AI-Predicted Disease Trajectories in Diabetes: A Retrospective Study

NCT ID: NCT06280729

Last Updated: 2024-02-28

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 10000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-03-01
• Study Completion Date: 2026-03-01

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.

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.

Conditions

Diabetes Mellitus, Type 1; Diabetes Mellitus, Type 2

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: RETROSPECTIVE

Study Groups

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

AI-Analyis

Intervention Type: 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..).

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

AI-Analyis

Intervention Type: 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..).

Interventions

AI-Analyis

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..).

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• 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.

Exclusion Criteria

• 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.

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

IRCCS San Raffaele

OTHER

Sponsor Role: lead

Responsible Party

Lorenzo Piemonti

Professor of Endocrinology Director, Diabetes Research Institute (SR-DRI) Director, Regenerative Medicine and Transplant Unit

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Lorenzo Piemonti, MD

Role: PRINCIPAL_INVESTIGATOR

IRCCS Ospedale San Raffaele srl

Emanuele Bosi, MD

Role: STUDY_DIRECTOR

IRCCS Ospedale San Raffaele srl

Locations

Diabetes Research Institute-IRCCS Ospedale San Raffaele

Milan, Lombardy, Italy

Countries

Italy

Central Contacts

Lorenzo Piemonti, MD

Role: CONTACT

piemonti.lorenzo@hsr.it

+390226432706

Other Identifiers

AI-TRYDIA

Identifier Type: -

Identifier Source: org_study_id