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AI for Glycemic Events Detection Via ECG in a Pediatric Population

NCT05278143 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 64

Last updated 2022-03-14

No results posted yet for this study

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.

Conditions

  • type1diabetes
  • Pediatrics

Sponsors & Collaborators

  • University of Warwick

    collaborator OTHER

  • Bambino Gesù Hospital and Research Institute

    lead OTHER

Principal Investigators

  • Matteo Ritrovato, PhD · Bambino Gesù Children's Hospital

Eligibility

Min Age
4 Years
Max Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-04-12
Primary Completion
2023-04-12
Completion
2023-04-12

Countries

  • Italy

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05278143 on ClinicalTrials.gov