A Retrospective Observational Study to Use Artificial Intelligence for Prediction of Disease TRajectorY in DIAbetes

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.