Clinical trial NCT06147583: Assessing Detection Algorithms for Insulin Pump Malfunctions in Type 1 Diabetes
Official title: Pilot Study for the Evaluation of Algorithms for the Detection of Subcutaneous Insulin Pump Malfunctions in Subjects With Type 1 Diabetes
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Diabetes Mellitus, Type I
Lead sponsor: University of Padova
Sponsor class: OTHER
Collaborators: name: Azienda Ospedaliera di Padova
First posted: 2023-11-27
Enrollment: 20
Sex: ALL
Minimum age (years): 18
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: SUPPORTIVE_CARE

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.

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.

Eligibility criteria: Inclusion Criteria:

* 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

Exclusion Criteria:

* 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

Primary outcomes:
measure: Sensitivity
timeFrame: During the intervention (during the inpatient insulin suspension to simulate a pump fault)
Secondary outcomes:
measure: False positive per day
timeFrame: Baseline pre-intervention (during the outpatient data collection)
Other outcomes:
Locations:
city: Padova
country: Italy
facility: Azienda Ospedaliera di Padova
state: PD
Study officials:
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.