Diabetes Mellitus, Type INCT06147583
Assessing Detection Algorithms for Insulin Pump Malfunctions in Type 1 Diabetes
A study testing Simulation of an insulin pump failure in people with Diabetes Mellitus, Type I, run by University of Padova at 1 site in Italy.
Trial summary
The goal of this clinical trial is to test the effectiveness of fault-detection algorithms in detecting malfunctioning of the insulin infusion system in an artificial pancreas (also known as Automated Insulin Delivery system) for type 1 diabetes. Read more about this study
- Who is it for?
- Ages 18 to 70 years
- Conditions the study is for
- Diabetes Mellitus, Type I
- What is being tested?
- Simulation of an insulin pump failure
- Type of treatment
- Injection or infusion
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 20 participants
- Where
- 1 siteItaly
- Study dates
- Dec 2023to Apr 2024
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 70 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Age between 18 (included) and 70 years
- At least 1 year from the diagnosis of type 1 diabetes mellitus
- Body mass index (BMI) less than 30 kg/m²
- Treated with automated insulin delivery system (AID) for at least 3 months
- Using carbohydrate counting to calculate meal bolus
- Glycated hemoglobin < 10%
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- If treated with antihypertensive, thyroid, antidepressant or lipid-lowering drugs, the therapy must be stable for at least 1 month before enrolment and remain stable for the entire duration of the study
- Awareness of the study design and purpose
- Willingness to undergo the study procedures
- Signing the informed consent
Who cannot join
- Pregnancy or breastfeeding; pregnancy planning (effective contraception is required in women of childbearing age)
- Hematocrit less than 36% in females and less than 38% in males
- Presence of ischemic heart disease or congestive heart failure or history of a cerebrovascular event
- Therapy with a drug that significantly affects glucose metabolism (e.g. steroids)
- Uncontrolled hypertension
- Allergy or adverse reaction to insulin
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- Known adrenal problems, pancreatic cancer, or insulinoma
- Any comorbid condition affecting glucose metabolism as judged by the investigator
- Current alcohol abuse, substance abuse, or serious mental illness, as judged by the investigator
- Unstable proliferative retinopathy according to fundus examination within the last year
- Known hemorrhagic diathesis or dyscrasia
- Blood donation in the last 3 months
- Renal failure with creatinine > 150 μmol/L
- Impaired hepatic function based on plasma AST/ALT levels > 2 times the upper limits of normal values
Trial locations and contacts
1 site in 1 country.
Italy1 site
Azienda Ospedaliera di Padova
Padova, PD, 35128
Daniela Bruttomesso, MD, PhD, 0498212183, daniela.bruttomesso@unipd.it
Federico Boscari, MD, Phd, 0498212180, federico.boscari@gmail.com
About this study
Official title: Pilot Study for the Evaluation of Algorithms for the Detection of Subcutaneous Insulin Pump Malfunctions in Subjects With Type 1 Diabetes
The goal of this clinical trial is to test the effectiveness of fault-detection algorithms in detecting malfunctioning of the insulin infusion system in an artificial pancreas (also known as Automated Insulin Delivery system) for type 1 diabetes.
The main questions it aims to answer is:
Read more
"Are the proposed algorithms effective in detecting insulin suspension?" Effectiveness accounts for both high sensitivity (i.e. the fraction of suspension correctly detected) and low false alarm rate.
The study has three phases:
- free-living artificial pancreas data collection,
- in-patient induction of hyperglycemia (mimicking an insulin pump malfunction),
- retrospective analysis of the collected data to evaluate the effectiveness of the proposed algorithms in detecting insulin suspension.
Read the detailed description
In individuals with type 1 diabetes, adjusting insulin doses to accommodate the ever-changing conditions of daily life is crucial for achieving satisfactory metabolic control. To address this challenge, researchers have developed an Automated Insulin Delivery (AID) system, commonly known as an artificial pancreas. This system comprises of an insulin pump, a continuous glucose monitoring (CGM) sensor, and a sophisticated control algorithm. The algorithm uses CGM data to calculate the insulin dose required to maintain good glycemic control, and it automatically commands the insulin infusion.
However, artificial pancreas systems can experience malfunctions, some of which are highly risky. The most dangerous malfunctions include insulin pump failures and infusion set occlusions, which lead to prolonged interruptions in insulin delivery. This exposes the patient to the risk of hyperglycemia and, even more dangerously, ketoacidosis, a severe complication that can result in hospitalization and, in severe cases, death. Unfortunately, patients do not always notice these issues in a timely manner.
This study aims to test new algorithms for detecting pump/infusion set malfunctions that result in reduced or interrupted insulin delivery. The study consists of three phases:
- Phase 1: Preliminary Data Collection (Free-living Data) In this phase, data related to glycemic trends and insulin administration in free-living conditions are collected. This data is obtained from a download form the patient's artificial pancreas. The one-month session is designed to gather a substantial amount of patient-specific data to enable the algorithms to learn how insulin and meals impact the patient's glycemia as recorded by the CGM sensor. During this phase, the patient continues to use their artificial pancreas in their daily life.
- Phase 2: Induction of Hyperglycemia The second phase involves the patient visiting the clinic, where, according to a specific protocol and a defined schedule, insulin infusion is temporarily suspended to simulate a pump malfunction. The resulting episode of hyperglycemia is closely monitored under medical supervision. At the end of the experiment, the study team assists the patient in restoring euglycemia before returning home.
- Phase 3: Retrospective Data Analysis In this phase, the collected data is retrospectively analyzed to evaluate the effectiveness of the proposed algorithms in detecting insulin suspension, simulating a pump malfunction. The sensitivity of the tested methods is assessed as the fraction of insulin suspensions (simulating a malfunction) correctly detected.
The uniqueness of this dataset lies in the controlled induction of malfunction, achieved by disconnecting the insulin pump and monitoring the resulting hyperglycemic episode. The presence of malfunctions in this data is certain and precisely characterized in terms of the start time and duration. The dataset resulting from this experimentation will be a valuable tool for the scientific community, enabling the retrospective testing of fault detection algorithms.
- Conditions
- Diabetes Mellitus, Type I
- Being tested
- Simulation of an insulin pump failure
- Medical subject headings
- Diabetes Mellitus, Type 1
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Simulation of an insulin pump failureOther
The intervention will consist in simulating an insulin pump failure by suspending insulin infusion and monitoring the consequent hyperglycemia.
| Study group | Role | What they receive |
|---|---|---|
| Insulin pump fault simulation | Experimental | Collection of patients data during outpatient use of AID (automated insulin delivery); Inpatient simulation of insulin pump faults by suspension of insulin administration. Receives: Other: Simulation of an insulin pump failure |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity
Fraction of correctly detected insulin suspension in the population
Measured: During the intervention (during the inpatient insulin suspension to simulate a pump fault)
Secondary outcomes (1)
False positive per day
Number of false alarms (normalized by the number of days of monitoring)
Measured: Baseline pre-intervention (during the outpatient data collection)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Nov 2023
- Study startDec 2023 (estimated)
- Primary completionFeb 2024 (estimated)
- Study completionApr 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Diabetes Mellitus, Type IStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Supportive care
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 20 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Padova
- Sponsor type
- University, hospital or other
- Collaborators
- Azienda Ospedaliera di Padova
- Responsible party
- Principal investigator, Daniela Bruttomesso, University of Padova
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06147583
- Sponsor's protocol ID
- 5731/AO/23
- Other IDs
- 5731/AO/23
- Status last verified
- Dec 2023
- First submitted
- 15 Nov 2023
- Last updated
- 14 Dec 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Meneghetti L, Dassau E, Doyle FJ 3rd, Del Favero S. Machine Learning-Based Anomaly Detection Algorithms to Alert Patients Using Sensor Augmented Pump of Infusion Site Failures. J Diabetes Sci Technol. 2022 May;16(3):641-648. doi: 10.1177/1932296821997854. Epub 2021 Mar 9.
PubMed 33686873Meneghetti L, Facchinetti A, Favero SD. Model-Based Detection and Classification of Insulin Pump Faults and Missed Meal Announcements in Artificial Pancreas Systems for Type 1 Diabetes Therapy. IEEE Trans Biomed Eng. 2021 Jan;68(1):170-180. doi: 10.1109/TBME.2020.3004270. Epub 2020 Dec 21.
PubMed 32746034Meneghetti L, Susto GA, Del Favero S. Detection of Insulin Pump Malfunctioning to Improve Safety in Artificial Pancreas Using Unsupervised Algorithms. J Diabetes Sci Technol. 2019 Nov;13(6):1065-1076. doi: 10.1177/1932296819881452. Epub 2019 Oct 14.
PubMed 31608660Facchinetti A, Del Favero S, Sparacino G, Cobelli C. An online failure detection method of the glucose sensor-insulin pump system: improved overnight safety of type-1 diabetic subjects. IEEE Trans Biomed Eng. 2013 Feb;60(2):406-16. doi: 10.1109/TBME.2012.2227256. Epub 2012 Nov 15.
PubMed 23193300
Study at a glance
From the registry record, last updated by the sponsor on 14 Dec 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Pilot Study for the Evaluation of Algorithms for the Detection of Subcutaneous Insulin Pump Malfunctions in Subjects With Type 1 Diabetes
- ClinicalTrials.gov ID
- NCT06147583
- Acronym / sponsor ID
- 5731/AO/23
- Conditions
- Diabetes Mellitus, Type I
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 20 participants (estimated)
- Lead sponsor
- University of PadovaUniversity, hospital or other
- Collaborators
- Azienda Ospedaliera di Padova
- Study start
- Dec 2023 (estimated)
- Primary completion
- Feb 2024 (estimated)
- Study completion
- Apr 2024 (estimated)
- First posted
- 27 Nov 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The goal of this clinical trial is to test the effectiveness of fault-detection algorithms in detecting malfunctioning of the insulin infusion system in an artificial pancreas (also known as Automated Insulin Delivery system) for type 1 diabetes.
The main questions it aims to answer is:
"Are the proposed algorithms effective in detecting insulin suspension?" Effectiveness accounts for both high sensitivity (i.e. the fraction of suspension correctly detected) and low false alarm rate.
The study has three phases:
- free-living artificial pancreas data collection,
- in-patient induction of hyperglycemia (mimicking an insulin pump malfunction),
- retrospective analysis of the collected data to evaluate the effectiveness of the proposed algorithms in detecting insulin suspension.
Detailed description
In individuals with type 1 diabetes, adjusting insulin doses to accommodate the ever-changing conditions of daily life is crucial for achieving satisfactory metabolic control. To address this challenge, researchers have developed an Automated Insulin Delivery (AID) system, commonly known as an artificial pancreas. This system comprises of an insulin pump, a continuous glucose monitoring (CGM) sensor, and a sophisticated control algorithm. The algorithm uses CGM data to calculate the insulin dose required to maintain good glycemic control, and it automatically commands the insulin infusion.
However, artificial pancreas systems can experience malfunctions, some of which are highly risky. The most dangerous malfunctions include insulin pump failures and infusion set occlusions, which lead to prolonged interruptions in insulin delivery. This exposes the patient to the risk of hyperglycemia and, even more dangerously, ketoacidosis, a severe complication that can result in hospitalization and, in severe cases, death. Unfortunately, patients do not always notice these issues in a timely manner.
This study aims to test new algorithms for detecting pump/infusion set malfunctions that result in reduced or interrupted insulin delivery. The study consists of three phases:
- Phase 1: Preliminary Data Collection (Free-living Data) In this phase, data related to glycemic trends and insulin administration in free-living conditions are collected. This data is obtained from a download form the patient's artificial pancreas. The one-month session is designed to gather a substantial amount of patient-specific data to enable the algorithms to learn how insulin and meals impact the patient's glycemia as recorded by the CGM sensor. During this phase, the patient continues to use their artificial pancreas in their daily life.
- Phase 2: Induction of Hyperglycemia The second phase involves the patient visiting the clinic, where, according to a specific protocol and a defined schedule, insulin infusion is temporarily suspended to simulate a pump malfunction. The resulting episode of hyperglycemia is closely monitored under medical supervision. At the end of the experiment, the study team assists the patient in restoring euglycemia before returning home.
- Phase 3: Retrospective Data Analysis In this phase, the collected data is retrospectively analyzed to evaluate the effectiveness of the proposed algorithms in detecting insulin suspension, simulating a pump malfunction. The sensitivity of the tested methods is assessed as the fraction of insulin suspensions (simulating a malfunction) correctly detected.
The uniqueness of this dataset lies in the controlled induction of malfunction, achieved by disconnecting the insulin pump and monitoring the resulting hyperglycemic episode. The presence of malfunctions in this data is certain and precisely characterized in terms of the start time and duration. The dataset resulting from this experimentation will be a valuable tool for the scientific community, enabling the retrospective testing of fault detection algorithms.
Study design
- Primary purpose
- Supportive care
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Insulin pump fault simulation | Experimental | Other: Simulation of an insulin pump failureCollection of patients data during outpatient use of AID (automated insulin delivery); Inpatient simulation of insulin pump faults by suspension of insulin administration. |
Who may qualify
- Ages
- 18 to 70 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (10 listed)
- Age between 18 (included) and 70 years
- At least 1 year from the diagnosis of type 1 diabetes mellitus
- Body mass index (BMI) less than 30 kg/m²
- Treated with automated insulin delivery system (AID) for at least 3 months
- Using carbohydrate counting to calculate meal bolus
Key exclusion criteria (14 listed)
- Pregnancy or breastfeeding; pregnancy planning (effective contraception is required in women of childbearing age)
- Hematocrit less than 36% in females and less than 38% in males
- Presence of ischemic heart disease or congestive heart failure or history of a cerebrovascular event
- Therapy with a drug that significantly affects glucose metabolism (e.g. steroids)
- Uncontrolled hypertension
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Simulation of an insulin pump failureThe intervention will consist in simulating an insulin pump failure by suspending insulin infusion and monitoring the consequent hyperglycemia. | Other | Insulin pump fault simulation |
Primary outcome
Sensitivity
Time frame: During the intervention (during the inpatient insulin suspension to simulate a pump fault)
Fraction of correctly detected insulin suspension in the population
1 secondary outcome
False positive per day
Time frame: Baseline pre-intervention (during the outpatient data collection)
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Dec 2023.
- Background: PubMed 33686873Meneghetti L, Dassau E, Doyle FJ 3rd, Del Favero S. Machine Learning-Based Anomaly Detection Algorithms to Alert Patients Using Sensor Augmented Pump of Infusion Site Failures. J Diabetes Sci Technol. 2022 May;16(3):641-648. doi: 10.1177/1932296821997854. Epub 2021 Mar 9.
- Background: PubMed 32746034Meneghetti L, Facchinetti A, Favero SD. Model-Based Detection and Classification of Insulin Pump Faults and Missed Meal Announcements in Artificial Pancreas Systems for Type 1 Diabetes Therapy. IEEE Trans Biomed Eng. 2021 Jan;68(1):170-180. doi: 10.1109/TBME.2020.3004270. Epub 2020 Dec 21.
- Background: PubMed 31608660Meneghetti L, Susto GA, Del Favero S. Detection of Insulin Pump Malfunctioning to Improve Safety in Artificial Pancreas Using Unsupervised Algorithms. J Diabetes Sci Technol. 2019 Nov;13(6):1065-1076. doi: 10.1177/1932296819881452. Epub 2019 Oct 14.
- Background: PubMed 23193300Facchinetti A, Del Favero S, Sparacino G, Cobelli C. An online failure detection method of the glucose sensor-insulin pump system: improved overnight safety of type-1 diabetic subjects. IEEE Trans Biomed Eng. 2013 Feb;60(2):406-16. doi: 10.1109/TBME.2012.2227256. Epub 2012 Nov 15.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Simulation of an insulin pump failure in people with Diabetes Mellitus, Type I, run by University of Padova at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 10 inclusion and 14 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 14 Dec 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Daniela Bruttomesso, MD, Phd (0498212183, daniela.bruttomesso@unipd.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity (time frame: During the intervention (during the inpatient insulin suspension to simulate a pump fault)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06147583, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Dec 2023.