk space (minimum risk = 0), where higher values correspond to higher risk. Values below 10 suggest low to moderate risk.
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
|
|---|---|---|
|
CGM Measured (f)
|
8.8 index
Standard Deviation 5
|
6.8 index
Standard Deviation 2.5
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
The low blood glucose index (LBGI) is based on a nonlinear transformation of blood glucose values that corrects for the asymmetry of the glucose scale. This transformation maps glucose values into a risk space (minimum risk = 0), where higher values correspond to higher risk. Values \<1 suggest low risk of hypoglycemia.
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
|
|---|---|---|
|
CGM Measured (g)
|
0.6 index
Standard Deviation 0.4
|
0.6 index
Standard Deviation 0.2
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
Weekly hyperglycemic event rate
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
|
|---|---|---|
|
CGM Measured (h)
|
1.9 events
Standard Deviation 2.0
|
1.1 events
Standard Deviation 1.0
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
Weekly hypoglycemic event rate
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
|
|---|---|---|
|
CGM Measured (i)
|
0.6 events
Standard Deviation 0.6
|
0.4 events
Standard Deviation 0.4
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
Number of participants whose % of time in range 70-180 mg/dL improved by 5% or more from baseline to 8 weeks.
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
|
|---|---|---|
|
Binary Outcome 1
|
13 Participants
|
—
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
Number of participants whose % of time in range 70-180 mg/dL improved by 10% or more from baseline to 8 weeks.
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
|
|---|---|---|
|
Binary Outcome 2
|
9 Participants
|
—
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
Participants with % of time in range 70-180 mg/dL \>70% and % of time \<70 mg/dL \<4%
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=29 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
|
|---|---|---|
|
Binary Outcome 3
|
10 Participants
|
13 Participants
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
Total daily insulin (units/kg)
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=27 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
|
|---|---|---|
|
Total Daily Insulin
|
0.59 U/kg
Standard Deviation 0.20
|
0.66 U/kg
Standard Deviation 0.19
|
SECONDARY outcome
Timeframe: Baseline and Weeks 1-8Population: Three participants were excluded due to lack of data. To meet criteria for inclusion, participants had to provide at least 168 hours of CGM data during baseline and the 8-week follow-up period and remain in closed loop for at least 50% of the 8-week period after closed loop initiation.
Percentage of total insulin delivered via basal administration.
Outcome measures
| Measure |
Baseline Period
n=29 Participants
Participants aged 2-6 years with Type 1 Diabetes were monitored using their existing insulin therapy (either multiple daily injections or personal insulin pump settings) prior to the initiation of the AI-driven intervention. Continuous glucose monitoring (CGM) data were collected for up to 28 days before the intervention to establish baseline glycemic control metrics.
|
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=27 Participants
Participants used the Tandem t:slim X2 insulin pump with Control-IQ technology for 8 weeks. Initial and adaptive pump settings were guided by an AI-based advisor through the UVA Clinical Portal. Investigators reviewed and could override AI-generated recommendations. This period was designed to evaluate the safety and efficacy of algorithm-driven insulin delivery in a pediatric population.
|
|---|---|---|
|
Basal Insulin
|
39 percentage of total insulin
Standard Deviation 17
|
38 percentage of total insulin
Standard Deviation 9
|
Adverse Events
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
Serious adverse events
| Measure |
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=32 participants at risk
In this single-arm intervention trial, all participants will use the study system (t:slim X2 with Control-IQ Technology and Dexcom Continuous Glucose Monitor) in closed-loop mode for 8 weeks at home with periodic parameter adjustment driven by an AI-based Advisor system.
AI-based Advisor system: Tandem t:slim X2 with Control-IQ and t:connect mobile application and Dexcom G6 or G7 system, connected to UVA cloud-based Physician Dashboard with insulin pump parameters driven by an AI-based Advisor system.
|
|---|---|
|
Endocrine disorders
Severe Hypoglycemia
|
3.1%
1/32 • Number of events 1 • 8 weeks
|
|
Endocrine disorders
Diabetic Ketoacidosis
|
3.1%
1/32 • Number of events 1 • 8 weeks
|
|
Endocrine disorders
Other serious adverse events
|
3.1%
1/32 • Number of events 1 • 8 weeks
|
Other adverse events
| Measure |
AI Advisor-driven At-home Closed Loop System Initiation and Parameter Adaptation
n=32 participants at risk
In this single-arm intervention trial, all participants will use the study system (t:slim X2 with Control-IQ Technology and Dexcom Continuous Glucose Monitor) in closed-loop mode for 8 weeks at home with periodic parameter adjustment driven by an AI-based Advisor system.
AI-based Advisor system: Tandem t:slim X2 with Control-IQ and t:connect mobile application and Dexcom G6 or G7 system, connected to UVA cloud-based Physician Dashboard with insulin pump parameters driven by an AI-based Advisor system.
|
|---|---|
|
Endocrine disorders
Hyperglycemia with or without Ketosis
|
9.4%
3/32 • Number of events 3 • 8 weeks
|
|
Gastrointestinal disorders
Gastroenteritis
|
6.2%
2/32 • Number of events 2 • 8 weeks
|
Additional Information
Marc Breton, PhD
University of Virginia Center for Diabetes Technology
Results disclosure agreements
- Principal investigator is a sponsor employee
- Publication restrictions are in place