type1diabetesNCT05278143
AI for Glycemic Events Detection Via ECG in a Pediatric Population
An observational study of type1diabetes and Pediatrics, run by Bambino Gesù Hospital and Research Institute at 1 site in Italy.
Trial 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. Read more about this study
- Who is it for?
- Ages 4 to 18 years
- Conditions the study is for
- type1diabetes, Pediatrics
- How many people
- 64 participants
- Where
- 1 siteItaly
- Study dates
- 12 Apr 2021to 12 Apr 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 4 to 18 years
- All sexes
- No healthy volunteers
- Child, Adult
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.
Who may be able to join
- Age less than 18 years old
- Diagnosed with type 1 diabetes
- Use of continuous glucose monitoring systems (CGM)
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
Italy1 site, 1 recruiting
- Recruiting
Bambino Gesù Children's Hospital
Rome, 00165
Martina Andellini, MasterDegree, +393357625261, martina.andellini@opbg.net
Matteo Ritrovato, PhD
About this study
Official title: Artificial Intelligence for Glycemic Events Detection Via Electrocardiogram in a Pediatric Population
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.
Read the 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.
- Conditions
- type1diabetes, Pediatrics
- Keywords
- Artificial intelligence, Continuous glucose monitoring system, Pediatrics, Glycemic Events Detection
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| type1diabetes patients who use CGM | 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. Participants will wear an additional non-invasive wearable device, Medtronic Zephyr BioPatch, for recording physiological data for three days. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Interval across different fiducial point
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.
Measured: three days
Slope across different fiducial points
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.
Measured: three days
Absolute power
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)
Measured: three days
Severe hypoglycaemic events detection
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.
Measured: three days
Hypoglycaemic events detection
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.
Measured: three days
Hyperglycaemic events detection
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.
Measured: three days
Severe hyperglycaemic events detection
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.
Measured: three days
Secondary outcomes (12)
Health related quality of life
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
Measured: one month
Glycated haemoglobin level (HbA1c)
Glycated haemoglobin level (percent) is a measure of the previous three-months average blood sugar level.
Measured: three months
Glycaemic variability (GV)
Glycaemic variability (mg/dl) is a measure of the fluctuations of glucose over three days.
Measured: three days
Frequency of severe hypoglycaemic events
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.
Measured: three days
Frequency of hypoglycaemic events
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.
Measured: three days
Frequency of hyperglycaemic events
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.
Measured: three days
Frequency of severe hyperglycaemic events
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.
Measured: three days
Time in range
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.
Measured: three days
Time in severe hypoglycaemia
Time in severe hypoglycaemia (percent) is the percentage of time that a person spends with their blood glucose levels less than 50 mg/dl.
Measured: three days
Time in hypoglycaemia
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.
Measured: three days
Time in hyperglycaemia
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.
Measured: three days
Time in severe hyperglycaemia
Time in severe hyperglycaemia (percent) is the percentage of time that a person spends with their blood glucose levels more than 240 mg/dl.
Measured: three days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Mar 2022
- Study start12 Apr 2021
- Primary completion12 Apr 2023 (estimated)
- Study completion12 Apr 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for type1diabetesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 64 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Bambino Gesù Hospital and Research Institute
- Sponsor type
- University, hospital or other
- Collaborators
- University of Warwick
- Responsible party
- Sponsor
- Investigators
- Matteo Ritrovato, PhD, Principal investigator, Bambino Gesù Children's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05278143
- Sponsor's protocol ID
- 2260_OPBG_2020
- Status last verified
- Jan 2022
- First submitted
- 31 Jan 2022
- Last updated
- 14 Mar 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 31932608Porumb 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.
Andellini M, Haleem S, Angelini M, Ritrovato M, Schiaffini R, Iadanza E, Pecchia L. Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population: study protocol. Health Technol (Berl). 2023;13(1):145-154. doi: 10.1007/s12553-022-00719-x. Epub 2023 Jan 23.
PubMed 36761922
Study at a glance
From the registry record, last updated by the sponsor on 14 Mar 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence for Glycemic Events Detection Via Electrocardiogram in a Pediatric Population
- ClinicalTrials.gov ID
- NCT05278143
- Acronym / sponsor ID
- 2260_OPBG_2020
- Conditions
- type1diabetes, Pediatrics
- Keywords
- Artificial intelligence, Continuous glucose monitoring system, Pediatrics, Glycemic Events Detection
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 64 participants (estimated)
- Lead sponsor
- Bambino Gesù Hospital and Research InstituteUniversity, hospital or other
- Collaborators
- University of Warwick
- Study start
- 12 Apr 2021 (actual)
- Primary completion
- 12 Apr 2023 (estimated)
- Study completion
- 12 Apr 2023 (estimated)
- First posted
- 14 Mar 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| type1diabetes patients who use CGM | – | –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. Participants will wear an additional non-invasive wearable device, Medtronic Zephyr BioPatch, for recording physiological data for three days. |
Who may qualify
- Ages
- 4 to 18 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult
- 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.
Key inclusion criteria (3 listed)
- Age less than 18 years old
- Diagnosed with type 1 diabetes
- Use of continuous glucose monitoring systems (CGM)
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Interval across different fiducial point
Time frame: 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.
Slope across different fiducial points
Time frame: 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.
Absolute power
Time frame: 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)
Severe hypoglycaemic events detection
Time frame: 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.
Hypoglycaemic events detection
Time frame: 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.
Hyperglycaemic events detection
Time frame: 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
Time frame: 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.
12 secondary outcomes
Health related quality of life
Time frame: one month
Glycated haemoglobin level (HbA1c)
Time frame: three months
Glycaemic variability (GV)
Time frame: three days
Frequency of severe hypoglycaemic events
Time frame: three days
Frequency of hypoglycaemic events
Time frame: three days
Frequency of hyperglycaemic events
Time frame: three days
Frequency of severe hyperglycaemic events
Time frame: three days
Time in range
Time frame: three days
Time in severe hypoglycaemia
Time frame: three days
Time in hypoglycaemia
Time frame: three days
Time in hyperglycaemia
Time frame: three days
Time in severe hyperglycaemia
Time frame: three days
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Mar 2022.
- Background: PubMed 31932608Porumb 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.
- BackgroundPorumb 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.
- Derived from this study: PubMed 36761922Andellini M, Haleem S, Angelini M, Ritrovato M, Schiaffini R, Iadanza E, Pecchia L. Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population: study protocol. Health Technol (Berl). 2023;13(1):145-154. doi: 10.1007/s12553-022-00719-x. Epub 2023 Jan 23.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Matteo Ritrovato, PhD principal investigator
Bambino Gesù Children's Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of type1diabetes and Pediatrics, run by Bambino Gesù Hospital and Research Institute at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 14 Mar 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Martina Andellini, PhDstudent (+393357625261, martina.andellini@opbg.net), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Interval across different fiducial point (time frame: three days). The study lists 7 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05278143, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Mar 2022.