AI-Predicted Disease Trajectories in Diabetes: A Retrospective Study

IRCCS San Raffaele10,000 enrolled

Overview

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.

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

Interventions

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..).

Eligibility

Sex: ALL

Medical Language ↔ Plain English

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.

Locations (1)

Diabetes Research Institute-IRCCS Ospedale San Raffaele

Milan, Lombardy, Italy

Outcomes

Primary Outcomes

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

Time frame: 0-36 month

Primary Endpoint

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

Time frame: 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.

Time frame: 0-36 month

Secondary Outcomes

Exploratory Objectives

Gather experience on the AI workflow in the healthcare setting, from data acquisition to model development and testing.

Time frame: 0-36 month

Central Contacts

Lorenzo Piemonti, MD

CONTACT

+390226432706 piemonti.lorenzo@hsr.it

Data from ClinicalTrials.gov

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