Diabetes Mellitus, Type 1AI-TRYDIANCT06280729
AI-Predicted Disease Trajectories in Diabetes: A Retrospective Study
An observational study of Diabetes Mellitus, Type 1 and Diabetes Mellitus, Type 2, run by IRCCS San Raffaele at 1 site in Italy.
Trial summary
The study explores the utilization of artificial intelligence (AI) to predict disease progression trajectories in patients with diabetes. By analyzing historical data from a retrospective cohort, we aim to identify patterns and predictors of disease evolution. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2
- What is being tested?
- AI-Analyis
- Type of treatment
- Pill or oral medicine
- How many people
- 10,000 participants
- Where
- 1 siteItaly
- Study dates
- 1 Mar 2024to 1 Mar 2026
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: The study population comprises individuals diagnosed with Diabetes Mellitus, encompassing both Type 1 Diabetes Mellitus (T1DM) and Type 2 Diabetes Mellitus (T2DM). This population is identified through medical records housed within the Electronic Health Record (EHR) system, specifically utilizing data generated by the Smart Digital Clinic Software (Meteda) since its inception in our hospital environment.
Who may be able to join
- Diagnosis: Individuals with a confirmed diagnosis of T1DM or T2DM, as indicated by their EHR labels or a history of glycated hemoglobin levels and medication usage consistent with diabetes management.
- Age: Patients of all ages are considered, with subgroups possibly defined for more detailed analysis (e.g., pediatric, adult, senior).
- Treatment history: Both patients who are newly diagnosed and those with an established history of diabetes treatment, including insulin and oral hypoglycemic agents, are included to capture a broad spectrum of disease trajectories.
Who cannot join
- Incomplete records: Patients with incomplete medical records that do not provide sufficient information on their diabetes diagnosis, treatment history, or follow-up data are excluded.
- Other significant diseases: Individuals with comorbid conditions that could significantly alter the natural history of diabetes or its management (e.g., end-stage renal disease not related to diabetes, active cancer treatment) might be excluded to ensure the study focuses on the diabetes trajectory.
Trial locations and contacts
1 site in 1 country.
Italy1 site
Diabetes Research Institute-IRCCS Ospedale San Raffaele
Milan, Lombardy, 20132
About this study
Official title: A Retrospective Observational Study to Use Artificial Intelligence for Prediction of Disease TRajectorY in DIAbetes
The study explores the utilization of artificial intelligence (AI) to predict disease progression trajectories in patients with diabetes. By analyzing historical data from a retrospective cohort, we aim to identify patterns and predictors of disease evolution. The approach seeks to enhance personalized treatment strategies and improve outcomes by foreseeing potential complications and disease milestones. The findings could pave the way for more targeted and effective management of diabetes through AI-driven insights.
Read the detailed description
The proposed study aims to harness the power of artificial intelligence (AI) and machine learning (ML) to address critical clinical needs in the management of Diabetes Mellitus (DM), a chronic and non-remissive disease that significantly impacts patients' lives. Despite the availability of hypoglycemic therapies, the prevention of both microvascular (retinopathy, nephropathy, neuropathy) and macrovascular (cardiovascular, cerebrovascular disease, and peripheral arterial disease) complications remains a challenge, with diabetic patients at higher risk compared to the general population.
The study focuses on two primary objectives: first, to a priori identify patients with varying probabilities of developing DM complications, allowing for a more resource-intensive approach for those at greater risk; second, to pinpoint the most effective therapeutic choices tailored to individual patient profiles. These objectives stem from distinct clinical characteristics and needs in the management of Type 1 DM (T1DM) and Type 2 DM (T2DM). For T1DM, the phenomenon of partial clinical remission post-diagnosis, marked by reduced insulin need and glycemic variability, suggests a window for improved long-term outcomes. Conversely, T2DM management lacks clear guidance for personalized medication regimens following metformin, highlighting a gap in treatment optimization.
Leveraging AI and ML for the analysis of multidimensional and longitudinal health data presents an innovative approach to predicting disease trajectories and therapeutic outcomes in DM. This observational, retrospective study, initially monocentric with potential for broader data integration, will delve into Electronic Health Records (EHR) using the Smart Digital Clinic Software (Meteda). By screening patients based on specific eligibility criteria, including DM type classification and historical health markers, this research aims to generate two distinct patient cohorts for in-depth analysis.
This study not only addresses a pressing clinical necessity by aiming to enhance personalized DM management and prevent complications but also contributes to the nascent field of AI application in healthcare. Through this exploration, the study seeks to offer new insights, validate AI and ML's utility in medical predictions, and establish a foundation for future research and clinical practices that embrace technological advancements for improved patient care.
- Being tested
- AI-Analyis
- Keywords
- Diabetes Mellitus (DM), Artificial Intelligence (AI), Machine Learning (ML), Type 1 Diabetes Mellitus (T1DM), Type 2 Diabetes Mellitus (T2DM), Personalized Treatment, Observational, Retrospective Study, Health Data Analysis
- Medical subject headings
- Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Diabetes Mellitus
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-AnalyisOther
The study will investigate classification (ie logistic regression, decision tree, random forest, support vector machine, K nearest neighbour, naive bayes) ML models and treatment effect estimation ML models (T-learner, X-learner..).
| Study group | Role | What they receive |
|---|---|---|
| T1DM cohort | A. T1DM label attached in the EHR OR B. patients with at least a record of Glycated Hemoglobin level of >6.5% (48 mmol/mol) AND < 45 years old AND no use of oral antidiabetic drug AND positivity of ≥2 anti-islet antibodies Receives: Other: AI-Analyis | |
| T2DM cohort: | A. T2DM label attached in the EHR OR B. patients with at least a record of Glycated Hemoglobin level of >6.5% (48 mmol/mol) AND Medication history of antidiabetic drug comprising insulin or not Receives: Other: AI-Analyis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Primary Endpoint
Development and validation of a model to predict Partial Clinical Remission (PCR) to identify individuals diagnosed with T1D who are most likely to undergo PCR in the early stages of the natural history of the disease. The definition for PCR, namely glycated hemoglobin adjusted for insulin dose (IDAA1c), will be evaluated at 6 and 12 months after the onset of diabetes. Remitters and nonremitters will be dichotomously divided by IDAA1c ≤9 and IDAA1c >9
Measured: 0-36 month
Primary Endpoint
Development and validation of a model to predict the development of chronic complications in patients with diabetes
Measured: 0-36 month
Primary Endpoint
Development and validation of a model to predict the response to different second lines of therapy in addition to metformin in patients with T2D who have failed the first line with metformin alone.
Measured: 0-36 month
Secondary outcomes (1)
Exploratory Objectives
Gather experience on the AI workflow in the healthcare setting, from data acquisition to model development and testing.
Measured: 0-36 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Feb 2024
- Study start1 Mar 2024 (estimated)
- Primary completion1 Mar 2025 (estimated)
- Study completion1 Mar 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Diabetes Mellitus, Type 1Studies of the same condition, open and under way.
- More trials run by IRCCS San RaffaeleThe sponsor's other registered studies.
- 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
- Other
- Time perspective
- Retrospective
- Enrolment
- 10,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- IRCCS San Raffaele
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Lorenzo Piemonti, IRCCS San Raffaele
- Investigators
- Lorenzo Piemonti, MD, Principal investigator, IRCCS Ospedale San Raffaele srlEmanuele Bosi, MD, Study director, IRCCS Ospedale San Raffaele srl
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06280729
- Sponsor's protocol ID
- AI-TRYDIA
- Acronym
- AI-TRYDIA
- Status last verified
- Feb 2024
- First submitted
- 15 Feb 2024
- Last updated
- 28 Feb 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Retrospective Observational Study to Use Artificial Intelligence for Prediction of Disease TRajectorY in DIAbetes
- ClinicalTrials.gov ID
- NCT06280729
- Acronym / sponsor ID
- AI-TRYDIA / AI-TRYDIA
- Keywords
- Diabetes Mellitus (DM), Artificial Intelligence (AI), Machine Learning (ML), Type 1 Diabetes Mellitus (T1DM), Type 2 Diabetes Mellitus (T2DM), Personalized Treatment, Observational, Retrospective Study, Health Data Analysis
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- IRCCS San RaffaeleUniversity, hospital or other
- Study start
- 1 Mar 2024 (estimated)
- Primary completion
- 1 Mar 2025 (estimated)
- Study completion
- 1 Mar 2026 (estimated)
- First posted
- 28 Feb 2024 (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.
The study explores the utilization of artificial intelligence (AI) to predict disease progression trajectories in patients with diabetes. By analyzing historical data from a retrospective cohort, we aim to identify patterns and predictors of disease evolution. The approach seeks to enhance personalized treatment strategies and improve outcomes by foreseeing potential complications and disease milestones. The findings could pave the way for more targeted and effective management of diabetes through AI-driven insights.
Detailed description
The proposed study aims to harness the power of artificial intelligence (AI) and machine learning (ML) to address critical clinical needs in the management of Diabetes Mellitus (DM), a chronic and non-remissive disease that significantly impacts patients' lives. Despite the availability of hypoglycemic therapies, the prevention of both microvascular (retinopathy, nephropathy, neuropathy) and macrovascular (cardiovascular, cerebrovascular disease, and peripheral arterial disease) complications remains a challenge, with diabetic patients at higher risk compared to the general population.
The study focuses on two primary objectives: first, to a priori identify patients with varying probabilities of developing DM complications, allowing for a more resource-intensive approach for those at greater risk; second, to pinpoint the most effective therapeutic choices tailored to individual patient profiles. These objectives stem from distinct clinical characteristics and needs in the management of Type 1 DM (T1DM) and Type 2 DM (T2DM). For T1DM, the phenomenon of partial clinical remission post-diagnosis, marked by reduced insulin need and glycemic variability, suggests a window for improved long-term outcomes. Conversely, T2DM management lacks clear guidance for personalized medication regimens following metformin, highlighting a gap in treatment optimization.
Leveraging AI and ML for the analysis of multidimensional and longitudinal health data presents an innovative approach to predicting disease trajectories and therapeutic outcomes in DM. This observational, retrospective study, initially monocentric with potential for broader data integration, will delve into Electronic Health Records (EHR) using the Smart Digital Clinic Software (Meteda). By screening patients based on specific eligibility criteria, including DM type classification and historical health markers, this research aims to generate two distinct patient cohorts for in-depth analysis.
This study not only addresses a pressing clinical necessity by aiming to enhance personalized DM management and prevent complications but also contributes to the nascent field of AI application in healthcare. Through this exploration, the study seeks to offer new insights, validate AI and ML's utility in medical predictions, and establish a foundation for future research and clinical practices that embrace technological advancements for improved patient care.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| T1DM cohort | – | Other: AI-AnalyisA. T1DM label attached in the EHR OR B. patients with at least a record of Glycated Hemoglobin level of >6.5% (48 mmol/mol) AND < 45 years old AND no use of oral antidiabetic drug AND positivity of ≥2 anti-islet antibodies |
| T2DM cohort: | – | Other: AI-AnalyisA. T2DM label attached in the EHR OR B. patients with at least a record of Glycated Hemoglobin level of >6.5% (48 mmol/mol) AND Medication history of antidiabetic drug comprising insulin or not |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study population comprises individuals diagnosed with Diabetes Mellitus, encompassing both Type 1 Diabetes Mellitus (T1DM) and Type 2 Diabetes Mellitus (T2DM). This population is identified through medical records housed within the Electronic Health Record (EHR) system, specifically utilizing data generated by the Smart Digital Clinic Software (Meteda) since its inception in our hospital environment.
Key inclusion criteria (3 listed)
- Diagnosis: Individuals with a confirmed diagnosis of T1DM or T2DM, as indicated by their EHR labels or a history of glycated hemoglobin levels and medication usage consistent with diabetes management.
- Age: Patients of all ages are considered, with subgroups possibly defined for more detailed analysis (e.g., pediatric, adult, senior).
- Treatment history: Both patients who are newly diagnosed and those with an established history of diabetes treatment, including insulin and oral hypoglycemic agents, are included to capture a broad spectrum of disease trajectories.
Key exclusion criteria (2 listed)
- Incomplete records: Patients with incomplete medical records that do not provide sufficient information on their diabetes diagnosis, treatment history, or follow-up data are excluded.
- Other significant diseases: Individuals with comorbid conditions that could significantly alter the natural history of diabetes or its management (e.g., end-stage renal disease not related to diabetes, active cancer treatment) might be excluded to ensure the study focuses on the diabetes trajectory.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-AnalyisThe study will investigate classification (ie logistic regression, decision tree, random forest, support vector machine, K nearest neighbour, naive bayes) ML models and treatment effect estimation ML models (T-learner, X-learner..). | Other | T1DM cohort, T2DM cohort: |
Primary outcome
Primary Endpoint
Time frame: 0-36 month
Development and validation of a model to predict Partial Clinical Remission (PCR) to identify individuals diagnosed with T1D who are most likely to undergo PCR in the early stages of the natural history of the disease. The definition for PCR, namely glycated hemoglobin adjusted for insulin dose (IDAA1c), will be evaluated at 6 and 12 months after the onset of diabetes. Remitters and nonremitters will be dichotomously divided by IDAA1c ≤9 and IDAA1c >9
Primary Endpoint
Time frame: 0-36 month
Development and validation of a model to predict the development of chronic complications in patients with diabetes
Primary Endpoint
Time frame: 0-36 month
Development and validation of a model to predict the response to different second lines of therapy in addition to metformin in patients with T2D who have failed the first line with metformin alone.
1 secondary outcome
Exploratory Objectives
Time frame: 0-36 month
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Feb 2024.
- 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.
Lorenzo Piemonti, MD principal investigator
IRCCS Ospedale San Raffaele srl
Emanuele Bosi, MD study director
IRCCS Ospedale San Raffaele srl
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 Diabetes Mellitus, Type 1 and Diabetes Mellitus, Type 2, run by IRCCS San Raffaele 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 2 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 28 Feb 2024. 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 Lorenzo Piemonti, MD (+390226432706, piemonti.lorenzo@hsr.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Primary Endpoint (time frame: 0-36 month). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 28 Feb 2024.