Assessing Detection Algorithms for Insulin Pump Malfunctions in Type 1 Diabetes

NCT06147583 | Unknown

• 1 other identifier: 5731/AO/23
• Study Type: interventional
• Target: 20
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Diabetes Mellitus, Type I

Outcome Measures

Primary Outcomes (1)

• Sensitivity

  Fraction of correctly detected insulin suspension in the population

  During the intervention (during the inpatient insulin suspension to simulate a pump fault)

Secondary Outcomes (1)

• False positive per day

  Baseline pre-intervention (during the outpatient data collection)

Study Arms (1)

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.

Other: Simulation of an insulin pump failure

Interventions

Simulation of an insulin pump failure OTHER

The intervention will consist in simulating an insulin pump failure by suspending insulin infusion and monitoring the consequent hyperglycemia.

Insulin pump fault simulation

Eligibility Criteria

• Age: 18 Years - 70 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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%
• 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

You may not qualify if:

• 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
• 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

Sponsors & Collaborators

• University of Padova: lead
• Azienda Ospedaliera di Padova: collaborator

Study Sites (1)

Azienda Ospedaliera di Padova

Padua, PD, 35128, Italy

Location

Related Publications (4)

• 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.

  PMID: 33686873 BACKGROUND

• Meneghetti 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.

  PMID: 32746034 BACKGROUND

• Meneghetti 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.

  PMID: 31608660 BACKGROUND

• Facchinetti 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.

  PMID: 23193300 BACKGROUND

MeSH Terms

Conditions

Diabetes Mellitus, Type 1

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Autoimmune Diseases; Immune System Diseases

Central Study Contacts

Daniela Bruttomesso, MD, Phd

CONTACT

0498212183; daniela.bruttomesso@unipd.it

Federico Boscari, MD, Phd

CONTACT

0498212180; federico.boscari@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: SUPPORTIVE CARE
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator and Medical Doctor

Study Record Dates

• First Submitted: November 15, 2023
• First Posted: November 27, 2023
• Study Start: December 1, 2023
• Primary Completion: February 1, 2024
• Study Completion: April 1, 2024
• Last Updated: December 14, 2023

Record last verified: 2023-12

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)