Ignite Creation Date:
2024-05-06 @ 7:48 PM
Last Modification Date:
2024-10-26 @ 3:14 PM
Study NCT ID:
NCT06147583
Status:
NOT_YET_RECRUITING
Last Update Posted:
2023-12-14
First Post:
2023-11-15
Brief Title:
Assessing Detection Algorithms for Insulin Pump Malfunctions in Type 1 Diabetes
Sponsor:
University of Padova
Organization:
University of Padova
Study Overview
Official Title:
Pilot Study for the Evaluation of Algorithms for the Detection of Subcutaneous Insulin Pump Malfunctions in Subjects With Type 1 Diabetes
Status:
NOT_YET_RECRUITING
Status Verified Date:
2023-12
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 ie 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
The main questions it aims to answer is
Are the proposed algorithms effective in detecting insulin suspension Effectiveness accounts for both high sensitivity ie 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 pumpinfusion 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 patients 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 patients 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
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 pumpinfusion 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 patients 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 patients 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| 5731AO23 | OTHER | CESC- Comitato Etico Sprimentazione Clinica | None |