AI-Predicted Disease Trajectories in Diabetes: A Retrospective Study

NCT06280729 | Not Yet Recruiting

• 1 other identifier: AI-TRYDIA
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Diabetes Mellitus, Type 1; Diabetes Mellitus, Type 2

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

Outcome Measures

Primary Outcomes (3)

• 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

  0-36 month

• Primary Endpoint

  Development and validation of a model to predict the development of chronic complications in patients with diabetes

  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.

  0-36 month

Secondary Outcomes (1)

• Exploratory Objectives

  0-36 month

Study Arms (2)

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

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

Other: AI-Analyis

Interventions

AI-Analyis OTHER

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..).

T1DM cohort; T2DM cohort:

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• IRCCS San Raffaele: lead

Study Sites (1)

Diabetes Research Institute-IRCCS Ospedale San Raffaele

Milan, Lombardy, 20132, Italy

Location

MeSH Terms

Conditions

Diabetes Mellitus, Type 1; Diabetes Mellitus, Type 2; Diabetes Mellitus

Condition Hierarchy (Ancestors)

Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Autoimmune Diseases; Immune System Diseases

Study Officials

• Lorenzo Piemonti, MD

  IRCCS Ospedale San Raffaele srl

  PRINCIPAL INVESTIGATOR

• Emanuele Bosi, MD

  IRCCS Ospedale San Raffaele srl

  STUDY DIRECTOR

Central Study Contacts

Lorenzo Piemonti, MD

CONTACT

+390226432706; piemonti.lorenzo@hsr.it

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor of Endocrinology Director, Diabetes Research Institute (SR-DRI) Director, Regenerative Medicine and Transplant Unit

Study Record Dates

• First Submitted: February 15, 2024
• First Posted: February 28, 2024
• Study Start: March 1, 2024
• Primary Completion: March 1, 2025
• Study Completion: March 1, 2026
• Last Updated: February 28, 2024

Record last verified: 2024-02

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)