AI for Glycemic Events Detection Via ECG in a Pediatric Population

• Conditions: type1diabetes; Pediatrics

• Registration Number: NCT05278143
• Lead Sponsor: Bambino Gesù Hospital and Research Institute

Brief Summary

Paediatric Type 1 Diabetes (T1D) patients are at greater risk for developing severe hypo and hyperglycaemic events due to poor glycaemic control and incorrect Insulin administration. To reduce the risk of adverse events, patients need to achieve the best possible glycaemic control through frequent blood glucose monitoring with finger prick or Continuous Glucose Monitoring (CGM) systems. However, several non-invasive techniques have been proposed aiming at exploiting changes in physiological parameters based on glucose levels. The overall objective of this study is to validate a deep learning algorithm to detect glycaemic events using electrocardiogram (ECG) signals collected through non-invasive device.

This observational single-arm study will enrol participants with T1D aged less than 18 years old who already use CGM device. Participants will wear an additional non-invasive wearable device, for recording physiological data (e.g. ECG, breathing waveform, 3-axis acceleration) for three days. ECG variables (e.g. heart rate variability features), respiratory rate, physical activity, posture and glycaemic measurements driven through ECG variables and other physiological signals (e.g. the frequency of hypo or hyperglycaemic events, the time spent in hypo- or hyperglycaemia and the time in range) are the main outcomes. A quality-of-life questionnaire will be administered to collect secondary outcomes. Data collected will be used to design, develop and validate the personalised and generalized classifiers based on a deep-learning artificial intelligence (AI) algorithm developed during the pilot study, able to automatically detect hypoglycaemic events by using few ECG heartbeats recorded with wearable devices.

This study is a validation study that will carry out additional tests on a larger diabetes sample population, to validate the previous promising pilot results that were based on four healthy adult subjects. Therefore, this study will provide evidence on the reliability of the deep-learning artificial intelligence algorithms investigators developed, in detecting glycaemic events in paediatric diabetic patients in free-living conditions. Additionally, this study aims to develop the generalized AI model for the automated glycaemic events detection on real-time ECG.

Detailed Description

As per inclusion criteria, the study participants continue to use their CGM device they are already using. During their routine diabetes hospital visit, the participants are asked to wear an additional wearable device, Medtronic Zephyr BioPatch, for recording the physiological data for a period of up to three days. After receiving the training session and relevant information about the study, the participants are allowed to return home with the wearable device attached. During the hospital visit, the quality of life questionnaire for paediatric patients (PEdsQL) is submitted to recruited patients. They are asked to answer questions on how T1D affects their daily activities.

During the monitoring days, patients can continue their daily activities undisturbed, without any changes in either physical activities or diet. In this way, data gathered from free-living conditions are obtained. They should wear the sensor during the day and the night and remove it while showering. The device should be approximately charged every 12-hours. For this reason, patients were provided with two devices. While wearing the second device the one used during the day should be recharged and vice versa. Patients receive regular contact from the research team not only to check on their safety and wellbeing, but also to ensure the data collection is successful. At the end of the third day, patients should return the devices to the hospital.

Study Timeline

• Study Start: 2021-04-12
• Status Verified: 2022-01
• Study First Submitted: 2022-01-31
• Study First Submitted QC: 2022-03-03
• Last Update Submitted: 2022-03-03
• Study First Posted: 2022-03-14
• Last Update Posted: 2022-03-14
• Primary Completion: 2023-04-12
• Study Completion: 2023-04-12

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 64

Inclusion Criteria

• Age less than 18 years old
• Diagnosed with type 1 diabetes
• Use of continuous glucose monitoring systems (CGM)

Exclusion Criteria

• Use of standard finger prick glucometer to measure glycemic values
• Be pregnant or becoming pregnant during the study
• Coexistence of celiac disease
• Coexistence of non-diabetic hypoglycemia
• Coexistence of cardiovascular pathologies and cardiac arrhythmias

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Slope across different fiducial points	three days	The Slope across different fiducial points (mV/ms) is one of the Heart Rate Variability Features (HRV) that are useful to quantify the difference in ECG signals for different glycaemic events. The glycaemic events can be determined non-invasively via ECG signals by the automated AI algorithm which are trained according to glucose measurements from the CGM. The difference in ECG signals for different glycaemic events can be quantified through the difference in the slope across different fiducial points (five fiducial points (P.Q.R,S,T) and 9 intervals among them) calculated over three days of continued ECG signal registration.
Interval across different fiducial point	three days	The interval across different fiducial points (millisecond) is one of the Heart Rate Variability Features (HRV) that are useful to quantify the difference in ECG signals for different glycaemic events. The glycaemic events can be determined non-invasively via ECG signals by the automated AI algorithm which are trained according to glucose measurements from the CGM. The difference in ECG signals for different glycaemic events can be quantified through the difference in the intervals across different fiducial points (five fiducial points (P.Q.R,S,T) and 9 intervals among them) calculated over three days of continued ECG signal registration.
Hypoglycaemic events detection	three days	The hypoglycaemic events (identified by glycaemic values between 50mg/dl and 70mg/dl) will be indirectly detected non-invasively via ECG signals by the automated AI algorithm which are trained according to glucose measurements from the CGM.; The deep-learning algorithm is able to automatically detect the hypoglycaemic events through the assessment of the ECG variables (heart rate (BPM), physical activity and posture (lying, standing, walking, running) and HRV features over three days of continued ECG and CGM signals registration.
Absolute power	three days	The absolute power (ms\^2/Hz) is one of the Heart Rate Variability Features (HRV) that are useful to quantify the difference in ECG signals for different glycaemic events over three days of continued ECG signal registration.The signal energy can be determined for 5 minutes ECG excerpt within Ultra Low Frequency (ULF) (≤0.003 Hz), Very Low Frequency (VLF) (0.0033-0.04 Hz), Low Frequency (LF) (0.04-0.15 Hz) and High Frequency (HF) (0.15-0.4 Hz)
Hyperglycaemic events detection	three days	The hyperglycaemic events (identified by glycaemic values between 180mg/dl and 240mg/dl) will be indirectly detected non-invasively via ECG signals by the automated AI algorithm which are trained according to glucose measurements from the CGM.; The deep-learning algorithm is able to automatically detect the hyperglycaemic events through the assessment of the ECG variables (heart rate (BPM), physical activity and posture (lying, standing, walking, running) and HRV features over three days of continued ECG and CGM signals registration.
Severe hyperglycaemic events detection	three days	The severe hyperglycaemic events (identified by glycaemic values \> 240mg/dl) will be indirectly detected non-invasively via ECG signals by the automated AI algorithm which are trained according to glucose measurements from the CGM.; The deep-learning algorithm is able to automatically detect the severe hyperglycaemic events through the assessment of the ECG variables (heart rate (BPM), physical activity and posture (lying, standing, walking, running) and HRV features over three days of continued ECG and CGM signals registration.
Severe hypoglycaemic events detection	three days	The severe hypoglycaemic events (identified by glycaemic values \< 50mg/dl) will be indirectly detected non-invasively via ECG signals by the automated AI algorithm which are trained according to glucose measurements from the CGM.; The deep-learning algorithm is able to automatically detect the severe hypoglycaemic events through the assessment of the ECG variables (heart rate (BPM), physical activity and posture (lying, standing, walking, running) and HRV features over three days of continued ECG and CGM signals registration.

Secondary Outcome Measures

Name	Time	Method
Frequency of hyperglycaemic events	three days	The frequency of hyperglycaemic events (Frequency (percent)) is measured as the ratio of the number of hyperglycaemic events (180 mg/dl \< glucose level \< 240 mg/dl) and the total number of glucose measurements over three days.
Time in hypoglycaemia	three days	Time in in hypoglycaemia (percent) is the percentage of time that a person spends with their blood glucose levels between 50 mg/dl and 70 mg/dl.
Health related quality of life	one month	The Health related quality of life for pediatric patients is assessed through the Pediatric Quality of Life Inventory (PedsQL) questionnaire. The Pediatric Quality of Life Inventory (PedsQL) is a 23-item generic health status instrument with parent and child forms that assesses five domains of health (physical functioning, emotional functioning, psychosocial functioning, social functioning, and school functioning) in children and adolescents ages 2 to 18.; the minimum and maximum values: 0, 100 higher scores mean a better outcome
Glycaemic variability (GV)	three days	Glycaemic variability (mg/dl) is a measure of the fluctuations of glucose over three days.
Frequency of hypoglycaemic events	three days	The frequency of hypoglycaemic events (Frequency (percent) is measured as the ratio between the number of hypoglycaemic events (50 mg/dl \< glucose level \< 70 mg/dl) and the total number of glucose measurements over three days.
Time in severe hypoglycaemia	three days	Time in severe hypoglycaemia (percent) is the percentage of time that a person spends with their blood glucose levels less than 50 mg/dl.
Glycated haemoglobin level (HbA1c)	three months	Glycated haemoglobin level (percent) is a measure of the previous three-months average blood sugar level.
Frequency of severe hypoglycaemic events	three days	the frequency of severe hypoglycaemic events (Frequency (percent) is measured as the ratio between the number of severe hypoglycaemic events (glucose level \< 50 mg/dl) and the total number of glucose measurements over three days.
Frequency of severe hyperglycaemic events	three days	The frequency of severe hyperglycaemic events (Frequency (percent) is measured as the ratio between the number of severe hyperglycaemic events (glucose level \> 240 mg/dl) and the total number of glucose measurements over three days.
Time in range	three days	Time in Range (percent) is the percentage of time that a person spends with their blood glucose levels between 70 mg/dl and 180 mg/dl.
Time in hyperglycaemia	three days	Time in hyperglycaemia (percent) is the percentage of time that a person spends with their blood glucose levels between 180 mg/dl and 240 mg/dl.
Time in severe hyperglycaemia	three days	Time in severe hyperglycaemia (percent) is the percentage of time that a person spends with their blood glucose levels more than 240 mg/dl.

Trial Locations

Locations (1)

Bambino Gesù Children's Hospital; 🇮🇹 Rome, Italy