Clinical trial NCT05278143: AI for Glycemic Events Detection Via ECG in a Pediatric Population
Official title: Artificial Intelligence for Glycemic Events Detection Via Electrocardiogram in a Pediatric Population
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: type1diabetes
Pediatrics
Keywords: Artificial intelligence
Continuous glucose monitoring system
Pediatrics
Glycemic Events Detection
Lead sponsor: Bambino Gesù Hospital and Research Institute
Sponsor class: OTHER
Collaborators: name: University of Warwick
First posted: 2022-03-14
Enrollment: 64
Sex: ALL
Minimum age (years): 4
Maximum age (years): 18
Standard ages: CHILD
ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Males and females diagnosed with T1D, aged less than 18 years old who are currently under the care of the Unit of Endocrinology and Diabetes of Bambino Gesù Children's Hospital, Rome, Italy and who already use continuous glucose monitoring (CGM) systems are eligible to be involved in the study.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Interval across different fiducial point
timeFrame: three days
measure: Slope across different fiducial points
timeFrame: three days
measure: Absolute power
timeFrame: three days
measure: Severe hypoglycaemic events detection
timeFrame: three days
measure: Hypoglycaemic events detection
timeFrame: three days
measure: Hyperglycaemic events detection
timeFrame: three days
measure: Severe hyperglycaemic events detection
timeFrame: three days
Secondary outcomes:
measure: Health related quality of life
timeFrame: one month
measure: Glycated haemoglobin level (HbA1c)
timeFrame: three months
measure: Glycaemic variability (GV)
timeFrame: three days
measure: Frequency of severe hypoglycaemic events
timeFrame: three days
measure: Frequency of hypoglycaemic events
timeFrame: three days
measure: Frequency of hyperglycaemic events
timeFrame: three days
measure: Frequency of severe hyperglycaemic events
timeFrame: three days
measure: Time in range
timeFrame: three days
measure: Time in severe hypoglycaemia
timeFrame: three days
measure: Time in hypoglycaemia
timeFrame: three days
measure: Time in hyperglycaemia
timeFrame: three days
measure: Time in severe hyperglycaemia
timeFrame: three days
Other outcomes:
Locations:
city: Rome
country: Italy
facility: Bambino Gesù Children's Hospital
Study officials:
affiliation: Bambino Gesù Children's Hospital
name: Matteo Ritrovato, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Porumb M, Stranges S, Pescape A, Pecchia L. Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG. Sci Rep. 2020 Jan 13;10(1):170. doi: 10.1038/s41598-019-56927-5.
citation: Porumb M, Griffen C, Hattersley J, Pecchia L. Nocturnal low glucose detection in healthy elderly from one-lead ECG using convolutional denoising autoencoders. Biomedical Signal Processing and Control. 2020;62:102054.